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Elbatanouny H, Kleanthous N, Dahrouj H, Alusi S, Almajali E, Mahmoud S, Hussain A. Insights into Parkinson's Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:3959. [PMID: 38931743 PMCID: PMC11207947 DOI: 10.3390/s24123959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/29/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024]
Abstract
Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients' quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.
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Affiliation(s)
- Hagar Elbatanouny
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | | | - Hayssam Dahrouj
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | - Sundus Alusi
- The Walton Centre NHS Foundation Trust, Liverpool L9 7LJ, UK;
| | - Eqab Almajali
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | - Soliman Mahmoud
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
- University of Khorfakkan, Khorfakkan, Sharjah 18119, United Arab Emirates
| | - Abir Hussain
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
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Zoetewei D, Herman T, Ginis P, Palmerini L, Brozgol M, Thumm PC, Ferrari A, Ceulemans E, Decaluwé E, Hausdorff JM, Nieuwboer A. On-Demand Cueing for Freezing of Gait in Parkinson's Disease: A Randomized Controlled Trial. Mov Disord 2024; 39:876-886. [PMID: 38486430 DOI: 10.1002/mds.29762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/24/2024] [Accepted: 02/09/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Cueing can alleviate freezing of gait (FOG) in people with Parkinson's disease (PD), but using the same cues continuously in daily life may compromise effectiveness. Therefore, we developed the DeFOG-system to deliver personalized auditory cues on detection of a FOG episode. OBJECTIVES We aimed to evaluate the effects of DeFOG during a FOG-provoking protocol: (1) after 4 weeks of DeFOG-use in daily life against an active control group; (2) after immediate DeFOG-use (within-group) in different medication states. METHOD In this randomized controlled trial, 63 people with PD and daily FOG were allocated to the DeFOG or active control group. Both groups received feedback on their daily living step counts using the device, but the DeFOG group also received on-demand cueing. Video-rated FOG severity was compared pre- and post-intervention through a FOG-provoking protocol administered at home off and on-medication, but without using DeFOG. Within-group effects were tested by comparing FOG during the protocol with and without DeFOG. RESULTS DeFOG-use during the 4 weeks was similar between groups, but we found no between-group differences in FOG-severity. However, the within-group analysis showed that FOG was alleviated by DeFOG (effect size d = 0.57), regardless of medication state. Combining DeFOG and medication yielded an effect size of d = 0.67. CONCLUSIONS DeFOG reduced FOG considerably in a population of severe freezers both off and on medication. Nonetheless, 4 weeks of DeFOG-use in daily life did not ameliorate FOG during the protocol unless DeFOG was worn. These findings suggest that on-demand cueing is only effective when used, similar to other walking aids. © 2024 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Demi Zoetewei
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Talia Herman
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Pieter Ginis
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Luca Palmerini
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Marina Brozgol
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Pablo Cornejo Thumm
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Alberto Ferrari
- Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena, Italy
- Science and Technology Park for Medicine, TPM, Democenter Foundation Mirandola, Modena, Italy
| | - Eva Ceulemans
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Eva Decaluwé
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Israel
- Department of Physical Therapy, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center and Department of Orthopedic Surgery, Rush University, Chicago, Illinois, USA
| | - Alice Nieuwboer
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
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Zhang W, Sun H, Huang D, Zhang Z, Li J, Wu C, Sun Y, Gong M, Wang Z, Sun C, Cui G, Guo Y, Chan P. Detection and prediction of freezing of gait with wearable sensors in Parkinson's disease. Neurol Sci 2024; 45:431-453. [PMID: 37843692 DOI: 10.1007/s10072-023-07017-y] [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: 02/11/2023] [Accepted: 08/06/2023] [Indexed: 10/17/2023]
Abstract
Freezing of gait (FoG) is one of the most distressing symptoms of Parkinson's Disease (PD), commonly occurring in patients at middle and late stages of the disease. Automatic and accurate FoG detection and prediction have emerged as a promising tool for long-term monitoring of PD and implementation of gait assistance systems. This paper reviews the recent development of FoG detection and prediction using wearable sensors, with attention on identifying knowledge gaps that need to be filled in future research. This review searched the PubMed and Web of Science databases to collect studies that detect or predict FoG with wearable sensors. After screening, 89 of 270 articles were included. The data description, extracted features, detection/prediction methods, and classification performance were extracted from the articles. As the number of papers of this area is increasing, the performance has been steadily improved. However, small datasets and inconsistent evaluation processes still hinder the application of FoG detection and prediction with wearable sensors in clinical practice.
