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Spooner RK, Bahners BH, Schnitzler A, Florin E. Time-resolved quantification of fine hand movements as a proxy for evaluating bradykinesia-induced motor dysfunction. Sci Rep 2024; 14:5340. [PMID: 38438484 PMCID: PMC10912452 DOI: 10.1038/s41598-024-55862-4] [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: 07/19/2023] [Accepted: 02/28/2024] [Indexed: 03/06/2024] Open
Abstract
Bradykinesia is a behavioral manifestation that contributes to functional dependencies in later life. However, the current state of bradykinesia indexing primarily relies on subjective, time-averaged categorizations of motor deficits, which often yield poor reliability. Herein, we used time-resolved analyses of accelerometer recordings during standardized movements, data-driven factor analyses, and linear mixed effects models (LMEs) to quantitatively characterize general, task- and therapy-specific indices of motor impairment in people with Parkinson's disease (PwP) currently undergoing treatment for bradykinesia. Our results demonstrate that single-trial, accelerometer-based features of finger-tapping and rotational hand movements were significantly modulated by divergent therapeutic regimens. Further, these features corresponded well to current gold standards for symptom monitoring, with more precise predictive capacities of bradykinesia-specific declines achieved when considering kinematic features from diverse movement types together, rather than in isolation. Herein, we report data-driven, sample-specific kinematic profiles of diverse movement types along a continuous spectrum of motor impairment, which importantly, preserves the temporal scale for which biomechanical fluctuations in motor deficits evolve in humans. Therefore, this approach may prove useful for tracking bradykinesia-induced motor decline in aging populations the future.
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Affiliation(s)
- Rachel K Spooner
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany.
| | - Bahne H Bahners
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany.
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2
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Sánchez-Fernández LP, Garza-Rodríguez A, Sánchez-Pérez LA, Martínez-Hernández JM. A Computer Method for Pronation-Supination Assessment in Parkinson's Disease Based on Latent Space Representations of Biomechanical Indicators. Bioengineering (Basel) 2023; 10:bioengineering10050588. [PMID: 37237657 DOI: 10.3390/bioengineering10050588] [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: 03/30/2023] [Revised: 05/11/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
One problem in the quantitative assessment of biomechanical impairments in Parkinson's disease patients is the need for scalable and adaptable computing systems. This work presents a computational method that can be used for motor evaluations of pronation-supination hand movements, as described in item 3.6 of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The presented method can quickly adapt to new expert knowledge and includes new features that use a self-supervised training approach. The work uses wearable sensors for biomechanical measurements. We tested a machine-learning model on a dataset of 228 records with 20 indicators from 57 PD patients and eight healthy control subjects. The test dataset's experimental results show that the method's precision rates for the pronation and supination classification task achieved up to 89% accuracy, and the F1-scores were higher than 88% in most categories. The scores present a root mean squared error of 0.28 when compared to expert clinician scores. The paper provides detailed results for pronation-supination hand movement evaluations using a new analysis method when compared to the other methods mentioned in the literature. Furthermore, the proposal consists of a scalable and adaptable model that includes expert knowledge and affectations not covered in the MDS-UPDRS for a more in-depth evaluation.
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Affiliation(s)
- Luis Pastor Sánchez-Fernández
- Centro de Investigación en Computación, Instituto Politécnico Nacional, Juan de Dios Bátiz Ave., México City 07738, Mexico
| | - Alejandro Garza-Rodríguez
- Centro de Investigación en Computación, Instituto Politécnico Nacional, Juan de Dios Bátiz Ave., México City 07738, Mexico
| | - Luis Alejandro Sánchez-Pérez
- Electrical and Computer Engineering Department, University of Michigan, 4901 Evergreen Rd, Dearborn, MI 48128, USA
| | - Juan Manuel Martínez-Hernández
- Instituto Politécnico Nacional, Escuela Nacional de Medicina y Homeopatía, Guillermo Massieu 239, México City 07320, Mexico
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Vanmechelen I, Haberfehlner H, De Vleeschhauwer J, Van Wonterghem E, Feys H, Desloovere K, Aerts JM, Monbaliu E. Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review. Front Robot AI 2023; 9:1068413. [PMID: 36714804 PMCID: PMC9879015 DOI: 10.3389/frobt.2022.1068413] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/30/2022] [Indexed: 01/10/2023] Open
Abstract
Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinson's Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson's Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.
