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Capato TTC, Chen J, Miranda JDA, Chien HF. Assisted technology in Parkinson's disease gait: what's up? ARQUIVOS DE NEURO-PSIQUIATRIA 2024; 82:1-10. [PMID: 38395424 PMCID: PMC10890908 DOI: 10.1055/s-0043-1777782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/21/2023] [Indexed: 02/25/2024]
Abstract
BACKGROUND Gait disturbances are prevalent and debilitating symptoms, diminishing mobility and quality of life for Parkinson's disease (PD) individuals. While traditional treatments offer partial relief, there is a growing interest in alternative interventions to address this challenge. Recently, a remarkable surge in assisted technology (AT) development was witnessed to aid individuals with PD. OBJECTIVE To explore the burgeoning landscape of AT interventions tailored to alleviate PD-related gait impairments and describe current research related to such aim. METHODS In this review, we searched on PubMed for papers published in English (2018-2023). Additionally, the abstract of each study was read to ensure inclusion. Four researchers searched independently, including studies according to our inclusion and exclusion criteria. RESULTS We included studies that met all inclusion criteria. We identified key trends in assistive technology of gait parameters analysis in PD. These encompass wearable sensors, gait analysis, real-time feedback and cueing techniques, virtual reality, and robotics. CONCLUSION This review provides a resource for guiding future research, informing clinical decisions, and fostering collaboration among researchers, clinicians, and policymakers. By delineating this rapidly evolving field's contours, it aims to inspire further innovation, ultimately improving the lives of PD patients through more effective and personalized interventions.
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Affiliation(s)
- Tamine T. C. Capato
- Universidade de São Paulo, Faculdade de Medicina, Departamento de Neurologia, Centro de Distúrbios do Movimento, São Paulo SP, Brazil.
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Nijmegen, The Netherlands.
| | - Janini Chen
- Universidade de São Paulo, Faculdade de Medicina FMUSP, Departamento de Ortopedia e Traumatologia, São Paulo, SP, Brazil.
| | - Johnny de Araújo Miranda
- Universidade de São Paulo, Faculdade de Medicina, Departamento de Neurologia, Centro de Distúrbios do Movimento, São Paulo SP, Brazil.
| | - Hsin Fen Chien
- Universidade de São Paulo, Faculdade de Medicina FMUSP, Departamento de Ortopedia e Traumatologia, São Paulo, SP, Brazil.
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Wu X, Ma L, Wei P, Shan Y, Chan P, Wang K, Zhao G. Wearable sensor devices can automatically identify the ON-OFF status of patients with Parkinson's disease through an interpretable machine learning model. Front Neurol 2024; 15:1387477. [PMID: 38751881 PMCID: PMC11094303 DOI: 10.3389/fneur.2024.1387477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 04/12/2024] [Indexed: 05/18/2024] Open
Abstract
Introduction Accurately and objectively quantifying the clinical features of Parkinson's disease (PD) is crucial for assisting in diagnosis and guiding the formulation of treatment plans. Therefore, based on the data on multi-site motor features, this study aimed to develop an interpretable machine learning (ML) model for classifying the "OFF" and "ON" status of patients with PD, as well as to explore the motor features that are most associated with changes in clinical symptoms. Methods We employed a support vector machine with a recursive feature elimination (SVM-RFE) algorithm to select promising motion features. Subsequently, 12 ML models were constructed based on these features, and we identified the model with the best classification performance. Then, we used the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model agnostic Explanations (LIME) methods to explain the model and rank the importance of those motor features. Results A total of 96 patients were finally included in this study. The naive Bayes (NB) model had the highest classification performance (AUC = 0.956; sensitivity = 0.8947, 95% CI 0.6686-0.9870; accuracy = 0.8421, 95% CI 0.6875-0.9398). Based on the NB model, we analyzed the importance of eight motor features toward the classification results using the SHAP algorithm. The Gait: range of motion (RoM) Shank left (L) (degrees) [Mean] might be the most important motor feature for all classification horizons. Conclusion The symptoms of PD could be objectively quantified. By utilizing suitable motor features to construct ML models, it became possible to intelligently identify whether patients with PD were in the "ON" or "OFF" status. The variations in these motor features were significantly correlated with improvement rates in patients' quality of life. In the future, they might act as objective digital biomarkers to elucidate the changes in symptoms observed in patients with PD and might be used to assist in the diagnosis and treatment of patients with PD.
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Affiliation(s)
- Xiaolong Wu
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Lin Ma
- Department of Neurorehabilitation, Rehabilitation Medicine of Capital Medical University, China Rehabilitation Research Centre, Beijing, China
| | - Penghu Wei
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Yongzhi Shan
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Piu Chan
- Department of Neurology and Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Kailiang Wang
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Guoguang Zhao
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
- Beijing Municipal Geriatric Medical Research Center, Beijing, China
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Zampogna A, Borzì L, Rinaldi D, Artusi CA, Imbalzano G, Patera M, Lopiano L, Pontieri F, Olmo G, Suppa A. Unveiling the Unpredictable in Parkinson's Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life. Bioengineering (Basel) 2024; 11:440. [PMID: 38790307 PMCID: PMC11117481 DOI: 10.3390/bioengineering11050440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 04/23/2024] [Accepted: 04/28/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Dyskinesias and freezing of gait are episodic disorders in Parkinson's disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes in spatiotemporal gait parameters. METHODS Seventy-one patients with Parkinson's disease (40 with dyskinesias and 33 with freezing of gait) were continuously monitored at home for a minimum of 5 days using a single wearable sensor. Dedicated machine-learning algorithms were used to categorize patients based on the occurrence of dyskinesias and freezing of gait. Additionally, specific spatiotemporal gait parameters were compared among patients with and without dyskinesias and/or freezing of gait. RESULTS The wearable sensor algorithms accurately classified patients with and without dyskinesias as well as those with and without freezing of gait based on the recorded dyskinesias and freezing of gait episodes. Standard spatiotemporal gait parameters did not differ significantly between patients with and without dyskinesias or freezing of gait. Both the time spent with dyskinesias and the number of freezing of gait episodes positively correlated with the disease severity and medication dosage. CONCLUSIONS A single inertial wearable sensor shows promise in monitoring complex, episodic movement patterns, such as dyskinesias and freezing of gait, during daily activities. This approach may help implement targeted therapeutic and preventive strategies for Parkinson's disease.
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Affiliation(s)
- Alessandro Zampogna
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy; (A.Z.); (M.P.)
- IRCCS Neuromed Institute, 86077 Pozzilli, IS, Italy
| | - Luigi Borzì
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy; (L.B.); (G.O.)
| | - Domiziana Rinaldi
- Department of Neuroscience, Mental Health and Sense Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy; (D.R.); (F.P.)
| | - Carlo Alberto Artusi
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, 10126 Torino, Italy; (C.A.A.); (G.I.); (L.L.)
- Neurology 2 Unit, A.O.U, Città della Salute e della Scienza di Torino, 10126 Torino, Italy
| | - Gabriele Imbalzano
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, 10126 Torino, Italy; (C.A.A.); (G.I.); (L.L.)
| | - Martina Patera
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy; (A.Z.); (M.P.)
| | - Leonardo Lopiano
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, 10126 Torino, Italy; (C.A.A.); (G.I.); (L.L.)
- Neurology 2 Unit, A.O.U, Città della Salute e della Scienza di Torino, 10126 Torino, Italy
| | - Francesco Pontieri
- Department of Neuroscience, Mental Health and Sense Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy; (D.R.); (F.P.)
| | - Gabriella Olmo
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy; (L.B.); (G.O.)
| | - Antonio Suppa
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy; (A.Z.); (M.P.)
- IRCCS Neuromed Institute, 86077 Pozzilli, IS, Italy
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Moorthy P, Weinert L, Schüttler C, Svensson L, Sedlmayr B, Müller J, Nagel T. Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review. JMIR Mhealth Uhealth 2024; 12:e52179. [PMID: 38578671 PMCID: PMC11031706 DOI: 10.2196/52179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/15/2023] [Accepted: 02/01/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND Wearable devices, mobile technologies, and their combination have been accepted into clinical use to better assess the physical fitness and quality of life of patients and as preventive measures. Usability is pivotal for overcoming constraints and gaining users' acceptance of technology such as wearables and their companion mobile health (mHealth) apps. However, owing to limitations in design and evaluation, interactive wearables and mHealth apps have often been restricted from their full potential. OBJECTIVE This study aims to identify studies that have incorporated wearable devices and determine their frequency of use in conjunction with mHealth apps or their combination. Specifically, this study aims to understand the attributes and evaluation techniques used to evaluate usability in the health care domain for these technologies and their combinations. METHODS We conducted an extensive search across 4 electronic databases, spanning the last 30 years up to December 2021. Studies including the keywords "wearable devices," "mobile apps," "mHealth apps," "physiological data," "usability," "user experience," and "user evaluation" were considered for inclusion. A team of 5 reviewers screened the collected publications and charted the features based on the research questions. Subsequently, we categorized these characteristics following existing usability and wearable taxonomies. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. RESULTS A total of 382 reports were identified from the search strategy, and 68 articles were included. Most of the studies (57/68, 84%) involved the simultaneous use of wearables and connected mobile apps. Wrist-worn commercial consumer devices such as wristbands were the most prevalent, accounting for 66% (45/68) of the wearables identified in our review. Approximately half of the data from the medical domain (32/68, 47%) focused on studies involving participants with chronic illnesses or disorders. Overall, 29 usability attributes were identified, and 5 attributes were frequently used for evaluation: satisfaction (34/68, 50%), ease of use (27/68, 40%), user experience (16/68, 24%), perceived usefulness (18/68, 26%), and effectiveness (15/68, 22%). Only 10% (7/68) of the studies used a user- or human-centered design paradigm for usability evaluation. CONCLUSIONS Our scoping review identified the types and categories of wearable devices and mHealth apps, their frequency of use in studies, and their implementation in the medical context. In addition, we examined the usability evaluation of these technologies: methods, attributes, and frameworks. Within the array of available wearables and mHealth apps, health care providers encounter the challenge of selecting devices and companion apps that are effective, user-friendly, and compatible with user interactions. The current gap in usability and user experience in health care research limits our understanding of the strengths and limitations of wearable technologies and their companion apps. Additional research is necessary to overcome these limitations.