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Affiliation(s)
- Wei Zhang
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
- Jiangsu Key Laboratory of Brain Disease Bioinformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Hong Sun
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, 100053, China
- National Clinical Research Center of Geriatric Disorders, Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, 100053, China
| | - Debin Huang
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Zixuan Zhang
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Jinyu Li
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Chan Wu
- Dongzhimen Hospital, Beijing University of Traditional Chinese Medicine, Beijing, 100029, China
| | - Yingying Sun
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Mengyi Gong
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Zhi Wang
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Chao Sun
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Guiyun Cui
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China.
| | - Yuzhu Guo
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.
| | - Piu Chan
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, 100053, China.
- National Clinical Research Center of Geriatric Disorders, Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, 100053, China.
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Guo Y, Yang J, Liu Y, Chen X, Yang GZ. Detection and assessment of Parkinson's disease based on gait analysis: A survey. Front Aging Neurosci 2022; 14:916971. [PMID: 35992585 PMCID: PMC9382193 DOI: 10.3389/fnagi.2022.916971] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.
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Affiliation(s)
- Yao Guo
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jianxin Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuxuan Liu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
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Bernardes RA, Ventura F, Neves H, Fernandes MI, Sousa P. Wearable Walking Assistant for Freezing of Gait With Environmental IoT Monitoring: A Contribution to the Discussion. Front Public Health 2022; 10:861621. [PMID: 35795702 PMCID: PMC9251205 DOI: 10.3389/fpubh.2022.861621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/30/2022] [Indexed: 01/14/2023] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease, significantly increasing in the last three decades. Worldwide, seven to ten million people are affected by PD. In people living with PD, freezing of gait (FoG) significantly impacts activities of daily living, potentially leading to falls, injuries, and loss of autonomy. FoG prevalence rates vary widely, reaching at least 50% of patients with PD. Current therapeutic options have limited effectiveness, and their complement with innovative technology-based solutions in the real world is demanded to enhance daily functioning for people living with PD. This article provides a narrative review of current technological developments for people living with PD and, derived from that evidence, presents a perspective on integrating wearable technology and IoT to support telemonitoring and self-management of people living with PD in their daily living environment. Complementing current therapeutic options with technology-based solutions in PD patients' real-world environment is crucial to enhancing the quality of life of people living with PD. In that way, wearable technology and IoT might constitute resources of excellence in seamless monitoring and self-management in people's home environments.
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Salchow-Hömmen C, Skrobot M, Jochner MCE, Schauer T, Kühn AA, Wenger N. Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders. Front Hum Neurosci 2022; 16:768575. [PMID: 35185496 PMCID: PMC8850274 DOI: 10.3389/fnhum.2022.768575] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/07/2022] [Indexed: 01/29/2023] Open
Abstract
The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.