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Affiliation(s)
- Inti Vanmechelen
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium,*Correspondence: Inti Vanmechelen,
| | - Helga Haberfehlner
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium,Amsterdam Movement Sciences, Amsterdam UMC, Department of Rehabilitation Medicine, Amsterdam, Netherlands
| | - Joni De Vleeschhauwer
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium
| | - Ellen Van Wonterghem
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium
| | - Hilde Feys
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium
| | - Kaat Desloovere
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Pellenberg, Belgium
| | - Jean-Marie Aerts
- Division of Animal and Human Health Engineering, KU Leuven, Department of Biosystems, Measure, Model and Manage Bioresponses (M3-BIORES), Leuven, Belgium
| | - Elegast Monbaliu
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium
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4
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A new scheme for the development of IMU-based activity recognition systems for telerehabilitation. Med Eng Phys 2022; 108:103876. [DOI: 10.1016/j.medengphy.2022.103876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 08/10/2022] [Accepted: 08/21/2022] [Indexed: 11/24/2022]
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5
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Lipsmeier F, Taylor KI, Postuma RB, Volkova-Volkmar E, Kilchenmann T, Mollenhauer B, Bamdadian A, Popp WL, Cheng WY, Zhang YP, Wolf D, Schjodt-Eriksen J, Boulay A, Svoboda H, Zago W, Pagano G, Lindemann M. Reliability and validity of the Roche PD Mobile Application for remote monitoring of early Parkinson's disease. Sci Rep 2022; 12:12081. [PMID: 35840753 PMCID: PMC9287320 DOI: 10.1038/s41598-022-15874-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 06/30/2022] [Indexed: 11/19/2022] Open
Abstract
Digital health technologies enable remote and therefore frequent measurement of motor signs, potentially providing reliable and valid estimates of motor sign severity and progression in Parkinson’s disease (PD). The Roche PD Mobile Application v2 was developed to measure bradykinesia, bradyphrenia and speech, tremor, gait and balance. It comprises 10 smartphone active tests (with ½ tests administered daily), as well as daily passive monitoring via a smartphone and smartwatch. It was studied in 316 early-stage PD participants who performed daily active tests at home then carried a smartphone and wore a smartwatch throughout the day for passive monitoring (study NCT03100149). Here, we report baseline data. Adherence was excellent (96.29%). All pre-specified sensor features exhibited good-to-excellent test–retest reliability (median intraclass correlation coefficient = 0.9), and correlated with corresponding Movement Disorder Society–Unified Parkinson's Disease Rating Scale items (rho: 0.12–0.71). These findings demonstrate the preliminary reliability and validity of remote at-home quantification of motor sign severity with the Roche PD Mobile Application v2 in individuals with early PD.
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Affiliation(s)
- Florian Lipsmeier
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Kirsten I Taylor
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Ronald B Postuma
- Department of Neurology, McGill University, Montreal General Hospital, Montreal, QC, Canada
| | - Ekaterina Volkova-Volkmar
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Timothy Kilchenmann
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Brit Mollenhauer
- Paracelsus-Elena-Klinik, Kassel, Germany.,Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Atieh Bamdadian
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Werner L Popp
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Wei-Yi Cheng
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Yan-Ping Zhang
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Detlef Wolf
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Jens Schjodt-Eriksen
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Anne Boulay
- Idorsia Pharmaceuticals Ltd, Allschwil, Switzerland
| | - Hanno Svoboda
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Wagner Zago
- Prothena Biosciences Inc, South San Francisco, CA, USA
| | - Gennaro Pagano
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Michael Lindemann
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
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Garza-Rodríguez A, Sánchez-Fernández LP, Sánchez-Pérez LA, Carbajal Hernández JJ. Fuzzy inference model based on triaxial signals for pronation and supination assessment in Parkinson's disease patients. Artif Intell Med 2020; 105:101873. [PMID: 32505417 DOI: 10.1016/j.artmed.2020.101873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 03/17/2020] [Accepted: 05/02/2020] [Indexed: 11/27/2022]
Abstract
Nowadays, the Unified Parkinson Disease Rating Scale supported by the Movement Disorder Society (MDS-UPDRS), is a standardized and widely accepted instrument to rate Parkinson's disease (PD). This work presents a thorough analysis of item 3.6 of the MDS-UPDRS scale which corresponds to the pronation and supination hand movements. The motivation for this work lies in the objective quantification of motor affectations not covered by the MDS-UPDRS scale such as unsteady oscillations and velocity decrements during the motor exploration. Overall, 12 different bio-mechanical features were quantified based on measurements performed by inertial measurement units (IMUs). After a feature selection process, the selected bio-mechanical features were used as inputs for a fuzzy inference model that predicts the stage of development of the disease in each patient. In addition to this model's output, the scores of three different expert examiners and the output of a fuzzy inference model which covers affectations strictly attached the MDS-UPDRS guidelines, were also considered to obtain an integrated computational model. The proposed integrated model was incorporated using the Analytic Hierarchy Process (AHP), which gives the novelty of a combined score that helps expert examiners to give a broader assessment of the disease that covers both affectations mentioned in the MDS-UPDRS guidelines and affectations not covered by it in an objective manner.