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Affiliation(s)
- Preetha Moorthy
- Department of Biomedical Informatics, Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lina Weinert
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Section for Oral Health, Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg, Germany
| | - Christina Schüttler
- Medical Center for Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Laura Svensson
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Brita Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Julia Müller
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Till Nagel
- Human Data Interaction Lab, Mannheim University of Applied Sciences, Mannheim, Germany
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Salaorni F, Bonardi G, Schena F, Tinazzi M, Gandolfi M. Wearable devices for gait and posture monitoring via telemedicine in people with movement disorders and multiple sclerosis: a systematic review. Expert Rev Med Devices 2024; 21:121-140. [PMID: 38124300 DOI: 10.1080/17434440.2023.2298342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 12/19/2023] [Indexed: 12/23/2023]
Abstract
INTRODUCTION Wearable devices and telemedicine are increasingly used to track health-related parameters across patient populations. Since gait and postural control deficits contribute to mobility deficits in persons with movement disorders and multiple sclerosis, we thought it interesting to evaluate devices in telemedicine for gait and posture monitoring in such patients. METHODS For this systematic review, we searched the electronic databases MEDLINE (PubMed), SCOPUS, Cochrane Library, and SPORTDiscus. Of the 452 records retrieved, 12 met the inclusion/exclusion criteria. Data about (1) study characteristics and clinical aspects, (2) technical, and (3) telemonitoring and teleconsulting were retrieved, The studies were quality assessed. RESULTS All studies involved patients with Parkinson's disease; most used triaxial accelerometers for general assessment (n = 4), assessment of motor fluctuation (n = 3), falls (n = 2), and turning (n = 3). Sensor placement and count varied widely across studies. Nine used lab-validated algorithms for data analysis. Only one discussed synchronous patient feedback and asynchronous teleconsultation. CONCLUSIONS Wearable devices enable real-world patient monitoring and suggest biomarkers for symptoms and behaviors related to underlying gait disorders. thus enriching clinical assessment and personalized treatment plans. As digital healthcare evolves, further research is needed to enhance device accuracy, assess user acceptability, and integrate these tools into telemedicine infrastructure. PROSPERO REGISTRATION CRD42022355460.
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Affiliation(s)
- Francesca Salaorni
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Giulia Bonardi
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Federico Schena
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Michele Tinazzi
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Marialuisa Gandolfi
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Neuromotor and Cognitive Rehabilitation Research Centre (CRRNC), University of Verona, Verona, Italy
- Neurorehabilitation Unit - Azienda Ospedaliera Universitaria Integrata, Verona
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Martino Cinnera A, Picerno P, Bisirri A, Koch G, Morone G, Vannozzi G. Upper limb assessment with inertial measurement units according to the international classification of functioning in stroke: a systematic review and correlation meta-analysis. Top Stroke Rehabil 2024; 31:66-85. [PMID: 37083139 DOI: 10.1080/10749357.2023.2197278] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 03/24/2023] [Indexed: 04/22/2023]
Abstract
OBJECTIVE To investigate the usefulness of inertial measurement units (IMUs) in the assessment of motor function of the upper limb (UL) in accordance with the international classification of functioning (ICF). DATA SOURCES PubMed; Scopus; Embase; WoS and PEDro databases were searched from inception to 1 February 2022. METHODS The current systematic review follows PRISMA recommendations. Articles including IMU assessment of UL in stroke individuals have been included and divided into four ICF categories (b710, b735, b760, d445). We used correlation meta-analysis to pool the Fisher Z-score of each correlation between kinematics and clinical assessment. RESULTS A total of 35 articles, involving 475 patients, met the inclusion criteria. In the included studies, IMUs have been employed to assess the mobility of joint functions (n = 6), muscle tone functions (n = 4), control of voluntary movement functions (n = 15), and hand and arm use (n = 15). A significant correlation was found in overall meta-analysis based on 10 studies, involving 213 subjects: (r = 0.69) (95% CI: 0.69/0.98; p < 0.001) as in the d445 (r = 0.71) and b760 (r = 0.64) ICF domains, with no heterogeneity across the studies. CONCLUSION The literature supports the integration of IMUs and conventional clinical assessment in functional evaluation of the UL after a stroke. The use of a limited number of wearable sensors can provide additional kinematic features of UL in all investigated ICF domains, especially in the ADL tasks when a strong correlation with clinical evaluation was found.
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Affiliation(s)
- Alex Martino Cinnera
- Scientific Institute for Research, Hospitalization and Health Care IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
| | - Pietro Picerno
- SMART Engineering Solutions & Technologies (SMARTEST) Research Center, Università Telematica "eCampus", Novedrate, Italy
| | | | - Giacomo Koch
- Department of Neuroscience and Rehabilitation, University of Ferrara, Italy
| | - Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Giuseppe Vannozzi
- Scientific Institute for Research, Hospitalization and Health Care IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
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Weizman Y, Tan AM, Fuss FK. The Use of Wearable Devices to Measure Sedentary Behavior during COVID-19: Systematic Review and Future Recommendations. SENSORS (BASEL, SWITZERLAND) 2023; 23:9449. [PMID: 38067820 PMCID: PMC10708690 DOI: 10.3390/s23239449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/17/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023]
Abstract
The SARS-CoV-2 pandemic resulted in approximately 7 million deaths and impacted 767 million individuals globally, primarily through infections. Acknowledging the impactful influence of sedentary behaviors, particularly exacerbated by COVID-19 restrictions, a substantial body of research has emerged, utilizing wearable sensor technologies to assess these behaviors. This comprehensive review aims to establish a framework encompassing recent studies concerning wearable sensor applications to measure sedentary behavior parameters during the COVID-19 pandemic, spanning December 2019 to December 2022. After examining 582 articles, 7 were selected for inclusion. While most studies displayed effective reporting standards and adept use of wearable device data for their specific research aims, our inquiry revealed deficiencies in apparatus accuracy documentation and study methodology harmonization. Despite methodological variations, diverse metrics, and the absence of thorough device accuracy assessments, integrating wearables within the pandemic context offers a promising avenue for objective measurements and strategies against sedentary behaviors.
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Affiliation(s)
- Yehuda Weizman
- Chair of Biomechanics, Faculty of Engineering Science, University of Bayreuth, D-95447 Bayreuth, Germany;
- Department of Health and Medical Sciences, School of Health Sciences, Hawthorn Campus, Swinburne University of Technology, Melbourne 3122, Australia;
| | - Adin Ming Tan
- Department of Health and Medical Sciences, School of Health Sciences, Hawthorn Campus, Swinburne University of Technology, Melbourne 3122, Australia;
| | - Franz Konstantin Fuss
- Chair of Biomechanics, Faculty of Engineering Science, University of Bayreuth, D-95447 Bayreuth, Germany;
- Division of Biomechatronics, Fraunhofer Institute for Manufacturing Engineering and Automation IPA, D-95447 Bayreuth, Germany
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Bao T, Gao J, Wang J, Chen Y, Xu F, Qiao G, Li F. A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022. Front Robot AI 2023; 10:1265543. [PMID: 38047061 PMCID: PMC10691112 DOI: 10.3389/frobt.2023.1265543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/06/2023] [Indexed: 12/05/2023] Open
Abstract
Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person's gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992-2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.
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Affiliation(s)
- Tong Bao
- School of Medicine, Tsinghua University, Beijing, China
- Institute for Precision Medicine, Tsinghua University, Beijing, China
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Jiasi Gao
- Institute for AI Industry Research, Tsinghua University, Beijing, China
| | - Jinyi Wang
- School of Medicine, Tsinghua University, Beijing, China
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Yang Chen
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Feng Xu
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Guanzhong Qiao
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
| | - Fei Li
- Institute for Precision Medicine, Tsinghua University, Beijing, China
- Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China
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Chen M, Sun Z, Xin T, Chen Y, Su F. An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3937-3946. [PMID: 37695969 DOI: 10.1109/tnsre.2023.3314100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Walking detection in the daily life of patients with Parkinson's disease (PD) is of great significance for tracking the progress of the disease. This study aims to implement an accurate, objective, and passive detection algorithm optimized based on an interpretable deep learning architecture for the daily walking of patients with PD and to explore the most representative spatiotemporal motor features. Five inertial measurement units attached to the wrist, ankle, and waist are used to collect motion data from 100 subjects during a 10-meter walking test. The raw data of each sensor are subjected to the continuous wavelet transform to train the classification model of the constructed 6-channel convolutional neural network (CNN). The results show that the sensor located at the waist has the best classification performance with an accuracy of 98.01%±0.85% and the area under the receiver operating characteristic curve (AUC) of 0.9981±0.0017 under ten-fold cross-validation. The gradient-weighted class activation mapping shows that the feature points with greater contribution to PD were concentrated in the lower frequency band (0.5~3Hz) compared with healthy controls. The visual maps of the 3D CNN show that only three out of the six time series have a greater contribution, which is used as a basis to further optimize the model input, greatly reducing the raw data processing costs (50%) while ensuring its performance (AUC=0.9929±0.0019). To the best of our knowledge, this is the first study to consider the visual interpretation-based optimization of an intelligent classification model in the intelligent diagnosis of PD.
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Wei S, Wu Z. The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7667. [PMID: 37765724 PMCID: PMC10537628 DOI: 10.3390/s23187667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023]
Abstract
The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.
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Affiliation(s)
- Suyao Wei
- College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China
| | - Zhihui Wu
- College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China
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Schalkamp AK, Peall KJ, Harrison NA, Sandor C. Wearable movement-tracking data identify Parkinson's disease years before clinical diagnosis. Nat Med 2023; 29:2048-2056. [PMID: 37400639 DOI: 10.1038/s41591-023-02440-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/05/2023] [Indexed: 07/05/2023]
Abstract
Parkinson's disease is a progressive neurodegenerative movement disorder with a long latent phase and currently no disease-modifying treatments. Reliable predictive biomarkers that could transform efforts to develop neuroprotective treatments remain to be identified. Using UK Biobank, we investigated the predictive value of accelerometry in identifying prodromal Parkinson's disease in the general population and compared this digital biomarker with models based on genetics, lifestyle, blood biochemistry or prodromal symptoms data. Machine learning models trained using accelerometry data achieved better test performance in distinguishing both clinically diagnosed Parkinson's disease (n = 153) (area under precision recall curve (AUPRC) 0.14 ± 0.04) and prodromal Parkinson's disease (n = 113) up to 7 years pre-diagnosis (AUPRC 0.07 ± 0.03) from the general population (n = 33,009) compared with all other modalities tested (genetics: AUPRC = 0.01 ± 0.00, P = 2.2 × 10-3; lifestyle: AUPRC = 0.03 ± 0.04, P = 2.5 × 10-3; blood biochemistry: AUPRC = 0.01 ± 0.00, P = 4.1 × 10-3; prodromal signs: AUPRC = 0.01 ± 0.00, P = 3.6 × 10-3). Accelerometry is a potentially important, low-cost screening tool for determining people at risk of developing Parkinson's disease and identifying participants for clinical trials of neuroprotective treatments.
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Affiliation(s)
- Ann-Kathrin Schalkamp
- Division of Psychological Medicine and Clinical Neuroscience, UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Kathryn J Peall
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, UK
| | - Neil A Harrison
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, UK
| | - Cynthia Sandor
- Division of Psychological Medicine and Clinical Neuroscience, UK Dementia Research Institute, Cardiff University, Cardiff, UK.