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Affiliation(s)
- Christina Salchow-Hömmen
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Matej Skrobot
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Magdalena C E Jochner
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Schauer
- Control Systems Group, Technische Universität Berlin, Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Charité-Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure Clinical Research Centre, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases, DZNE, Berlin, Germany
| | - Nikolaus Wenger
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
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Morgan C, Tonkin EL, Craddock I, Whone AL. Acceptability of an In-Home Multimodal Sensor Platform in Parkinson’s Disease: A Qualitative Study (Preprint). JMIR Hum Factors 2022; 9:e36370. [PMID: 35797101 PMCID: PMC9305404 DOI: 10.2196/36370] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/07/2022] [Accepted: 05/23/2022] [Indexed: 12/28/2022] Open
Abstract
Background Parkinson disease (PD) symptoms are complex, gradually progressive, and fluctuate hour by hour. Home-based technological sensors are being investigated to measure symptoms and track disease progression. A smart home sensor platform, with cameras and wearable devices, could be a useful tool to use to get a fuller picture of what someone’s symptoms are like. High-resolution video can capture the ground truth of symptoms and activities. There is a paucity of information about the acceptability of such sensors in PD. Objective The primary objective of our study was to explore the acceptability of living with a multimodal sensor platform in a naturalistic setting in PD. Two subobjectives are to identify any suggested limitations and to explore the sensors’ impact on participant behaviors. Methods A qualitative study was conducted with an inductive approach using semistructured interviews with a cohort of PD and control participants who lived freely for several days in a home-like environment while continuously being sensed. Results This study of 24 participants (12 with PD) found that it is broadly acceptable to use multimodal sensors including wrist-worn wearables, cameras, and other ambient sensors passively in free-living in PD. The sensor that was found to be the least acceptable was the wearable device. Suggested limitations on the platform for home deployment included camera-free time and space. Behavior changes were noted by the study participants, which may have related to being passively sensed. Recording high-resolution video in the home setting for limited periods of time was felt to be acceptable to all participants. Conclusions The results broaden the knowledge of what types of sensors are acceptable for use in research in PD and what potential limitations on these sensors should be considered in future work. The participants’ reported behavior change in this study should inform future similar research design to take this factor into account. Collaborative research study design, involving people living with PD at every stage, is important to ensure that the technology is acceptable and that the data outcomes produced are ecologically valid and accurate. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2020-041303
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Affiliation(s)
- Catherine Morgan
- Translational Health Sciences, University of Bristol Medical School, Bristol, United Kingdom
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Bristol, United Kingdom
| | - Emma L Tonkin
- School of Computer Science, Electrical and Electronic Engineering, University of Bristol, Bristol, United Kingdom
| | - Ian Craddock
- School of Computer Science, Electrical and Electronic Engineering, University of Bristol, Bristol, United Kingdom
| | - Alan L Whone
- Translational Health Sciences, University of Bristol Medical School, Bristol, United Kingdom
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Bristol, United Kingdom
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Zoetewei D, Herman T, Brozgol M, Ginis P, Thumm PC, Ceulemans E, Decaluwé E, Palmerini L, Ferrari A, Nieuwboer A, Hausdorff JM. Protocol for the DeFOG trial: A randomized controlled trial on the effects of smartphone-based, on-demand cueing for freezing of gait in Parkinson's disease. Contemp Clin Trials Commun 2021; 24:100817. [PMID: 34816053 PMCID: PMC8591418 DOI: 10.1016/j.conctc.2021.100817] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 06/22/2021] [Accepted: 06/27/2021] [Indexed: 12/16/2022] Open
Abstract
Background Freezing of gait (FOG) is a highly incapacitating symptom that affects many people with Parkinson's disease (PD). Cueing triggered upon real-time FOG detection (on-demand cueing) shows promise for FOG treatment. Yet, the feasibility of implementation and efficacy in daily life is still unknown. Therefore, this study aims to investigate the effectiveness of DeFOG: a smartphone and sensor-based on-demand cueing solution for FOG. Methods Sixty-two PD patients with FOG will be recruited for this single-blind, multi-center, randomized controlled phase II trial. Patients will be randomized into either the intervention group or the active control group. For four weeks, both groups will receive feedback about their physical activity using the wearable DeFOG system in daily life. In addition, the intervention group will also receive on-demand auditory cueing and instructions. Before and after the intervention, home-based assessments will be performed to evaluate the primary outcome, i.e., “percentage time frozen” during a FOG-provoking protocol. Secondary outcomes include the training effects on physical activity monitored over 7 days and the user-friendliness of the technology. Discussion The DeFOG trial will investigate the effectiveness of personalized on-demand cueing in a controlled design, delivered for 4 weeks in the patient's home environment. We anticipate that DeFOG will reduce FOG to a greater degree than in the control group and we will explore the impact of the intervention on physical activity levels. We expect to gain in-depth insight into whether and how patients control FOG using cueing methods in their daily lives. Trial registration Clinicaltrials.gov NCT03978507.