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Affiliation(s)
- Alejandro Garza-Rodríguez
- Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz Ave., 07738, México City, Mexico
| | - Luis Pastor Sánchez-Fernández
- Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz Ave., 07738, México City, Mexico.
| | - Luis Alejandro Sánchez-Pérez
- Electrical and Computer Engineering Department, University of Michigan, 4901 Evergreen Rd., Dearborn, MI, 48128, USA
| | - José Juan Carbajal Hernández
- Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz Ave., 07738, México City, Mexico
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7
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Fundarò C, Cavalieri C, Pinna GD, Giardini A, Mancini F, Casale R. Upper Limb Interactive Weightless Technology-Aided Intervention and Assessment Picks Out Motor Skills Improvement in Parkinson's Disease: A Pilot Study. Front Neurol 2020; 11:40. [PMID: 32117009 PMCID: PMC7033477 DOI: 10.3389/fneur.2020.00040] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 01/13/2020] [Indexed: 11/18/2022] Open
Abstract
Background: In Parkinson's disease, reaching movements are conditioned by motor planning and execution deficiency. Recently, rehabilitation, aided by high technological devices, was employed for Parkinson's disease. Objective: We aimed to (1) investigate the changes in the upper limb motor performances in a sample of a patient with Parkinson's disease after a weightless training, with a passive exoskeleton, in an augmented-feedback environment; (2) highlight differences by motor parameters (performance, speed, and movement accuracy) and by type of movement (simple or complex); and (3) evaluate movement improvements by UPDRS II–III. Methods: Observational pilot study. Twenty right-handed patients with Parkinson's disease, Hohen and Yahr 2, Mini Mental State Examination ≥24 were evaluated. All patients underwent 5 day/week sessions for 4 weeks, 30 min for each arm; the training was performed with 12 exercises (single and multi-joints, horizontal and vertical movements). All the patients were assessed by UPDRS II–III and the evaluation tests provided by the device's software: a simple movement, the vertical capture, and a complex movement, the horizontal capture. For each test, we analyzed reached target percentage, movement execution time, and accuracy. Results: After training, a significant improvement of accuracy and speed for simple movement on the dominant arm, of reached targets and speed for complex movement on both sides were shown. UPDRS II and III improved significantly after training. Conclusions: In our study, a motor training aided by a high technological device improves motor parameters and highlights differences between the type of movement (simple or complex) and movement parameters (speed and accuracy) in a sample of patients with Parkinson's disease.