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12
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Seifer AK, Dorschky E, Küderle A, Moradi H, Hannemann R, Eskofier BM. EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:6565. [PMID: 37514858 PMCID: PMC10383770 DOI: 10.3390/s23146565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023]
Abstract
Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user's daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid's integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 ± 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring.
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Affiliation(s)
- Ann-Kristin Seifer
- Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Eva Dorschky
- Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Arne Küderle
- Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | - Hamid Moradi
- Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
| | | | - Björn M Eskofier
- Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
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13
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Castiglia SF, Trabassi D, Conte C, Ranavolo A, Coppola G, Sebastianelli G, Abagnale C, Barone F, Bighiani F, De Icco R, Tassorelli C, Serrao M. Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:4983. [PMID: 37430896 DOI: 10.3390/s23104983] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/14/2023] [Accepted: 05/16/2023] [Indexed: 07/12/2023]
Abstract
The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1-6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at τ4 and τ5, and MSE in the ML direction at τ4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD.
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Affiliation(s)
- Stefano Filippo Castiglia
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
- Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, 00078 Monte Porzio Catone, Italy
| | - Dante Trabassi
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Carmela Conte
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Alberto Ranavolo
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
| | - Gianluca Coppola
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Gabriele Sebastianelli
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Chiara Abagnale
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Francesca Barone
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
| | - Federico Bighiani
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
- Movement Analysis Research Unit, IRCSS Mondino Foundation, 27100 Pavia, Italy
| | - Roberto De Icco
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
- Movement Analysis Research Unit, IRCSS Mondino Foundation, 27100 Pavia, Italy
| | - Cristina Tassorelli
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
- Movement Analysis Research Unit, IRCSS Mondino Foundation, 27100 Pavia, Italy
| | - Mariano Serrao
- Department of Medical and Surgical Sciences and Biotechnologies, "Sapienza" University of Rome, Polo Pontino, 04100 Latina, Italy
- Movement Analysis Laboratory, Policlinico Italia, 00162 Rome, Italy
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14
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Giuliano C, Cerri S, Cesaroni V, Blandini F. Relevance of Biochemical Deep Phenotyping for a Personalised Approach to Parkinson's Disease. Neuroscience 2023; 511:100-109. [PMID: 36572171 DOI: 10.1016/j.neuroscience.2022.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 10/05/2022] [Accepted: 12/19/2022] [Indexed: 12/25/2022]
Abstract
Parkinson's disease (PD) is a multifactorial neurodegenerative disorder characterised by the progressive loss of dopaminergic neurons in the nigrostriatal tract. The identification of disease-modifying therapies is the Holy Grail of PD research, but to date no drug has been approved as such a therapy. A possible reason is the remarkable phenotypic heterogeneity of PD patients, which can generate confusion in the interpretation of results or even mask the efficacy of a therapeutic intervention. This heterogeneity should be taken into account in clinical trials, stratifying patients by their expected response to drugs designed to engage selected molecular targets. In this setting, stratification methods (clinical and genetic) should be supported by biochemical phenotyping of PD patients, in line with the deep phenotyping concept. Collection, from single patients, of a range of biological samples would streamline the generation of these profiles. Several studies have proposed biochemical characterisations of patient cohorts based on analysis of blood, cerebrospinal fluid, urine, stool, saliva and skin biopsy samples, with extracellular vesicles attracting increasing interest as a source of biomarkers. In this review we report and critically discuss major studies that used a biochemical approach to stratify their PD cohorts. The analyte most studied is α-synuclein, while other studies have focused on neurofilament light chain, lysosomal proteins, inflammasome-related proteins, LRRK2 and the urinary proteome. At present, stratification of PD patients, while promising, is still a nascent approach. Deep phenotyping of patients will allow clinical researchers to identify homogeneous subgroups for the investigation of tailored disease-modifying therapies, enhancing the chances of therapeutic success.
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Affiliation(s)
- Claudio Giuliano
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Silvia Cerri
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Valentina Cesaroni
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Fabio Blandini
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy.
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15
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Cui Z. ANALYSIS OF THE ARTICULAR LOAD ON THE LOWER LIMBS DURING BACKFLIP. REV BRAS MED ESPORTE 2023. [DOI: 10.1590/1517-8692202329012022_0464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
ABSTRACT Introduction: Sports injuries restrict the acquisition of optimal results and represent a great threat to the athlete's physical health, lower limb injuries being the most prominent, mainly those caused by landing. Objective: Use biomechanics to analyze the joint load of the lower limbs during the landing process in the gymnastics backflip, establishing movement control and reducing the risk of lower limb injury. Methods: The male athletes of the National Gymnastics Team were selected as the research objects, and the three-dimensional backflip (BS) motion trajectory was completed after the landing process was completed, the vertical ground reaction force (VGRF) after landing and the lower limb muscle electromyography (EMG) after landing was collected, and the human multi-body system model and the landing platform model of the landing action were completed with the help of the system simulation software. Results: Statistics show that gymnasts train more intensively during competition or daily training, performing more than 200 landings per week, a factor that increases the risk of injuries during the backflip in athletes. Conclusion: The lower limb joint load of the landing action in gymnastics backflip is high, which will cause a certain risk of injury, and specific measures can be taken to control it. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.
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16
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Shida TKF, Costa TM, de Oliveira CEN, de Castro Treza R, Hondo SM, Los Angeles E, Bernardo C, Dos Santos de Oliveira L, de Jesus Carvalho M, Coelho DB. A public data set of walking full-body kinematics and kinetics in individuals with Parkinson's disease. Front Neurosci 2023; 17:992585. [PMID: 36875659 PMCID: PMC9978741 DOI: 10.3389/fnins.2023.992585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Abstract
Background To our knowledge, there is no Parkinson's disease (PD) gait biomechanics data sets available to the public. Objective This study aimed to create a public data set of 26 idiopathic individuals with PD who walked overground on ON and OFF medication. Materials and methods Their upper extremity, trunk, lower extremity, and pelvis kinematics were measured using a three-dimensional motion-capture system (Raptor-4; Motion Analysis). The external forces were collected using force plates. The results include raw and processed kinematic and kinetic data in c3d and ASCII files in different file formats. In addition, a metadata file containing demographic, anthropometric, and clinical data is provided. The following clinical scales were employed: Unified Parkinson's disease rating scale motor aspects of experiences of daily living and motor score, Hoehn & Yahr, New Freezing of Gait Questionnaire, Montreal Cognitive Assessment, Mini Balance Evaluation Systems Tests, Fall Efficacy Scale-International-FES-I, Stroop test, and Trail Making Test A and B. Results All data are available at Figshare (https://figshare.com/articles/dataset/A_dataset_of_overground_walking_full-body_kinematics_and_kinetics_in_individuals_with_Parkinson_s_disease/14896881). Conclusion This is the first public data set containing a three-dimensional full-body gait analysis of individuals with PD under the ON and OFF medication. It is expected to contribute so that different research groups worldwide have access to reference data and a better understanding of the effects of medication on gait.
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Affiliation(s)
| | - Thaisy Moraes Costa
- Biomedical Engineering, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Claudia Eunice Neves de Oliveira
- Biomedical Engineering, Federal University of ABC, São Bernardo do Campo, Brazil.,Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Renata de Castro Treza
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Sandy Mikie Hondo
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Emanuele Los Angeles
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Claudionor Bernardo
- Biomedical Engineering, Federal University of ABC, São Bernardo do Campo, Brazil
| | | | | | - Daniel Boari Coelho
- Biomedical Engineering, Federal University of ABC, São Bernardo do Campo, Brazil.,Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
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17
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Prieto-Avalos G, Sánchez-Morales LN, Alor-Hernández G, Sánchez-Cervantes JL. A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases. BIOSENSORS 2022; 13:72. [PMID: 36671907 PMCID: PMC9856141 DOI: 10.3390/bios13010072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.
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Affiliation(s)
- Guillermo Prieto-Avalos
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Laura Nely Sánchez-Morales
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
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18
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Chatzaki C, Skaramagkas V, Kefalopoulou Z, Tachos N, Kostikis N, Kanellos F, Triantafyllou E, Chroni E, Fotiadis DI, Tsiknakis M. Can Gait Features Help in Differentiating Parkinson's Disease Medication States and Severity Levels? A Machine Learning Approach. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249937. [PMID: 36560313 PMCID: PMC9787905 DOI: 10.3390/s22249937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 05/14/2023]
Abstract
Parkinson's disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively.
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Affiliation(s)
- Chariklia Chatzaki
- Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece
- Correspondence:
| | - Vasileios Skaramagkas
- Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece
| | | | - Nikolaos Tachos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
- Biomedical Research Institute, Foundation for Research and Technology—Hellas, 45110 Ioannina, Greece
| | | | | | | | - Elisabeth Chroni
- Department of Neurology, Patras University Hospital, 26404 Patra, Greece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece
- Biomedical Research Institute, Foundation for Research and Technology—Hellas, 45110 Ioannina, Greece
| | - Manolis Tsiknakis
- Biomedical Informatics and eHealth Laboratory, Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Estavromenos, 71410 Heraklion, Crete, Greece
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, Vassilika Vouton, 71110 Heraklion, Crete, Greece
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19
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Wu J, Maurenbrecher H, Schaer A, Becsek B, Awai Easthope C, Chatzipirpiridis G, Ergeneman O, Pané S, Nelson BJ. Human gait-labeling uncertainty and a hybrid model for gait segmentation. Front Neurosci 2022; 16:976594. [PMID: 36570841 PMCID: PMC9773262 DOI: 10.3389/fnins.2022.976594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 11/18/2022] [Indexed: 12/13/2022] Open
Abstract
Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems. To date, their reliability and limitations in manual labeling of gait events have not been studied. Objectives Evaluate manual labeling uncertainty and introduce a hybrid stride detection and gait-event estimation model for autonomous, long-term, and remote monitoring. Methods Estimate inter-labeler inconsistencies by computing the limits-of-agreement. Develop a hybrid model based on dynamic time warping and convolutional neural network to identify valid strides and eliminate non-stride data in inertial (walking) data collected by a wearable device. Finally, detect gait events within a valid stride region. Results The limits of inter-labeler agreement for key gait events heel off, toe off, heel strike, and flat foot are 72, 16, 24, and 80 ms, respectively; The hybrid model's classification accuracy for stride and non-stride are 95.16 and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24, 5, 9, and 13 ms, respectively, when compared to the average human labels. Conclusions The results show the inherent labeling uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers, and it is a valid model to reliably detect strides and estimate the gait events in human gait data. Significance This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.