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Affiliation(s)
- Demi Zoetewei
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Talia Herman
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Marina Brozgol
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Pieter Ginis
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Pablo Cornejo Thumm
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Eva Ceulemans
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Eva Decaluwé
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Luca Palmerini
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136, Bologna, Italy.,Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, 40126, Bologna, Italy
| | - Alberto Ferrari
- Department of Engineering "Enzo Ferrari" University of Modena and Reggio Emilia, Modena, Italy.,Science & Technology Park for Medicine, TPM, Democenter Foundation, Mirandola, Modena, Italy
| | - Alice Nieuwboer
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Israel.,Department of Physical Therapy, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Rush Alzheimer's Disease Center and Department of Orthopedic Surgery, Rush University, Chicago, IL, USA
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Abou L, Peters J, Wong E, Akers R, Dossou MS, Sosnoff JJ, Rice LA. Gait and Balance Assessments using Smartphone Applications in Parkinson's Disease: A Systematic Review. J Med Syst 2021; 45:87. [PMID: 34392429 PMCID: PMC8364438 DOI: 10.1007/s10916-021-01760-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 08/04/2021] [Indexed: 01/21/2023]
Abstract
Gait dysfunctions and balance impairments are key fall risk factors and associated with reduced quality of life in individuals with Parkinson's Disease (PD). Smartphone-based assessments show potential to increase remote monitoring of the disease. This review aimed to summarize the validity, reliability, and discriminative abilities of smartphone applications to assess gait, balance, and falls in PD. Two independent reviewers screened articles systematically identified through PubMed, Web of Science, Scopus, CINAHL, and SportDiscuss. Studies that used smartphone-based gait, balance, or fall applications in PD were retrieved. The validity, reliability, and discriminative abilities of the smartphone applications were summarized and qualitatively discussed. Methodological quality appraisal of the studies was performed using the quality assessment tool for observational cohort and cross-sectional studies. Thirty-one articles were included in this review. The studies present mostly with low risk of bias. In total, 52% of the studies reported validity, 22% reported reliability, and 55% reported discriminative abilities of smartphone applications to evaluate gait, balance, and falls in PD. Those studies reported strong validity, good to excellent reliability, and good discriminative properties of smartphone applications. Only 19% of the studies formally evaluated the usability of their smartphone applications. The current evidence supports the use of smartphone to assess gait and balance, and detect freezing of gait in PD. More studies are needed to explore the use of smartphone to predict falls in this population. Further studies are also warranted to evaluate the usability of smartphone applications to improve remote monitoring in this population.Registration: PROSPERO CRD 42020198510.
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Affiliation(s)
- Libak Abou
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Joseph Peters
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ellyce Wong
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Rebecca Akers
- Department of Rehabilitation Science, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Mauricette Sènan Dossou
- Centre National Hospitalier et Universitaire de Pneumo-Phtisiologie, Cotonou, Littoral, Benin
| | - Jacob J Sosnoff
- Department of Physical Therapy and Rehabilitation Science, School of Health Professions, Medical Center, University of Kansas, Kansas City, KS, USA
| | - Laura A Rice
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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10
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Long short term memory based patient-dependent model for FOG detection in Parkinson's disease. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.11.036] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Sweeney D, Quinlan LR, Richardson M, Meskell P, ÓLaighin G. Double-Tap Interaction as an Actuation Mechanism for On-Demand Cueing in Parkinson's Disease. SENSORS 2019; 19:s19235167. [PMID: 31779099 PMCID: PMC6928615 DOI: 10.3390/s19235167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 11/15/2019] [Accepted: 11/22/2019] [Indexed: 11/16/2022]
Abstract
Freezing of Gait (FoG) is one of the most debilitating symptoms of Parkinson’s disease and is an important contributor to falls. When the management of freezing episodes cannot be achieved through medication or surgery, non-pharmacological methods, such as cueing, have emerged as effective techniques, which ameliorates FoG. The use of On-Demand cueing systems (systems that only provide cueing stimuli during a FoG episode) has received attention in recent years. For such systems, the most common method of triggering the onset of cueing stimuli, utilize autonomous real-time FoG detection algorithms. In this article, we assessed the potential of a simple double-tap gesture interaction to trigger the onset of cueing stimuli. The intended purpose of our study was to validate the use of double-tap gesture interaction to facilitate Self-activated On-Demand cueing. We present analyses that assess if PwP can perform a double-tap gesture, if the gesture can be detected using an accelerometer’s embedded gestural interaction recognition function and if the action of performing the gesture aggravates FoG episodes. Our results demonstrate that a double-tap gesture may provide an effective actuation method for triggering On-Demand cueing. This opens up the potential future development of self-activated cueing devices as a method of On-Demand cueing for PwP and others.