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Affiliation(s)
- Cira Fundarò
- Neurophysiopathology Unit, Istituti Clinici Scientifici Maugeri, IRCSS, Montescano, Italy
| | - Carlo Cavalieri
- Neuromotory Rehabilitation Unit 1, Istituti Clinici Scientifici Maugeri, IRCSS, Montescano, Italy
| | - Gian Domenico Pinna
- Department of Biomedical Engineering, Istituti Clinici Scientifici Maugeri, IRCSS, Montescano, Italy
| | - Anna Giardini
- Psychology Unit, Istituti Clinici Scientifici Maugeri, IRCSS, Montescano, Italy
| | - Francesca Mancini
- U. O. Neurologia-Stroke Unit e Laboratorio di Neuroscienze, Istituto Auxologico, IRCCS, Milan, Italy
| | - Roberto Casale
- OPUSMedica PC&R, Persons, Care and Research, Piacenza, Italy
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Shahtalebi S, Atashzar SF, Samotus O, Patel RV, Jog MS, Mohammadi A. PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models. Sci Rep 2020; 10:2195. [PMID: 32042111 PMCID: PMC7010677 DOI: 10.1038/s41598-020-58912-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 01/17/2020] [Indexed: 12/04/2022] Open
Abstract
The global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including Parkinson's Disease (PD) and Essential Tremor (ET). Pathological Hand Tremor (PHT), which is considered among the most common motor symptoms of such disorders, can severely affect patients' independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation/prediction of nonstationary PHT is critical, however, the required level of accuracy has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. This paper addresses this unmet need through establishing a deep recurrent model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time PHT elimination framework, the PHTNet, by incorporating deep bidirectional recurrent neural networks. The PHTNet is developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. The PHTNet is the first intelligent systems model developed on this scale for PHT elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.
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Affiliation(s)
- Soroosh Shahtalebi
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, H3G 1M8, QC, Canada
| | - Seyed Farokh Atashzar
- Departments of Electrical and Computer Engineering, and Mechanical and Aerospace Engineering, New York University, New York, 10003, NY, USA
- NYU WIRELESS center, New York University (NYU), New York, USA
| | - Olivia Samotus
- London Movement Disorders Centre, London Health Sciences Centre, London, ON, Canada
| | - Rajni V Patel
- Department of Electrical and Computer Engineering, University of Western Ontario, London, N6A 5B9, ON, Canada
| | - Mandar S Jog
- London Movement Disorders Centre, London Health Sciences Centre, London, ON, Canada
| | - Arash Mohammadi
- Concordia Institute for Information Systems Engineering, Concordia University, Montreal, H3G 1M8, QC, Canada.
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Di Lazzaro G, Ricci M, Al-Wardat M, Schirinzi T, Scalise S, Giannini F, Mercuri NB, Saggio G, Pisani A. Technology-Based Objective Measures Detect Subclinical Axial Signs in Untreated, de novo Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2020; 10:113-122. [PMID: 31594252 DOI: 10.3233/jpd-191758] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Technology-based objective measures (TOMs) recently gained relevance to support clinicians in the assessment of motor function in Parkinson's disease (PD), although limited data are available in the early phases. OBJECTIVE To assess motor performances of a population of newly diagnosed, drug free PD patients using wearable inertial sensors and to compare them to healthy controls (HC) and differentiate different PD subtypes [tremor dominant (TD), postural instability gait disability (PIGD), and mixed phenotype (MP)]. METHODS We enrolled 65 subjects, 36 newly diagnosed, drug-free PD patients and 29 HCs. PD patients were clinically defined as tremor dominant, postural instability-gait difficulties or mixed phenotype. All 65 subjects performed seven MDS-UPDRS III motor tasks wearing inertial sensors: rest tremor, postural tremor, rapid alternating hand movement, foot tapping, heel-to-toe tapping, Timed-Up-and-Go test (TUG) and pull test. The most relevant motor tasks were found combining ReliefF ranking and Kruskal- Wallis feature-selection methods. We used these features, linked to the relevant motor tasks, to highlight differences between PD from HC, by means of Support Vector Machine (SVM) classifier. Furthermore, we adopted SVM to support the relevance of each motor task on the classification accuracy, excluding one task at time. RESULTS Motion analysis distinguished PD from HC with an accuracy as high as 97%, based on SVM performed with measured features from tremor and bradykinesia items, pull test and TUG. Heel-to-toe test was the most relevant, followed by TUG and Pull Test. CONCLUSIONS In this pilot study, we demonstrate that the SVM algorithm successfully distinguishes de novo drug-free PD patients from HC. Surprisingly, pull test and TUG tests provided relevant features for obtaining high SVM classification accuracy, differing from the report of the experienced examiner. The use of TOMs may improve diagnostic accuracy for these patients.