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Affiliation(s)
- Jiaen Wu
- Multi-Scale Robotics Lab, ETH Zurich, Zurich, Switzerland,Magnes AG, Zurich, Switzerland,*Correspondence: Jiaen Wu
| | | | | | | | - Chris Awai Easthope
- Cereneo Foundation, Center for Interdisciplinary Research (CEFIR), Vitznau, Switzerland
| | | | | | - Salvador Pané
- Multi-Scale Robotics Lab, ETH Zurich, Zurich, Switzerland
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20
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Ngueleu AM, Barthod C, Best KL, Routhier F, Otis M, Batcho CS. Criterion validity of ActiGraph monitoring devices for step counting and distance measurement in adults and older adults: a systematic review. J Neuroeng Rehabil 2022; 19:112. [PMID: 36253787 PMCID: PMC9575229 DOI: 10.1186/s12984-022-01085-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 09/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Wearable activity monitors such as ActiGraph monitoring devices are widely used, especially in research settings. Various research studies have assessed the criterion validity of ActiGraph devices for step counting and distance estimation in adults and older adults. Although several studies have used the ActiGraph devices as a reference system for activity monitoring, there is no summarized evidence of the psychometric properties. The main objective of this systematic review was to summarize evidence related to the criterion validity of ActiGraph monitoring devices for step counting and distance estimation in adults and/or older adults. METHODS Literature searches were conducted in six databases (Medline (OVID), Embase, IEEExplore, CINAHL, Engineering Village and Web of Science). Two reviewers independently conducted selection, a quality analysis of articles (using COSMIN and MacDermid's grids) and data extraction. RESULTS This review included 21 studies involving 637 participants (age 30.3 ± 7.5 years (for adults) and 82.7 ± 3.3 years (for older adults)). Five ActiGraph devices (7164, GT1M, wGTX +, GT3X +/wGT3X + and wGT3X - BT) were used to collect data at the hip, wrist and ankle to assess various walking and running speeds (ranging from 0.2 m/s to 4.44 m/s) over durations of 2 min to 3 days (13 h 30 mins per day) for step counting and distance estimation. The ActiGraph GT3X +/wGT3X + and wGT3X - BT had better criterion validity than the ActiGraph 7164, wGTX + and GT1M according to walking and running speeds for step counting. Validity of ActiGraph wGT3X + was good for distance estimation. CONCLUSION The ActiGraph wGT3X - BT and GT3X +/wGT3X + have good criterion validity for step counting, under certain conditions related to walking speeds, positioning and data processing.
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Affiliation(s)
- Armelle-Myriane Ngueleu
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, Québec City, Québec, Canada.,Department of Rehabilitation, Faculty of Medicine, Université Laval, Québec City, Québec, Canada
| | - Corentin Barthod
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, Québec City, Québec, Canada.,Department of Rehabilitation, Faculty of Medicine, Université Laval, Québec City, Québec, Canada
| | - Krista Lynn Best
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, Québec City, Québec, Canada.,Department of Rehabilitation, Faculty of Medicine, Université Laval, Québec City, Québec, Canada
| | - François Routhier
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, Québec City, Québec, Canada.,Department of Rehabilitation, Faculty of Medicine, Université Laval, Québec City, Québec, Canada
| | - Martin Otis
- Automation and Interactive Robotic Laboratory (AIRL), Department of Applied Science, Université de Quebec À Chicoutimi, 555 Blvd of University, Chicoutimi, Québec, Canada
| | - Charles Sèbiyo Batcho
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, Québec City, Québec, Canada. .,Department of Rehabilitation, Faculty of Medicine, Université Laval, Québec City, Québec, Canada.
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21
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Chen M, Sun Z, Su F, Chen Y, Bu D, Lyu Y. An Auxiliary Diagnostic System for Parkinson's Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2254-2263. [PMID: 35947560 DOI: 10.1109/tnsre.2022.3197807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized mainly by motor-related impairment, an accurate, quantitative, and objective diagnosis is an effective way to slow the disease deterioration process. In this paper, a user-friendly auxiliary diagnostic system for PD is constructed based on the upper limb movement conditions of 100 subjects consisting of 50 PD patients and 50 healthy subjects. This system includes wearable sensors that collect upper limb movement data, host computer for data processing and classification, and graphic user interface (GUI). The genetic algorithm optimized random forest classifier is introduced to classify PD and normal states based on the selected optimal features, and the 50 trials leave-one-out cross-validation is used to evaluate the performance of the classifier, with the highest accuracy of 94.4%. The classification accuracy among different upper limb movement tasks and with the different number of sensors are compared, results show that the task with only alternation hand movement also has satisfactory classification accuracy, and sensors on both wrists performance better than one sensor on a single wrist. The utility of the proposed system is illustrated by neurologists with a deployed GUI during the clinical inquiry, opening the possibility for a wide range of applications in the auxiliary diagnosis of PD.
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22
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Li W, Chen X, Zhang J, Lu J, Zhang C, Bai H, Liang J, Wang J, Du H, Xue G, Ling Y, Ren K, Zou W, Chen C, Li M, Chen Z, Zou H. Recognition of Freezing of Gait in Parkinson’s Disease Based on Machine Vision. Front Aging Neurosci 2022; 14:921081. [PMID: 35912091 PMCID: PMC9329960 DOI: 10.3389/fnagi.2022.921081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundFreezing of gait (FOG) is a common clinical manifestation of Parkinson’s disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment.ObjectiveA method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital.MethodsIn this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model.ResultsWe adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%.ConclusionA method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.
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Affiliation(s)
- Wendan Li
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | | | - Jintao Zhang
- Department of Neurology, The 960th Hospital of PLA, Taian, China
| | - Jianjun Lu
- Department of Neurosurgery, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Chencheng Zhang
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongmin Bai
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Junchao Liang
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Jiajia Wang
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Hanqiang Du
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Gaici Xue
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Yun Ling
- GYENNO SCIENCE Co., LTD., Shenzhen, China
| | - Kang Ren
- GYENNO SCIENCE Co., LTD., Shenzhen, China
| | | | - Cheng Chen
- GYENNO SCIENCE Co., LTD., Shenzhen, China
| | - Mengyan Li
- Department of Neurology, Guangzhou First People’s Hospital, Guangzhou, China
- *Correspondence: Mengyan Li,
| | - Zhonglue Chen
- GYENNO SCIENCE Co., LTD., Shenzhen, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
- Zhonglue Chen,
| | - Haiqiang Zou
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
- Branch of National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Guangzhou, China
- Haiqiang Zou,
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23
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Hecker P, Steckhan N, Eyben F, Schuller BW, Arnrich B. Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends. Front Digit Health 2022; 4:842301. [PMID: 35899034 PMCID: PMC9309252 DOI: 10.3389/fdgth.2022.842301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/25/2022] [Indexed: 11/25/2022] Open
Abstract
Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.
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Affiliation(s)
- Pascal Hecker
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- audEERING GmbH, Gilching, Germany
- *Correspondence: Pascal Hecker ; orcid.org/0000-0001-6604-1671
| | - Nico Steckhan
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | | | - Björn W. Schuller
- audEERING GmbH, Gilching, Germany
- EIHW – Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- GLAM – Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
| | - Bert Arnrich
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
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Chavez JM, Tang W. A Vision-Based System for Stage Classification of Parkinsonian Gait Using Machine Learning and Synthetic Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:4463. [PMID: 35746246 PMCID: PMC9229496 DOI: 10.3390/s22124463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 06/15/2023]
Abstract
Parkinson's disease is characterized by abnormal gait, which worsens as the condition progresses. Although several methods have been able to classify this feature through pose-estimation algorithms and machine-learning classifiers, few studies have been able to analyze its progression to perform stage classification of the disease. Moreover, despite the increasing popularity of these systems for gait analysis, the amount of available gait-related data can often be limited, thereby, hindering the progress of the implementation of this technology in the medical field. As such, creating a quantitative prognosis method that can identify the severity levels of a Parkinsonian gait with little data could help facilitate the study of the Parkinsonian gait for rehabilitation. In this contribution, we propose a vision-based system to analyze the Parkinsonian gait at various stages using linear interpolation of Parkinsonian gait models. We present a comparison between the performance of a k-nearest neighbors algorithm (KNN), support-vector machine (SVM) and gradient boosting (GB) algorithms in classifying well-established gait features. Our results show that the proposed system achieved 96-99% accuracy in evaluating the prognosis of Parkinsonian gaits.
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Affiliation(s)
| | - Wei Tang
- Klipsch School of Electrical Engineering, New Mexico State University, Las Cruces, NM 88003, USA
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Rodríguez-Martín D, Cabestany J, Pérez-López C, Pie M, Calvet J, Samà A, Capra C, Català A, Rodríguez-Molinero A. A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ON TM. Front Neurol 2022; 13:912343. [PMID: 35720090 PMCID: PMC9202426 DOI: 10.3389/fneur.2022.912343] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.
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Affiliation(s)
| | - Joan Cabestany
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Carlos Pérez-López
- Department of Investigation, Consorci Sanitari Alt Penedès - Garraf, Vilanova i la Geltrú, Spain
| | - Marti Pie
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Joan Calvet
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Albert Samà
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | | | - Andreu Català
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
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Schalkamp AK, Rahman N, Monzón-Sandoval J, Sandor C. Deep phenotyping for precision medicine in Parkinson's disease. Dis Model Mech 2022; 15:dmm049376. [PMID: 35647913 PMCID: PMC9178512 DOI: 10.1242/dmm.049376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.
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Affiliation(s)
| | | | | | - Cynthia Sandor
- UK Dementia Research Institute at Cardiff University,Division of Psychological Medicine and Clinical Neuroscience, Haydn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK
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Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis. SENSORS 2022; 22:s22103700. [PMID: 35632109 PMCID: PMC9148133 DOI: 10.3390/s22103700] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/03/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023]
Abstract
The aim of this study was to determine which supervised machine learning (ML) algorithm can most accurately classify people with Parkinson’s disease (pwPD) from speed-matched healthy subjects (HS) based on a selected minimum set of IMU-derived gait features. Twenty-two gait features were extrapolated from the trunk acceleration patterns of 81 pwPD and 80 HS, including spatiotemporal, pelvic kinematics, and acceleration-derived gait stability indexes. After a three-level feature selection procedure, seven gait features were considered for implementing five ML algorithms: support vector machine (SVM), artificial neural network, decision trees (DT), random forest (RF), and K-nearest neighbors. Accuracy, precision, recall, and F1 score were calculated. SVM, DT, and RF showed the best classification performances, with prediction accuracy higher than 80% on the test set. The conceptual model of approaching ML that we proposed could reduce the risk of overrepresenting multicollinear gait features in the model, reducing the risk of overfitting in the test performances while fostering the explainability of the results.