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Affiliation(s)
- Dean Sweeney
- Electrical & Electronic Engineering, School of Engineering, NUI Galway, University Road, H91 HX31 Galway, Ireland; (D.S.); (G.Ó.)
- Human Movement Laboratory, CÚRAM Centre for Research in Medical Devices, NUI Galway, University Road, H91 HX31 Galway, Ireland
| | - Leo R. Quinlan
- Human Movement Laboratory, CÚRAM Centre for Research in Medical Devices, NUI Galway, University Road, H91 HX31 Galway, Ireland
- Physiology, School of Medicine, NUI Galway, University Road, H91 W5P7 Galway, Ireland
- Correspondence:
| | - Margaret Richardson
- Neurology Department University Hospital Limerick, Dooradoyle, V94 F858 Limerick, Ireland;
| | - Pauline Meskell
- Department of Nursing and Midwifery, University of Limerick, Castletroy, V94 X5K6 Limerick, Ireland;
| | - Gearóid ÓLaighin
- Electrical & Electronic Engineering, School of Engineering, NUI Galway, University Road, H91 HX31 Galway, Ireland; (D.S.); (G.Ó.)
- Human Movement Laboratory, CÚRAM Centre for Research in Medical Devices, NUI Galway, University Road, H91 HX31 Galway, Ireland
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Devlin K, Alshaikh JT, Pantelyat A. Music Therapy and Music-Based Interventions for Movement Disorders. Curr Neurol Neurosci Rep 2019; 19:83. [PMID: 31720865 DOI: 10.1007/s11910-019-1005-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
PURPOSE OF REVIEW There is emerging evidence that music therapy and other methods using music and rhythm may meaningfully improve a broad range of symptoms in neurological and non-neurological disorders. This review highlights the findings of recent studies utilizing music and rhythm-based interventions for gait impairment, other motor symptoms, and non-motor symptoms in Parkinson disease (PD) and other movement disorders. Limitations of current studies as well as future research directions are discussed. RECENT FINDINGS Multiple studies have demonstrated short-term benefits of rhythmic auditory stimulation on gait parameters including gait freezing in PD, with recent studies indicating that it may reduce falls. Demonstration of benefits for gait in both dopaminergic "on" and "off" states suggests that this intervention can be a valuable addition to the current armamentarium of PD therapies. There is also emerging evidence of motor and non-motor benefits from group dancing, singing, and instrumental music performance in PD. Preliminary evidence for music therapy and music-based interventions in movement disorders other than PD (such as Huntington disease, Tourette syndrome, and progressive supranuclear palsy) is limited but promising. Music therapy and other music and rhythm-based interventions may offer a range of symptomatic benefits to patients with PD and other movement disorders. Studies investigating the potential mechanisms of music's effects and well-controlled multicenter trials of these interventions are urgently needed.