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Affiliation(s)
- Giulia Di Lazzaro
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Mariachiara Ricci
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Mohammad Al-Wardat
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Tommaso Schirinzi
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Simona Scalise
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Franco Giannini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Nicola B Mercuri
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
- Santa Lucia Foundation, IRCCS, Rome, Italy
| | - Giovanni Saggio
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Antonio Pisani
- Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
- Santa Lucia Foundation, IRCCS, Rome, Italy
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10
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Bobić V, Djurić-Jovičić M, Dragašević N, Popović MB, Kostić VS, Kvaščev G. An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors. SENSORS (BASEL, SWITZERLAND) 2019; 19:E2644. [PMID: 31212680 PMCID: PMC6603543 DOI: 10.3390/s19112644] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 05/15/2019] [Accepted: 06/04/2019] [Indexed: 01/26/2023]
Abstract
Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive and clinically important movements can be captured and objectively evaluated. In this paper, a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson's disease and atypical parkinsonism. The methodology comprises several simple and repeatable signal-processing techniques that are applied for the extraction of important movement features. The decision support system consists of simple rules designed to match universally defined criteria that are evaluated in clinical practice. The accuracy of the system is calculated based on the reference scores provided by two neurologists. The proposed expert system achieved an accuracy of 88.16% for files on which neurologists agreed with their scores. The introduced system is simple, repeatable, easy to implement, and can provide good assistance in clinical practice, providing a detailed analysis of finger-tapping performance and decision support for symptom evaluation.
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Affiliation(s)
- Vladislava Bobić
- University of Belgrade-School of Electrical Engineering, 11000 Belgrade, Serbia.
- Innovation Center, School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
| | - Milica Djurić-Jovičić
- Innovation Center, School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
| | - Nataša Dragašević
- Clinic of Neurology, School of Medicine, University of Belgrade, 11000 Belgrade, Serbia.
| | - Mirjana B Popović
- University of Belgrade-School of Electrical Engineering, 11000 Belgrade, Serbia.
- Institute for Medical Research, University of Belgrade, 11000 Belgrade, Serbia.
| | - Vladimir S Kostić
- Clinic of Neurology, School of Medicine, University of Belgrade, 11000 Belgrade, Serbia.
| | - Goran Kvaščev
- University of Belgrade-School of Electrical Engineering, 11000 Belgrade, Serbia.
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11
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Teshuva I, Hillel I, Gazit E, Giladi N, Mirelman A, Hausdorff JM. Using wearables to assess bradykinesia and rigidity in patients with Parkinson's disease: a focused, narrative review of the literature. J Neural Transm (Vienna) 2019; 126:699-710. [PMID: 31115669 DOI: 10.1007/s00702-019-02017-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 05/14/2019] [Indexed: 10/26/2022]
Abstract
The potential of using wearable technologies for the objective assessment of motor symptoms in Parkinson's disease (PD) has gained prominence recently. Nonetheless, compared to tremor and gait impairment, less emphasis has been placed on the quantification of bradykinesia and rigidity. This review aimed to consolidate the existing research on objective measurement of bradykinesia and rigidity in PD through the use of wearables, focusing on the continuous monitoring of these two symptoms in free-living environments. A search of PubMed was conducted through a combination of keyword and MeSH searches. We also searched the IEEE, Google Scholar, Embase, and Scopus databases to ensure thorough results and to minimize the chances of missing relevant studies. Papers published after the year 2000 with sample sizes greater than five were included. Studies were assessed for quality and information was extracted regarding the devices used and their location on the body, the setting and duration of the study, the "gold standard" used as a reference for validation, the metrics used, and the results of each paper. Thirty-one and eight studies met the search criteria and evaluated bradykinesia and rigidity, respectively. Several studies reported strong associations between wearable-based measures and the gold-standard references for bradykinesia, and, to a lesser extent, rigidity. Only a few, pilot studies investigated the measurement of bradykinesia and rigidity in the home and free-living settings. While the current results are promising for the future of wearables, additional work is needed on their validation and adaptation in ecological, free-living settings. Doing so has the potential to improve the assessment and treatment of motor fluctuations and symptoms of PD more generally through real-time objective monitoring of bradykinesia and rigidity.
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Affiliation(s)
- Itay Teshuva
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Inbar Hillel
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Nir Giladi
- 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, Tel Aviv, Israel.,Department of Neurology and Neurosurgery, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Anat Mirelman
- 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, Tel Aviv, Israel.,Department of Neurology and Neurosurgery, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - 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, Tel Aviv, Israel. .,Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv, Israel. .,Rush Alzheimer's Disease Center, Chicago, USA. .,Department of Orthopedic Surgery, Rush University Medical Center, Chicago, USA.