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28
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Events Detection of Anticipatory Postural Adjustments through a Wearable Accelerometer Sensor Is Comparable to That Measured by the Force Platform in Subjects with Parkinson's Disease. SENSORS 2022; 22:s22072668. [PMID: 35408282 PMCID: PMC9003325 DOI: 10.3390/s22072668] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 03/26/2022] [Accepted: 03/28/2022] [Indexed: 02/06/2023]
Abstract
Out-of-the-lab instrumented gait testing focuses on steady-state gait and usually does not include gait initiation (GI) measures. GI involves Anticipatory Postural Adjustments (APAs), which propel the center of mass (COM) forward and laterally before the first step. These movements are impaired in persons with Parkinson’s disease (PD), contributing to their pathological gait. The use of a simple GI testing system, outside the lab, would allow improving gait rehabilitation of PD patients. Here, we evaluated the metrological quality of using a single inertial measurement unit for APA detection as compared with the use of a gold-standard system, i.e., the force platforms. Twenty-five PD and eight elderly subjects (ELD) were asked to initiate gait in response to auditory stimuli while wearing an IMU on the trunk. Temporal parameters (APA-Onset, Time-to-Toe-Off, Time-to-Heel-Strike, APA-Duration, Swing-Duration) extracted from the accelerometric data and force platforms were significantly correlated (mean(SD), r: 0.99(0.01), slope: 0.97(0.02)) showing a good level of agreement (LOA [s]: 0.04(0.01), CV [%]: 2.9(1.7)). PD showed longer APA-Duration compared to ELD ([s] 0.81(0.17) vs. 0.59(0.09) p < 0.01). APA parameters showed moderate correlation with the MDS-UPDRS Rigidity, Characterizing-FOG questionnaire and FAB-2 planning. The single IMU-based reconstruction algorithm was effective in measuring APAs timings in PD. The current work sets the stage for future developments of tele-rehabilitation and home-based exercises.
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29
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Walha R, Gaudreault N, Dagenais P, Boissy P. Spatiotemporal parameters and gait variability in people with psoriatic arthritis (PsA): a cross-sectional study. J Foot Ankle Res 2022; 15:19. [PMID: 35246222 PMCID: PMC8895502 DOI: 10.1186/s13047-022-00521-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 02/10/2022] [Indexed: 11/21/2022] Open
Abstract
Background Foot involvement is a major manifestation of psoriatic arthritis (PsA) and can lead to severe levels of foot pain and disability and impaired functional mobility and quality of life. Gait spatiotemporal parameters (STPs) and gait variability, used as a clinical index of gait stability, have been associated with several adverse health outcomes, including risk of falling, functional decline, and mortality in a wide range of populations. Previous studies showed some alterations in STPs in people with PsA. However, gait variability and the relationships between STPs, gait variability and self-reported foot pain and disability have never been studied in these populations. Body-worn inertial measurement units (IMUs) are gaining interest in measuring gait parameters in clinical settings. Objectives To assess STPs and gait variability in people with PsA using IMUs, to explore their relationship with self-reported foot pain and function and to investigate the feasibility of using IMUs to discriminate patient groups based on gait speed-critical values. Methods Twenty-one participants with PsA (age: 53.9 ± 8.9 yrs.; median disease duration: 6 yrs) and 21 age- and sex-matched healthy participants (age 54.23 ± 9.3 yrs) were recruited. All the participants performed three 10-m walk test trials at their comfortable speed. STPs and gait variability were recorded and calculated using six body-worn IMUs and Mobility Lab software (APDM®). Foot pain and disability were assessed in participants with PsA using the foot function index (FFI). Results Cadence, gait speed, stride length, and swing phase were significantly lower, while double support was significantly higher, in the PsA group (p < 0.006). Strong correlations between STPs and the FFI total score were demonstrated (|r| > 0.57, p < 0.006). Gait variability was significantly increased in the PsA group, but it was not correlated with foot pain or function (p < 0.006). Using the IMUs, three subgroups of participants with PsA with clinically meaningful differences in self-reported foot pain and disability were discriminated. Conclusion STPs were significantly altered in participants with PsA, which could be associated with self-reported foot pain and disability. Future studies are required to confirm the increased gait variability highlighted in this study and its potential underlying causes. Using IMUs has been useful to objectively assess foot function in people with PsA. Trial registration ClinicalTrials.gov, NCT05075343, Retrospectively registered on 29 September 2021.
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Affiliation(s)
- Roua Walha
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Nathaly Gaudreault
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Pierre Dagenais
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Patrick Boissy
- Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada. .,Research Center on Aging, CIUSSS Estrie CHUS, Sherbrooke, QC, Canada.
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Pau M, Mulas I, Putzu V, Asoni G, Viale D, Mameli I, Allali G. Functional mobility in older women with and without motoric cognitive risk syndrome: a quantitative assessment using wearable inertial sensors. JOURNAL OF GERONTOLOGY AND GERIATRICS 2022. [DOI: 10.36150/2499-6564-n259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Weizman Y, Tirosh O, Fuss FK, Tan AM, Rutz E. Recent State of Wearable IMU Sensors Use in People Living with Spasticity: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22051791. [PMID: 35270937 PMCID: PMC8914967 DOI: 10.3390/s22051791] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/17/2022] [Accepted: 02/22/2022] [Indexed: 05/02/2023]
Abstract
Spasticity is a disabling characteristic of neurological disorders, described by a velocity-dependent increase in muscle tone during passive stretch. During the last few years, many studies have been carried out to assess spasticity using wearable IMU (inertial measurements unit) sensors. This review aims to provide an updated framework of the current research on IMUs wearable sensors in people living with spasticity in recent studies published between 2017 and 2021. A total of 322 articles were screened, then finally 10 articles were selected. Results show the lack of homogenization of study procedures and missing apparatus information in some studies. Still, most studies performed adequately on measures of reporting and found that IMUs wearable data was successful in their respective purposes and goals. As IMUs estimate translational and rotational body motions, we believe there is a strong potential for these applications to estimate velocity-dependent exaggeration of stretch reflexes and spasticity-related characteristics in spasticity. This review also proposes new directions of research that should be challenged by larger study groups and could be of interest to both researchers as well as clinicians. The use of IMUs to evaluate spasticity is a promising avenue to provide an objective measurement as compared to non-instrumented traditional assessments.
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Affiliation(s)
- Yehuda Weizman
- Department of Health and Medical Sciences, School of Health Sciences, Hawthorn Campus, Swinburne University of Technology, Melbourne 3122, Australia; (O.T.); (A.M.T.)
- Correspondence: ; Tel.: +61-3921-45320
| | - Oren Tirosh
- Department of Health and Medical Sciences, School of Health Sciences, Hawthorn Campus, Swinburne University of Technology, Melbourne 3122, Australia; (O.T.); (A.M.T.)
| | - Franz Konstantin Fuss
- Chair of Biomechanics, Faculty of Engineering Science, University of Bayreuth, D-95440 Bayreuth, Germany;
| | - Adin Ming Tan
- Department of Health and Medical Sciences, School of Health Sciences, Hawthorn Campus, Swinburne University of Technology, Melbourne 3122, Australia; (O.T.); (A.M.T.)
| | - Erich Rutz
- Department of Orthopaedics, The Royal Children’s Hospital, Melbourne 3052, Australia;
- Murdoch Children’s Research Institute, MCRI, Parkville, Melbourne 3052, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne 3052, Australia
- Medical Faculty, University of Basel, 4001 Basel, Switzerland
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Hellec J, Chorin F, Castagnetti A, Guérin O, Colson SS. Smart Eyeglasses: A Valid and Reliable Device to Assess Spatiotemporal Parameters during Gait. SENSORS 2022; 22:s22031196. [PMID: 35161941 PMCID: PMC8846265 DOI: 10.3390/s22031196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 12/14/2022]
Abstract
The study aims to determine the validity and reproducibility of step duration and step length parameters measured during walking in healthy participants using an accelerometer embedded in smart eyeglasses. Twenty young volunteers participated in two identical sessions comprising a 30 s gait assessment performed at three different treadmill speeds under two conditions (i.e., with and without a cervical collar). Spatiotemporal parameters (i.e., step duration and step length normalized by the lower limb length) were obtained with both the accelerometer embedded in smart eyeglasses and an optoelectronic system. The relative intra- and inter-session reliability of step duration and step length computed from the vertical acceleration data were excellent for all experimental conditions. An excellent absolute reliability was observed for the eyeglasses for all conditions and concurrent validity between systems was observed. An accelerometer incorporated in smart eyeglasses is accurate to measure step duration and step length during gait.
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Affiliation(s)
- Justine Hellec
- Université Côte d’Azur, LAMHESS, EUR HEALTHY, 06205 Nice, France; (F.C.); (S.S.C.)
- Ellcie Healthy, 06600 Antibes, France
- Correspondence:
| | - Frédéric Chorin
- Université Côte d’Azur, LAMHESS, EUR HEALTHY, 06205 Nice, France; (F.C.); (S.S.C.)
- Université Côte d’Azur, CHU, Cimiez, Plateforme Fragilité, 06000 Nice, France
| | | | | | - Serge S. Colson
- Université Côte d’Azur, LAMHESS, EUR HEALTHY, 06205 Nice, France; (F.C.); (S.S.C.)
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33
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Herwig A, Agic A, Huppertz HJ, Klingebiel R, Zuhorn F, Schneider WX, Schäbitz WR, Rogalewski A. Differentiating Progressive Supranuclear Palsy and Parkinson's Disease With Head-Mounted Displays. Front Neurol 2022; 12:791366. [PMID: 35002933 PMCID: PMC8733559 DOI: 10.3389/fneur.2021.791366] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Progressive supranuclear palsy (PSP) is a neurodegenerative disorder that, especially in the early stages of the disease, is clinically difficult to distinguish from Parkinson's disease (PD). Objective: This study aimed at assessing the use of eye-tracking in head-mounted displays (HMDs) for differentiating PSP and PD. Methods: Saccadic eye movements of 13 patients with PSP, 15 patients with PD, and a group of 16 healthy controls (HCs) were measured. To improve applicability in an inpatient setting and standardize the diagnosis, all the tests were conducted in a HMD. In addition, patients underwent atlas-based volumetric analysis of various brain regions based on high-resolution MRI. Results: Patients with PSP displayed unique abnormalities in vertical saccade velocity and saccade gain, while horizontal saccades were less affected. A novel diagnostic index was derived, multiplying the ratios of vertical to horizontal gain and velocity, allowing segregation of PSP from PD with high sensitivity (10/13, 77%) and specificity (14/15, 93%). As expected, patients with PSP as compared with patients with PD showed regional atrophy in midbrain volume, the midbrain plane, and the midbrain tegmentum plane. In addition, we found for the first time that oculomotor measures (vertical gain, velocity, and the diagnostic index) were correlated significantly to midbrain volume in the PSP group. Conclusions: Assessing eye movements in a HMD provides an easy to apply and highly standardized tool to differentiate PSP of patients from PD and HCs, which will aid in the diagnosis of PSP.