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Affiliation(s)
- Kerry Devlin
- Peabody Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Jumana T Alshaikh
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alexander Pantelyat
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Moral-Munoz JA, Zhang W, Cobo MJ, Herrera-Viedma E, Kaber DB. Smartphone-based systems for physical rehabilitation applications: A systematic review. Assist Technol 2019; 33:223-236. [DOI: 10.1080/10400435.2019.1611676] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Affiliation(s)
- Jose A. Moral-Munoz
- Dept. of Nursing and Physiotherapy, University of Cadiz, Cadiz, Spain
- Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz (INiBICA), University of Cádiz, Cádiz, Spain
| | - Wenjuan Zhang
- Dept. of Industrial & Systems Engineering, North Carolina State University, Raleigh, North Carolina, USA
| | - Manuel J. Cobo
- Dept. of Computer Science and Engineering, University of Cadiz, Cadiz, Spain
| | - Enrique Herrera-Viedma
- Dept. of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
| | - David B. Kaber
- Dept. of Industrial & Systems Engineering, North Carolina State University, Raleigh, North Carolina, USA
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14
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Software and Hardware Requirements and Trade-Offs in Operating Systems for Wearables: A Tool to Improve Devices' Performance. SENSORS 2019; 19:s19081904. [PMID: 31013637 PMCID: PMC6514583 DOI: 10.3390/s19081904] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 04/08/2019] [Accepted: 04/18/2019] [Indexed: 11/17/2022]
Abstract
Wearable device requirements currently vary from soft to hard real-time constraints. Frequently, hardware improvements are a way to speed-up the global performance of a solution. However, changing some parts or the whole hardware may increase device complexity, raising the costs and leading to development delays of products or research prototypes. This paper focuses on software improvements, presenting a tool designed to create different versions of operating systems (OSs) fitting the specifications of wearable devices projects. Authors have developed a software tool allowing the end-user to craft a new OS in just a few steps. In order to validate the generated OS, an original wearable prototype for mining environments is outlined. Resulting data presented here allows for measuring the actual impact an OS has in different variables of a solution. Finally, the analysis also allows for evaluating the performance impact associated with each hardware part. Results suggest the viability of using the proposed solution when searching for performance improvements on wearables.
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15
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Sweeney D, Quinlan LR, Browne P, Richardson M, Meskell P, ÓLaighin G. A Technological Review of Wearable Cueing Devices Addressing Freezing of Gait in Parkinson's Disease. SENSORS 2019; 19:s19061277. [PMID: 30871253 PMCID: PMC6470562 DOI: 10.3390/s19061277] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 03/01/2019] [Accepted: 03/03/2019] [Indexed: 11/16/2022]
Abstract
Freezing of gait is one of the most debilitating symptoms of Parkinson’s disease and is an important contributor to falls, leading to it being a major cause of hospitalization and nursing home admissions. When the management of freezing episodes cannot be achieved through medication or surgery, non-pharmacological methods such as cueing have received attention in recent years. Novel cueing systems were developed over the last decade and have been evaluated predominantly in laboratory settings. However, to provide benefit to people with Parkinson’s and improve their quality of life, these systems must have the potential to be used at home as a self-administer intervention. This paper aims to provide a technological review of the literature related to wearable cueing systems and it focuses on current auditory, visual and somatosensory cueing systems, which may provide a suitable intervention for use in home-based environments. The paper describes the technical operation and effectiveness of the different cueing systems in overcoming freezing of gait. The “What Works Clearinghouse (WWC)” tool was used to assess the quality of each study described. The paper findings should prove instructive for further researchers looking to enhance the effectiveness of future cueing systems.
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Affiliation(s)
- Dean Sweeney
- Electrical & Electronic Engineering, School of Engineering and Informatics, NUI Galway, University Road, H91 TK33 Galway, Ireland.
- Human Movement Laboratory, CÚRAM Centre for Research in Medical Devices, NUI Galway, University Road, H91 TK33 Galway, Ireland.
| | - Leo R Quinlan
- Human Movement Laboratory, CÚRAM Centre for Research in Medical Devices, NUI Galway, University Road, H91 TK33 Galway, Ireland.
- Physiology, School of Medicine, NUI Galway, University Road, H91 TK33 Galway, Ireland.
| | - Patrick Browne
- Neurology Department, University Hospital Galway, H91 YR71 Galway, Ireland.
- School of Nursing and Midwifery, NUI Galway, University Road, H91 TK33 Galway, Ireland.
- School of Medicine, NUI Galway, University Road, H91 TK33 Galway, Ireland.
| | - Margaret Richardson
- Neurology Department University Hospital Limerick, Dooradoyle, V94 F858 Limerick, Ireland.
| | - Pauline Meskell
- Department of Nursing and Midwifery University of Limerick, Castletroy, V94 T9PX Limerick, Ireland.
| | - Gearóid ÓLaighin
- Electrical & Electronic Engineering, School of Engineering and Informatics, NUI Galway, University Road, H91 TK33 Galway, Ireland.