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12
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Lee WL, Sinclair NC, Jones M, Tan JL, Proud EL, Peppard R, McDermott HJ, Perera T. Objective evaluation of bradykinesia in Parkinson's disease using an inexpensive marker-less motion tracking system. Physiol Meas 2019; 40:014004. [PMID: 30650391 DOI: 10.1088/1361-6579/aafef2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Quantification of bradykinesia (slowness of movement) is crucial for the treatment and monitoring of Parkinson's disease. Subjective observational techniques are the de-facto 'gold standard', but such clinical rating scales suffer from poor sensitivity and inter-rater variability. Although various technologies have been developed for assessing bradykinesia in recent years, most still require considerable expertise and effort to operate. Here we present a novel method to utilize an inexpensive off-the-shelf hand-tracker (Leap Motion) to quantify bradykinesia. APPROACH Eight participants with Parkinson's disease receiving benefit from deep brain stimulation were recruited for the study. Participants were assessed 'on' and 'off' stimulation, with the 'on' condition repeated to evaluate reliability. Participants performed wrist pronation/supination, hand open/close, and finger-tapping tasks during each condition. Tasks were simultaneously captured by our software and rated by three clinicians. A linear regression model was developed to predict clinical scores and its performance was assessed with leave-one-subject-out cross validation. MAIN RESULTS Aggregate bradykinesia scores predicted by our method were in strong agreement (R = 0.86) with clinical scores. The model was able to differentiate therapeutic states and comparison between the test-retest conditions yielded no significant difference (p = 0.50). SIGNIFICANCE These findings demonstrate that our method can objectively quantify bradykinesia in agreement with clinical observation and provide reliable measurements over time. The hardware is readily accessible, requiring only a modest computer and our software to perform assessments, thus making it suitable for both clinic- and home-based symptom tracking.
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Affiliation(s)
- Wee Lih Lee
- Bionics Institute, East Melbourne, Victoria, Australia
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13
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Daunoraviciene K, Ziziene J, Griskevicius J, Pauk J, Ovcinikova A, Kizlaitiene R, Kaubrys G. Quantitative assessment of upper extremities motor function in multiple sclerosis. Technol Health Care 2018; 26:647-653. [PMID: 29843287 DOI: 10.3233/thc-182511] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Upper extremity (UE) motor function deficits are commonly noted in multiple sclerosis (MS) patients and assessing it is challenging because of the lack of consensus regarding its definition. Instrumented biomechanical analysis of upper extremity movements can quantify coordination with different spatiotemporal measures and facilitate disability rating in MS patients. OBJECTIVE To identify objective quantitative parameters for more accurate evaluation of UE disability and relate it to existing clinical scores. METHODS Thirty-four MS patients and 24 healthy controls (CG) performed a finger-to-nose test as fast as possible and, in addition, clinical evaluation kinematic parameters of UE were measured by using inertial sensors. RESULTS Generally, a higher disability score was associated with an increase of several temporal parameters, like slower task performance. The time taken to touch their nose was longer when the task was fulfilled with eyes closed. Time to peak angular velocity significantly changed in MS patients (EDSS > 5.0). The inter-joint coordination significantly decreases in MS patients (EDSS 3.0-5.5). Spatial parameters indicated that maximal ROM changes were in elbow flexion. CONCLUSIONS Our findings have revealed that spatiotemporal parameters are related to the UE motor function and MS disability level. Moreover, they facilitate clinical rating by supporting clinical decisions with quantitative data.