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Affiliation(s)
- Arvid Herwig
- Department of Psychology, Clinical Psychology and Psychotherapy, University of Bremen, Bremen, Germany.,Department of Psychology, Neuro-Cognitive Psychology, and Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany
| | - Almedin Agic
- Department of Neurology, Evangelisches Klinikum Bethel, University Hospital OWL of the University Bielefeld, Bielefeld, Germany
| | | | - Randolf Klingebiel
- Department of Neuroradiology, Evangelisches Klinikum Bethel, University Hospital OWL of the University Bielefeld, Bielefeld, Germany
| | - Frédéric Zuhorn
- Department of Neurology, Evangelisches Klinikum Bethel, University Hospital OWL of the University Bielefeld, Bielefeld, Germany
| | - Werner X Schneider
- Department of Psychology, Neuro-Cognitive Psychology, and Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany
| | - Wolf-Rüdiger Schäbitz
- Department of Neurology, Evangelisches Klinikum Bethel, University Hospital OWL of the University Bielefeld, Bielefeld, Germany
| | - Andreas Rogalewski
- Department of Neurology, Evangelisches Klinikum Bethel, University Hospital OWL of the University Bielefeld, Bielefeld, Germany
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Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor. SENSORS 2022; 22:s22020412. [PMID: 35062375 PMCID: PMC8778464 DOI: 10.3390/s22020412] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/23/2021] [Accepted: 01/05/2022] [Indexed: 02/06/2023]
Abstract
Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.
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Sibley K, Girges C, Candelario J, Milabo C, Salazar M, Esperida JO, Dushin Y, Limousin P, Foltynie T. An Evaluation of KELVIN, an Artificial Intelligence Platform, as an Objective Assessment of the MDS UPDRS Part III. JOURNAL OF PARKINSON'S DISEASE 2022; 12:2223-2233. [PMID: 36155530 DOI: 10.3233/jpd-223493] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
BACKGROUND Parkinson's disease severity is typically measured using the Movement Disorder Society Unified Parkinson's disease rating scale (MDS-UPDRS). While training for this scale exists, users may vary in how they score a patient with the consequence of intra-rater and inter-rater variability. OBJECTIVE In this study we explored the consistency of an artificial intelligence platform compared with traditional clinical scoring in the assessment of motor severity in PD. METHODS Twenty-two PD patients underwent simultaneous MDS-UPDRS scoring by two experienced MDS-UPDRS raters and the two sets of accompanying video footage were also scored by an artificial intelligence video analysis platform known as KELVIN. RESULTS KELVIN was able to produce a summary score for 7 MDS-UPDRS part 3 items with good inter-rater reliability (Intraclass Correlation Coefficient (ICC) 0.80 in the OFF-medication state, ICC 0.73 in the ON-medication state). Clinician scores had exceptionally high levels of inter-rater reliability in both the OFF (0.99) and ON (0.94) medication conditions (possibly reflecting the highly experienced team). There was an ICC of 0.84 in the OFF-medication state and 0.31 in the ON-medication state between the mean Clinician and mean Kelvin scores for the equivalent 7 motor items, possibly due to dyskinesia impacting on the KELVIN scores. CONCLUSION We conclude that KELVIN may prove useful in the capture and scoring of multiple items of MDS-UPDRS part 3 with levels of consistency not far short of that achieved by experienced MDS-UPDRS clinical raters, and is worthy of further investigation.
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Affiliation(s)
- Krista Sibley
- Department of Clinical & Movement Neurosciences, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Christine Girges
- Department of Clinical & Movement Neurosciences, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Joseph Candelario
- Department of Clinical & Movement Neurosciences, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Catherine Milabo
- Department of Clinical & Movement Neurosciences, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Maricel Salazar
- Department of Clinical & Movement Neurosciences, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
| | - John Onil Esperida
- Department of Clinical & Movement Neurosciences, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
| | | | - Patricia Limousin
- Department of Clinical & Movement Neurosciences, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Thomas Foltynie
- Department of Clinical & Movement Neurosciences, UCL Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, UK
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Wang X, Chen L, Zhou H, Xu Y, Zhang H, Yang W, Tang X, Wang J, Lv Y, Yan P, Peng Y. Enriched Rehabilitation Improves Gait Disorder and Cognitive Function in Parkinson's Disease: A Randomized Clinical Trial. Front Neurosci 2021; 15:733311. [PMID: 34924926 PMCID: PMC8674725 DOI: 10.3389/fnins.2021.733311] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Studies on non-pharmacological strategies for improving gait performance and cognition in Parkinson's disease (PD) are of great significance. We aimed to investigate the effect of and mechanism underlying enriched rehabilitation as a potentially effective strategy for improving gait performance and cognition in early-stage PD. Methods: Forty participants with early-stage PD were randomly assigned to receive 12 weeks (2 h/day, 6 days/week) of enriched rehabilitation (ER; n = 20; mean age, 66.14 ± 4.15 years; 45% men) or conventional rehabilitation (CR; n = 20; mean age 65.32 ± 4.23 years; 50% men). In addition, 20 age-matched healthy volunteers were enrolled as a control (HC) group. We assessed the general motor function using the Unified PD Rating Scale-Part III (UPDRS-III) and gait performance during single-task (ST) and dual-task (DT) conditions pre- and post-intervention. Cognitive function assessments included the Montreal Cognitive Assessment (MoCA), the Symbol Digit Modalities Test (SDMT), and the Trail Making Test (TMT), which were conducted pre- and post-intervention. We also investigated alteration in positive resting-state functional connectivity (RSFC) of the left dorsolateral prefrontal cortex (DLPFC) in participants with PD, mediated by ER, using functional magnetic resonance imaging (fMRI). Results: Compared with the HC group, PD participants in both ER and CR groups performed consistently poorer on cognitive and motor assessments. Significant improvements were observed in general motor function as assessed by the UPDRS-III in both ER and CR groups post-intervention. However, only the ER group showed improvements in gait parameters under ST and DT conditions post-intervention. Moreover, ER had a significant effect on cognition, which was reflected in increased MoCA, SDMT, and TMT scores post-intervention. MoCA, SDMT, and TMT scores were significantly different between ER and CR groups post-intervention. The RSFC analysis showed strengthened positive functional connectivity between the left DLPFC and other brain areas including the left insula and left inferior frontal gyrus (LIFG) post-ER. Conclusion: Our findings indicated that ER could serve as a potentially effective therapy for early-stage PD for improving gait performance and cognitive function. The underlying mechanism based on fMRI involved strengthened RSFC between the left DLPFC and other brain areas (e.g., the left insula and LIFG).
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Affiliation(s)
- Xin Wang
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - LanLan Chen
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongyu Zhou
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Yao Xu
- Department of Neurology, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongying Zhang
- Department of Medical Imaging, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenrui Yang
- Graduate School, Dalian Medical University, Dalian, China
| | - XiaoJia Tang
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Junya Wang
- Medical College, Yangzhou University, Yangzhou, China
| | - Yichen Lv
- School of Rehabilitation Medicine, Binzhou Medical University, Yantai, China
| | - Ping Yan
- School of Nursing, Yangzhou University, Yangzhou, China
| | - Yuan Peng
- Department of Rehabilitation Medicine, Guangzhou First People's Hospital, Guangzhou, China
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Weizman Y, Tirosh O, Beh J, Fuss FK, Pedell S. Gait Assessment Using Wearable Sensor-Based Devices in People Living with Dementia: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182312735. [PMID: 34886459 PMCID: PMC8656771 DOI: 10.3390/ijerph182312735] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/30/2021] [Accepted: 11/30/2021] [Indexed: 11/28/2022]
Abstract
The ability of people living with dementia to walk independently is a key contributor to their overall well-being and autonomy. For this reason, understanding the relationship between dementia and gait is significant. With rapidly emerging developments in technology, wearable devices offer a portable and affordable alternative for healthcare experts to objectively estimate kinematic parameters with great accuracy. This systematic review aims to provide an updated overview and explore the opportunities in the current research on wearable sensors for gait analysis in adults over 60 living with dementia. A systematic search was conducted in the following scientific databases: PubMed, Cochrane Library, and IEEE Xplore. The targeted search identified 1992 articles that were potentially eligible for inclusion, but, following title, abstract, and full-text review, only 6 articles were deemed to meet the inclusion criteria. Most studies performed adequately on measures of reporting, in and out of a laboratory environment, and found that sensor-derived data are successful in their respective objectives and goals. Nevertheless, we believe that additional studies utilizing standardized protocols should be conducted in the future to explore the impact and usefulness of wearable devices in gait-related characteristics such as fall prognosis and early diagnosis in people living with dementia.
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Affiliation(s)
- Yehuda Weizman
- Department of Health and Medical Science, School of Health Science, Swinburne University of Technology, Hawthorn, VIC 3122, Australia;
- Correspondence: ; Tel.: +61-3921-45320
| | - Oren Tirosh
- Department of Health and Medical Science, School of Health Science, Swinburne University of Technology, Hawthorn, VIC 3122, Australia;
| | - Jeanie Beh
- Centre for Design Innovation, Swinburne University of Technology, Hawthorn, VIC 3122, Australia; (J.B.); (S.P.)
| | - Franz Konstantin Fuss
- Chair of Biomechanics, Faculty of Engineering Science, University of Bayreuth, D-95440 Bayreuth, Germany;
| | - Sonja Pedell
- Centre for Design Innovation, Swinburne University of Technology, Hawthorn, VIC 3122, Australia; (J.B.); (S.P.)
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Adams JL, Dinesh K, Snyder CW, Xiong M, Tarolli CG, Sharma S, Dorsey ER, Sharma G. A real-world study of wearable sensors in Parkinson's disease. NPJ Parkinsons Dis 2021; 7:106. [PMID: 34845224 PMCID: PMC8629990 DOI: 10.1038/s41531-021-00248-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 10/27/2021] [Indexed: 12/17/2022] Open
Abstract
Most wearable sensor studies in Parkinson's disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson's disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson's walked significantly less (median [inter-quartile range]: 4980 [2835-7163] steps/day) than controls (7367 [5106-8928] steps/day; P = 0.04). Tremor was present for 1.6 [0.4-5.9] hours (median [range]) per day in most-affected hands (MDS-UPDRS 3.17a or 3.17b = 1-4) of individuals with Parkinson's, which was significantly higher than the 0.5 [0.3-2.3] hours per day in less-affected hands (MDS-UPDRS 3.17a or 3.17b = 0). These results, which require replication in larger cohorts, advance our understanding of the manifestations of Parkinson's in real-world settings.