- Human Movement Laboratory, CÚRAM Centre for Research in Medical Devices, NUI Galway, University Road, H91 TK33 Galway, Ireland.
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Ashour AS, El-Attar A, Dey N, El-Naby MMA, El-Kader HA. Patient-dependent Freezing of Gait Detection using Signals from Multi-accelerometer Sensors in Parkinson’s Disease. 2018 9TH CAIRO INTERNATIONAL BIOMEDICAL ENGINEERING CONFERENCE (CIBEC) 2018. [DOI: 10.1109/cibec.2018.8641809] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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El-Attar A, Ashour AS, Dey N, Abdelkader H, Abd El-Naby MM, Sherratt RS. Discrete wavelet transform-based freezing of gait detection in Parkinson’s disease. J EXP THEOR ARTIF IN 2018. [DOI: 10.1080/0952813x.2018.1519000] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Amira El-Attar
- Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt
| | - Amira S. Ashour
- Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt
| | - Nilanjan Dey
- Department of Information Technology, Techno India College of Technology, Kolkata, India
| | - Hatem Abdelkader
- Department of Information systems, Faculty of Computers and Information, Minufiya University, Menoufiya, Egypt
| | - Mostafa M. Abd El-Naby
- Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt
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Rovini E, Maremmani C, Cavallo F. Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review. Telemed J E Health 2018; 25:167-183. [PMID: 29969384 DOI: 10.1089/tmj.2018.0035] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Parkinson's disease is a common neurodegenerative pathology that significantly influences quality of life (QoL) of people affected. The increasing interest and development in telemedicine services and internet of things technologies aim to implement automated smart systems for remote assistance of patients. The wide variability of Parkinson's disease in the clinical expression, as well as in the symptom progression, seems to address the patients' care toward a personalized therapy. OBJECTIVES This review addresses automated systems based on wearable/portable devices for the remote treatment and management of Parkinson's disease. The idea is to obtain an overview of the telehealth and automated systems currently developed to address the impairments due to the pathology to allow clinicians to improve the quality of care for Parkinson's disease with benefits for patients in QoL. DATA SOURCES The research was conducted within three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between January 2008 and September 2017. STUDY ELIGIBILITY CRITERIA Accurate exclusion criteria and selection strategy were applied to screen the 173 articles found. RESULTS Ultimately, 55 articles were fully evaluated and included in this review. Divided into three categories, they were automated systems actually tested at home, implemented mobile applications for Parkinson's disease assessment, or described a telehealth system architecture. CONCLUSION This review would provide an exhaustive overview of wearable systems for the remote management and automated assessment of Parkinson's disease, taking into account the reliability and acceptability of the implemented technologies.
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Affiliation(s)
- Erika Rovini
- 1 The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera (PI), Italy
| | - Carlo Maremmani
- 2 U.O. Neurologia, Ospedale delle Apuane (AUSL Toscana Nord Ovest), Massa (MS), Italy
| | - Filippo Cavallo
- 1 The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera (PI), Italy
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Ahn D, Chung H, Lee HW, Kang K, Ko PW, Kim NS, Park T. Smart Gait-Aid Glasses for Parkinson's Disease Patients. IEEE Trans Biomed Eng 2017; 64:2394-2402. [PMID: 28113199 DOI: 10.1109/tbme.2017.2655344] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Parkinson's disease (PD) is a chronic progressive disease caused by loss of dopaminergic neurons in the substantia nigra, degenerating the nervous system of a patient over time. Freezing of gait (FOG), which is a form of akinesia, is a symptom of PD. Meanwhile, recent studies show that the gait of PD patients experiencing FOG can be significantly improved by providing the regular visual or auditory patterns for the patients. In this paper, we propose a gait-aid system built upon smart glasses. Our system continuously monitors the gait and so on of a PD patient to detect FOG, and upon detection of FOG it projects visual patterns on the glasses as if the patterns were actually on the floor. Conducting experiments involving ten PD patients, we demonstrate that our system achieves the accuracy of 92.86 % in detecting FOG episodes and that it improves the gait speed and stride length of PD patients by 15.3 ∼ 37.2% and 18.7 ∼ 31.7%, respectively.
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20
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Ghamari M, Janko B, Sherratt RS, Harwin W, Piechockic R, Soltanpur C. A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments. SENSORS (BASEL, SWITZERLAND) 2016; 16:E831. [PMID: 27338377 PMCID: PMC4934257 DOI: 10.3390/s16060831] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Revised: 05/23/2016] [Accepted: 06/02/2016] [Indexed: 01/28/2023]
Abstract
Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments.
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Affiliation(s)
- Mohammad Ghamari
- Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX 79968, USA.
| | - Balazs Janko
- Department to Biomedical Engineering, University of Reading, Reading RG6 6AY, UK.
| | - R Simon Sherratt
- Department to Biomedical Engineering, University of Reading, Reading RG6 6AY, UK.
| | - William Harwin
- Department to Biomedical Engineering, University of Reading, Reading RG6 6AY, UK.
| | - Robert Piechockic
- School of Electrical and Electronic Engineering, Bristol University, Bristol BS8 1UB, UK.
| | - Cinna Soltanpur
- Department of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.
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Ellis RJ, Ng YS, Zhu S, Tan DM, Anderson B, Schlaug G, Wang Y. A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson's Disease. PLoS One 2015; 10:e0141694. [PMID: 26517720 PMCID: PMC4627774 DOI: 10.1371/journal.pone.0141694] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2015] [Accepted: 10/11/2015] [Indexed: 11/18/2022] Open
Abstract
Background A well-established connection exists between increased gait variability and greater fall likelihood in Parkinson’s disease (PD); however, a portable, validated means of quantifying gait variability (and testing the efficacy of any intervention) remains lacking. Furthermore, although rhythmic auditory cueing continues to receive attention as a promising gait therapy for PD, its widespread delivery remains bottlenecked. The present paper describes a smartphone-based mobile application (“SmartMOVE”) to address both needs. Methods The accuracy of smartphone-based gait analysis (utilizing the smartphone’s built-in tri-axial accelerometer and gyroscope to calculate successive step times and step lengths) was validated against two heel contact–based measurement devices: heel-mounted footswitch sensors (to capture step times) and an instrumented pressure sensor mat (to capture step lengths). 12 PD patients and 12 age-matched healthy controls walked along a 26-m path during self-paced and metronome-cued conditions, with all three devices recording simultaneously. Results Four outcome measures of gait and gait variability were calculated. Mixed-factorial analysis of variance revealed several instances in which between-group differences (e.g., increased gait variability in PD patients relative to healthy controls) yielded medium-to-large effect sizes (eta-squared values), and cueing-mediated changes (e.g., decreased gait variability when PD patients walked with auditory cues) yielded small-to-medium effect sizes—while at the same time, device-related measurement error yielded small-to-negligible effect sizes. Conclusion These findings highlight specific opportunities for smartphone-based gait analysis to serve as an alternative to conventional gait analysis methods (e.g., footswitch systems or sensor-embedded walkways), particularly when those methods are cost-prohibitive, cumbersome, or inconvenient.
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Affiliation(s)
- Robert J. Ellis
- School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Singapore
| | - Yee Sien Ng
- Department of Rehabilitation Medicine, Singapore General Hospital, Outram Rd, Singapore, 169608, Singapore
| | - Shenggao Zhu
- NUS Graduate School for Integrative Sciences and Engineering, 28 Medical Drive, Singapore, 117456, Singapore
| | - Dawn M. Tan
- Department of Rehabilitation Medicine, Singapore General Hospital, Outram Rd, Singapore, 169608, Singapore
| | - Boyd Anderson
- School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Singapore
| | - Gottfried Schlaug
- Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Palmer 127, Boston, MA, 02215, United States of America
| | - Ye Wang
- School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, 28 Medical Drive, Singapore, 117456, Singapore
- * E-mail:
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