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Affiliation(s)
- Jamie L Adams
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
| | - Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | | | - Mulin Xiong
- Michigan State University College of Human Medicine, East Lansing, MI, USA
| | - Christopher G Tarolli
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Saloni Sharma
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - E Ray Dorsey
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
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Standardized Biomechanical Investigation of Posture and Gait in Pisa Syndrome Disease. Symmetry (Basel) 2021. [DOI: 10.3390/sym13122237] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Pisa syndrome is one of the possible postural deformities associated with Parkinson’s disease and it is clinically defined as a sustained lateral bending of the trunk. Some previous studies proposed clinical and biomechanical investigation to understand the pathophysiological mechanisms that occur, mainly focusing on EMG patterns and clinics. The current research deals with the assessment of a standardized biomechanical analysis to investigate the Pisa syndrome postural effects. Eight patients participated in the experimental test. Both static posture and gait trials were performed. An optoelectronic system and two force plates were used for data acquisition, while a custom multi-segments kinematic model of the human spine was used to evaluate the 3D angles. All subjects showed an important flexion of the trunk superior segment with respect to the inferior one, with a strong variability among patients (range values between 4.3° and 41.0°). Kinematics, ground reaction forces and spatio-temporal parameters are influenced by the asymmetrical trunk posture. Moreover, different proprioception, compensation and abilities of correction were depicted among subjects. Considering the forces exchanged by the feet with the floor during standing, results highlighted a significant asymmetry (p-value = 0.02) between the omo and contralateral side in a normal static posture, with greater load distribution on the same side of lateral deviation. When asked to self-correct the posture, all patients demonstrated a reduction of asymmetry, but without stressing any statistical significance. All these aspects might be crucial for the definition of a PS patients’ classification and for the assessment of the efficacy of treatments and rehabilitation.
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Betteridge C, Mobbs RJ, Fonseka RD, Natarajan P, Ho D, Choy WJ, Sy LW, Pell N. Objectifying clinical gait assessment: using a single-point wearable sensor to quantify the spatiotemporal gait metrics of people with lumbar spinal stenosis. JOURNAL OF SPINE SURGERY 2021; 7:254-268. [PMID: 34734130 DOI: 10.21037/jss-21-16] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 06/25/2021] [Indexed: 11/06/2022]
Abstract
Background Wearable accelerometer-containing devices have become a mainstay in clinical studies which attempt to classify the gait patterns in various diseases. A gait profile for lumbar spinal stenosis (LSS) has not been developed, and no study has validated a simple wearable system for the clinical assessment of gait in lumbar stenosis. This study identifies the changes to gait patterns that occur in LSS to create a preliminary disease-specific gait profile. In addition, this study compares a chest-based wearable sensor, the MetaMotionC© device and inertial measurement unit python script (MMC/IMUPY) system, against a reference-standard, videography, to preliminarily assess its accuracy in measuring the gait features of patients with LSS. Methods We conduct a cross-sectional observational study examining the walking patterns of 25 LSS patients and 33 healthy controls. To construct a preliminary disease-specific gait profile for LSS, the gait patterns of the 25 LSS patients and 25 healthy controls with similar ages were compared. To assess the accuracy of the MMC/IMUPY system in measuring the gait features of patients with LSS, its results were compared with videography for the 21 LSS and 33 healthy controls whose walking bouts exceeded 30 m. Results Patients suffering from LSS walked significantly slower, with shorter, less frequent steps and higher asymmetry compared to healthy controls. The MMC/IMUPY system had >90% agreement with videography for all spatiotemporal gait metrics that both methods could measure. Conclusions The MMC/IMUPY system is a simple and feasible system for the construction of a preliminary disease-specific gait profile for LSS. Before clinical application in everyday living conditions is possible, further studies involving the construction of a more detailed disease-specific gait profile for LSS by disease severity, and the validation of the MMC/IMUPY system in the home environment, are required.
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Affiliation(s)
- Callum Betteridge
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Ralph J Mobbs
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - R Dineth Fonseka
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Pragadesh Natarajan
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Daniel Ho
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Wen Jie Choy
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Luke W Sy
- NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia.,School of Biomechanics, University of New South Wales, Sydney, Australia
| | - Nina Pell
- NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
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Ullrich M, Kuderle A, Reggi L, Cereatti A, Eskofier BM, Kluge F. Machine learning-based distinction of left and right foot contacts in lower back inertial sensor gait data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5958-5961. [PMID: 34892475 DOI: 10.1109/embc46164.2021.9630653] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Digital gait measures derived from wearable inertial sensors have been shown to support the treatment of patients with motor impairments. From a technical perspective, the detection of left and right initial foot contacts (ICs) is essential for the computation of stride-by-stride outcome measures including gait asymmetry. However, in a majority of studies only one sensor close to the center of mass is used, complicating the assignment of detected ICs to the respective foot. Therefore, we developed an algorithm including supervised machine learning (ML) models for the robust classification of left and right ICs using multiple features from the gyroscope located at the lower back. The approach was tested on a data set including 40 participants (ten healthy controls, ten hemiparetic, ten Parkinson's disease, and ten Huntington's disease patients) and reached an accuracy of 96.3% for the overall data set and up to 100.0% for the Parkinson's sub data set. These results were compared to a state-of-the-art algorithm. The ML approaches outperformed this traditional algorithm in all subgroups. Our study contributes to an improved classification of left and right ICs in inertial sensor signals recorded at the lower back and thus enables a reliable computation of clinically relevant mobility measures.
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Godkin FE, Turner E, Demnati Y, Vert A, Roberts A, Swartz RH, McLaughlin PM, Weber KS, Thai V, Beyer KB, Cornish B, Abrahao A, Black SE, Masellis M, Zinman L, Beaton D, Binns MA, Chau V, Kwan D, Lim A, Munoz DP, Strother SC, Sunderland KM, Tan B, McIlroy WE, Van Ooteghem K. Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease. J Neurol 2021; 269:2673-2686. [PMID: 34705114 PMCID: PMC8548705 DOI: 10.1007/s00415-021-10831-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 01/02/2023]
Abstract
BACKGROUND Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD). METHODS Thirty-nine participants with CVD, Alzheimer's disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson's disease, or amyotrophic lateral sclerosis (median age 68 (45-83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance. RESULTS Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17-22 min/day), with significant differences between some locations (p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear (p < 0.001) and there was a small but significant increase in non-wear over the collection period (p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners. CONCLUSION A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.
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Affiliation(s)
- F Elizabeth Godkin
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Erin Turner
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Youness Demnati
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Adam Vert
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Angela Roberts
- School of Communication Sciences and Disorders, Elborn College, Western University, London, ON, Canada.,Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
| | - Richard H Swartz
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Kyle S Weber
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Vanessa Thai
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kit B Beyer
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Benjamin Cornish
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Agessandro Abrahao
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sandra E Black
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Mario Masellis
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Lorne Zinman
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Malcolm A Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Vivian Chau
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Donna Kwan
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Andrew Lim
- Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Douglas P Munoz
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Kelly M Sunderland
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - William E McIlroy
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Karen Van Ooteghem
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
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Ali F, Loushin SR, Botha H, Josephs KA, Whitwell JL, Kaufman K. Laboratory based assessment of gait and balance impairment in patients with progressive supranuclear palsy. J Neurol Sci 2021; 429:118054. [PMID: 34461552 PMCID: PMC8489851 DOI: 10.1016/j.jns.2021.118054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 07/27/2021] [Accepted: 08/23/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Gait and balance abnormalities are a significant source of morbidity and mortality in progressive supranuclear palsy (PSP). Gait impairment in PSP is primarily assessed clinically on exam or with the use of rating scales. Three dimensional video based gait and balance analysis performed in a laboratory setting is a highly accurate method of motion analysis (Wren et al., 2020), however limited data is available in patients with PSP. RESEARCH QUESTION In this study we assess the objective features of postural control, kinematics, kinetic and temporal-spatial gait metrics in PSP, using three-dimensional video motion analysis in a laboratory setting compared to normal controls. METHODS Three-dimensional motion was captured using a 10-camera motion capture system, 41 body markers and ground embedded force plates in 16 patients with PSP patients and compared to motorically normal controls. RESULTS Spatiotemporal, kinematic, and kinetic gait measures effectively differentiated patients with PSP from controls. Patients had slower gait velocity, lower cadence, increased double support time and abnormal antero-posterior sway. Joint kinematics and kinetics were reduced and showed less variation among patients with PSP compared to controls which is suggestive of bradykinesia. Objective gait measures of abnormality correlated with clinical disease severity. Postural sway metrics distinguished PSP from controls and captured gait imbalance. SIGNIFICANCE Objective measures of gait and balance abnormalities in patients with PSP provide an outcome measure that can be potentially used for early disease detection, in clinical trials and to validate portable motion capture devices in the future.
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Affiliation(s)
- Farwa Ali
- Department of Neurology, Rochester, MN, United States of America.
| | - Stacy R Loushin
- Department of Orthopedic Surgery, Rochester, MN, United States of America
| | - Hugo Botha
- Department of Neurology, Rochester, MN, United States of America
| | - Keith A Josephs
- Department of Neurology, Rochester, MN, United States of America
| | - Jennifer L Whitwell
- Department of Radiology, Mayo Clinic Rochester, Rochester, MN, United States of America
| | - Kenton Kaufman
- Department of Orthopedic Surgery, Rochester, MN, United States of America
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Picerno P, Iosa M, D'Souza C, Benedetti MG, Paolucci S, Morone G. Wearable inertial sensors for human movement analysis: a five-year update. Expert Rev Med Devices 2021; 18:79-94. [PMID: 34601995 DOI: 10.1080/17434440.2021.1988849] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings. AREAS COVERED Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings. EXPERT OPINION IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.
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Affiliation(s)
- Pietro Picerno
- SMART Engineering Solutions & Technologies (SMARTEST) Research Center, Università Telematica "Ecampus", Novedrate, Comune, Italy
| | - Marco Iosa
- Department of Psychology, Sapienza University, Rome, Italy.,Irrcs Santa Lucia Foundation, Rome, Italy
| | - Clive D'Souza
- Center for Ergonomics, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, USA.,Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Maria Grazia Benedetti
- Physical Medicine and Rehabilitation Unit, IRCCS-Istituto Ortopedico Rizzoli, Bologna, Italy
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Sharma P, Pahuja SK, Veer K. A Systematic Review of Machine Learning Based Gait characteristics in Parkinson's disease. Mini Rev Med Chem 2021; 22:1216-1229. [PMID: 34579631 DOI: 10.2174/1389557521666210927151553] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/29/2021] [Accepted: 05/18/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Parkinson's disease is a pervasive neuro disorder that affects people's quality of life throughout the world. The unsatisfactory results of clinical rating scales open the door for more research. PD treatment using current biomarkers seems a difficult task. So automatic evaluation at an early stage may enhance the quality and time-period of life. METHODS Grading of Recommendations Assessment, Development, and Evaluation (GRADE) and Population, intervention, comparison, and outcome (PICO) search methodology schemes are followed to search the data and eligible studies for this survey. Approximate 1500 articles were extracted using related search strings. After the stepwise mapping and elimination of studies, 94 papers are found suitable for the present review. RESULTS After the quality assessment of extracted studies, nine inhibitors are identified to analyze people's gait with Parkinson's disease, where four are critical. This review also differentiates the various machine learning classification techniques with their PD analysis characteristics in previous studies. The extracted research gaps are described as future perspectives. Results can help practitioners understand the PD gait as a valuable biomarker for detection, quantification, and classification. CONCLUSION Due to less cost and easy recording of gait, gait-based techniques are becoming popular in PD detection. By encapsulating the gait-based studies, it gives an in-depth knowledge of PD, different measures that affect gait detection and classification.
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Affiliation(s)
- Pooja Sharma
- Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab. India
| | - S K Pahuja
- Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab. India
| | - Karan Veer
- Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab. India
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Lee YY, Li MH, Luh JJ, Tai CH. Reliability of using foot-worn devices to measure gait parameters in people with Parkinson's disease. NeuroRehabilitation 2021; 49:57-64. [PMID: 34180427 DOI: 10.3233/nre-210101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Recent advances in technology have warranted the use of wearable sensors to monitor gait and posture. However, the psychometric properties of using wearable devices to measure gait-related outcomes have not been fully established in patients with Parkinson's disease (PD). OBJECTIVE This study aimed to investigate the test-retest reliability of body-worn sensors for gait evaluation in people with PD. Additionally, the influence of disease severity on the reliability was determined. METHODS Twenty individuals with PD were recruited. During the first evaluation, the participants wore inertial sensors on their shoes and walked along a walkway thrice at their comfortable walking speed. The participants were then required to return to the lab after 3-5 days to complete the second evaluation with the same study procedure. Test-retest reliability of gait-related outcomes were calculated. To determine whether the results would be affected by disease severity, reliability was re-calculated by subdividing the participants into early and mid-advanced stages of the disease. RESULTS The results showed moderate to good reliability (ICC = 0.64-0.87) of the wearable sensors for gait assessment in the general population with PD. Subgroup analysis showed that the reliability was higher among patients at early stages (ICC = 0.71-0.97) compared to those at mid-advanced stages (ICC = 0.65-0.81) of PD. CONCLUSIONS Wearable sensors could reliably measure gait parameters in people with PD, and the reliability was higher among individuals at early stages of the disease compared to those at mid-advanced stages. Absolute reliability values were calculated to act as references for future studies.
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Affiliation(s)
- Ya-Yun Lee
- School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.,Division of Physical Therapy, Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan
| | - Min-Hao Li
- School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.,Division of Physical Therapy, Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan
| | - Jer-Junn Luh
- School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chun-Hwei Tai
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
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Ghislieri M, Agostini V, Rizzi L, Knaflitz M, Lanotte M. Atypical Gait Cycles in Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2021; 21:5079. [PMID: 34372315 PMCID: PMC8347347 DOI: 10.3390/s21155079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/21/2021] [Accepted: 07/23/2021] [Indexed: 12/15/2022]
Abstract
It is important to find objective biomarkers for evaluating gait in Parkinson's Disease (PD), especially related to the foot and lower leg segments. Foot-switch signals, analyzed through Statistical Gait Analysis (SGA), allow the foot-floor contact sequence to be characterized during a walking session lasting five-minutes, which includes turnings. Gait parameters were compared between 20 PD patients and 20 age-matched controls. PDs showed similar straight-line speed, cadence, and double-support compared to controls, as well as typical gait-phase durations, except for a small decrease in the flat-foot contact duration (-4% of the gait cycle, p = 0.04). However, they showed a significant increase in atypical gait cycles (+42%, p = 0.006), during both walking straight and turning. A forefoot strike, instead of a "normal" heel strike, characterized the large majority of PD's atypical cycles, whose total percentage was 25.4% on the most-affected and 15.5% on the least-affected side. Moreover, we found a strong correlation between the atypical cycles and the motor clinical score UPDRS-III (r = 0.91, p = 0.002), in the subset of PD patients showing an abnormal number of atypical cycles, while we found a moderate correlation (r = 0.60, p = 0.005), considering the whole PD population. Atypical cycles have proved to be a valid biomarker to quantify subtle gait dysfunctions in PD patients.
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Affiliation(s)
- Marco Ghislieri
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy; (V.A.); (M.K.)
- PoliToBIOMed Lab, Politecnico di Torino, 10129 Turin, Italy
| | - Valentina Agostini
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy; (V.A.); (M.K.)
- PoliToBIOMed Lab, Politecnico di Torino, 10129 Turin, Italy
| | - Laura Rizzi
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, 10126 Turin, Italy; (L.R.); (M.L.)
- AOU Città della Salute e della Scienza di Torino, 10126 Turin, Italy
| | - Marco Knaflitz
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy; (V.A.); (M.K.)
- PoliToBIOMed Lab, Politecnico di Torino, 10129 Turin, Italy
| | - Michele Lanotte
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, 10126 Turin, Italy; (L.R.); (M.L.)
- AOU Città della Salute e della Scienza di Torino, 10126 Turin, Italy
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Mulas I, Putzu V, Asoni G, Viale D, Mameli I, Pau M. Clinical assessment of gait and functional mobility in Italian healthy and cognitively impaired older persons using wearable inertial sensors. Aging Clin Exp Res 2021; 33:1853-1864. [PMID: 32978750 PMCID: PMC7518096 DOI: 10.1007/s40520-020-01715-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 09/09/2020] [Indexed: 12/14/2022]
Abstract
AIM The main purpose of the present study was to verify the feasibility of wearable inertial sensors (IMUs) in a clinical setting to screen gait and functional mobility in Italian older persons. In particular, we intended to verify the capability of IMUs to discriminate individuals with and without cognitive impairments and assess the existence of significant correlations between mobility parameters extracted by processing trunk accelerations and cognitive status. METHODS This is a cross-sectional study performed on 213 adults aged over 65 years (mean age 77.0 ± 5.4; 62% female) who underwent cognitive assessment (through Addenbrooke's Cognitive Examination Revised, ACE-R) instrumental gait analysis and the Timed Up and Go (TUG) test carried out using a wearable IMU located in the lower back. RESULTS Individuals with cognitive impairments exhibit a peculiar gait pattern, characterized by significant reduction of speed (- 34% vs. healthy individuals), stride length (- 28%), cadence (- 9%), and increase in double support duration (+ 11%). Slight, but significant changes in stance and swing phase duration were also detected. Poorer performances in presence of cognitive impairment were observed in terms of functional mobility as overall and sub-phase TUG times resulted significantly higher with respect to healthy individuals (overall time, + 38%, sub-phases times ranging from + 22 to + 34%), although with some difference associated with age. The severity of mobility alterations was found moderately to strongly correlated with the ACE-R score (Spearman's rho = 0.58 vs. gait speed, 0.54 vs. stride length, 0.66 vs. overall TUG time). CONCLUSION The findings obtained in the present study suggest that wearable IMUs appear to be an effective solution for the clinical assessment of mobility parameters of older persons screened for cognitive impairments within a clinical setting. They may represent a useful tool for the clinician in verifying the effectiveness of interventions to alleviate the impact of mobility limitations on daily life in cognitively impaired individuals.
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Affiliation(s)
- Ilaria Mulas
- Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Piazza d'Armi, 09123, Cagliari, Italy
| | - Valeria Putzu
- Center for Cognitive Disorders and Dementia, Geriatric Unit SS. Trinità Hospital, Via Romagna 16, 09127, Cagliari, Italy
| | - Gesuina Asoni
- Center for Cognitive Disorders and Dementia, Geriatric Unit SS. Trinità Hospital, Via Romagna 16, 09127, Cagliari, Italy
| | - Daniela Viale
- Center for Cognitive Disorders and Dementia, Geriatric Unit SS. Trinità Hospital, Via Romagna 16, 09127, Cagliari, Italy
| | - Irene Mameli
- Center for Cognitive Disorders and Dementia, Geriatric Unit SS. Trinità Hospital, Via Romagna 16, 09127, Cagliari, Italy
| | - Massimiliano Pau
- Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Piazza d'Armi, 09123, Cagliari, Italy.
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Cicirelli G, Impedovo D, Dentamaro V, Marani R, Pirlo G, D'Orazio TR. Human Gait Analysis in Neurodegenerative Diseases: a Review. IEEE J Biomed Health Inform 2021; 26:229-242. [PMID: 34181559 DOI: 10.1109/jbhi.2021.3092875] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegnerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. The work continues with a survey on the publicly available datasets principally used for comparing results. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined.
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50
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Corrà MF, Atrsaei A, Sardoreira A, Hansen C, Aminian K, Correia M, Vila-Chã N, Maetzler W, Maia L. Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson's Disease. SENSORS 2021; 21:s21123974. [PMID: 34207565 PMCID: PMC8229328 DOI: 10.3390/s21123974] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/31/2021] [Accepted: 06/07/2021] [Indexed: 11/16/2022]
Abstract
Accurate assessment of Parkinson's disease (PD) ON and OFF states in the usual environment is essential for tailoring optimal treatments. Wearables facilitate measurements of gait in novel and unsupervised environments; however, differences between unsupervised and in-laboratory measures have been reported in PD. We aimed to investigate whether unsupervised gait speed discriminates medication states and which supervised tests most accurately represent home performance. In-lab gait speeds from different gait tasks were compared to home speeds of 27 PD patients at ON and OFF states using inertial sensors. Daily gait speed distribution was expressed in percentiles and walking bout (WB) length. Gait speeds differentiated ON and OFF states in the lab and the home. When comparing lab with home performance, ON assessments in the lab showed moderate-to-high correlations with faster gait speeds in unsupervised environment (r = 0.69; p < 0.001), associated with long WB. OFF gait assessments in the lab showed moderate correlation values with slow gait speeds during OFF state at home (r = 0.56; p = 0.004), associated with short WB. In-lab and daily assessments of gait speed with wearables capture additional integrative aspects of PD, reflecting different aspects of mobility. Unsupervised assessment using wearables adds complementary information to the clinical assessment of motor fluctuations in PD.
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Affiliation(s)
- Marta Francisca Corrà
- Abel Salazar Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313 Porto, Portugal; (M.C.); (L.M.)
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
- Correspondence:
| | - Arash Atrsaei
- Laboratory of Movement Analysis and Measurement, Swiss Federal Institute of Technology in Lausanne (EPFL), 1015 Lausanne, Switzerland; (A.A.); (K.A.)
| | - Ana Sardoreira
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
| | - Clint Hansen
- Department of Neurology, Christian-Albrechts-University, 24118 Kiel, Germany; (C.H.); (W.M.)
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, Swiss Federal Institute of Technology in Lausanne (EPFL), 1015 Lausanne, Switzerland; (A.A.); (K.A.)
| | - Manuel Correia
- Abel Salazar Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313 Porto, Portugal; (M.C.); (L.M.)
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
| | - Nuno Vila-Chã
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts-University, 24118 Kiel, Germany; (C.H.); (W.M.)
| | - Luís Maia
- Abel Salazar Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313 Porto, Portugal; (M.C.); (L.M.)
- University Hospital Santo Antonio of Porto (CHUP), 4099-001 Porto, Portugal; (A.S.); (N.V.-C.)
- Institute for Research and Innovation in Health (i3s), University of Porto, 4200-135 Porto, Portugal
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