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Affiliation(s)
- Kristina Daunoraviciene
- Department of Biomechanical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania
| | - Jurgita Ziziene
- Department of Biomechanical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania
| | - Julius Griskevicius
- Department of Biomechanical Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania
| | - Jolanta Pauk
- Department of Mechanics and Applied Computer Science, Bialystok University of Technology, Bialystok, Poland
| | - Agne Ovcinikova
- Department of Neurology and Neurosurgery, Centre for Neurology, Vilnius University, Vilnius, Lithuania
| | - Rasa Kizlaitiene
- Department of Neurology and Neurosurgery, Centre for Neurology, Vilnius University, Vilnius, Lithuania
| | - Gintaras Kaubrys
- Department of Neurology and Neurosurgery, Centre for Neurology, Vilnius University, Vilnius, Lithuania
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14
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Corona F, Pilloni G, Arippa F, Porta M, Casula C, Cossu G, Pau M. Quantitative assessment of upper limb functional impairments in people with Parkinson's disease. Clin Biomech (Bristol, Avon) 2018; 57:137-143. [PMID: 29986276 DOI: 10.1016/j.clinbiomech.2018.06.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 06/26/2018] [Accepted: 06/26/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND In clinical routine, upper limb motor disorders of people with Parkinson's disease are commonly assessed using scale- or timed tests, but such tools are not fully suitable for providing detailed information about their type and magnitude. To partly overcome these limitations, the present study aims to quantitatively investigate upper limb functional impairments through quantitative analysis of the "hand-to-mouth" task. METHODS Twenty-five individuals with Parkinson's disease and 20 age-matched healthy individuals underwent a kinematic analysis of the hand-to-mouth task from which spatio-temporal and kinematic measures, including summary measures (Arm Variable Score and Arm Profile Score), were calculated and correlated with clinical scores (Hoehn & Yahr, H&Y and the Unified Parkinson Disease Rating Scale, UPDRS). FINDINGS The "hand-to-mouth" movement is significantly altered in individuals with Parkinson's disease, especially in terms of reduced velocity, reduced range of motion of elbow flexion-extension and deviation from a physiologic pattern (Arm Profile Score 12.8° vs. 10.1° of unaffected, P = 0.002). Significant moderate correlations were found between movement duration and UPDRS-III (rho = 0.478, P = 0.001) and between the Arm Profile Score and H&Y (rho = 0.481, P = 0.024) and UPDRS-III (rho = 0.326, P = 0.001). INTERPRETATION On the basis of such findings, we can state that the kinematic analysis of "hand-to-mouth" movement, and in particular the summary indexes, are suitable for easily representing upper limb movement alterations in people with Parkinson's disease, thus allowing the monitoring either of disease progression or effectiveness of pharmacologic and rehabilitative treatments.
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Affiliation(s)
- Federica Corona
- Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Cagliari, Italy.
| | - Giuseppina Pilloni
- Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Cagliari, Italy
| | - Federico Arippa
- Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Cagliari, Italy
| | - Micaela Porta
- Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Cagliari, Italy
| | - Carlo Casula
- AOB "G. Brotzu" General Hospital, Cagliari, Italy
| | | | - Massimiliano Pau
- Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Cagliari, Italy
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15
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Memar S, Delrobaei M, Pieterman M, McIsaac K, Jog M. Quantification of whole-body bradykinesia in Parkinson's disease participants using multiple inertial sensors. J Neurol Sci 2018; 387:157-165. [PMID: 29571855 DOI: 10.1016/j.jns.2018.02.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 01/10/2018] [Accepted: 02/01/2018] [Indexed: 11/29/2022]
Abstract
Bradykinesia (slowness of movement) is a common motor symptom of Parkinson's disease (PD) that can severely affect quality of life for those living with the disease. Assessment and treatment of PD motor symptoms largely depends on clinical scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). However, such clinical scales rely on the visual assessment by a human observer, naturally resulting in inter-rater variability. Although previous studies have developed objective means for measuring bradykinesia in PD patients, their evaluation was restricted by the type of movement and number of joints assessed. These studies failed to provide a more comprehensive, whole-body evaluation capable of measuring multiple joints simultaneously. This study utilizes wearable inertial measurement units (IMUs) to quantify whole-body movements, providing novel bradykinesia indices for walking (WBI) and standing up from a chair (sit-to-stand; SBI). The proposed bradykinesia indices include the joint angles at both upper and lower limbs and trunk motion to compute a complete, objective score for whole body bradykinesia. Thirty PD and 11 age-matched healthy control participants were recruited for the study. The participants performed two standard walking tasks that involved multiple body joints in the upper and lower limbs. The WBI and SBI successfully identified differences between control and PD participants. The indices also effectively identified differences within the PD population, distinguishing participants assessed with (ON) and without (OFF) levodopa; the gold-standard of treatment for PD. The goal of this study is to provide health professionals with an objective score for whole body bradykinesia by simultaneously measuring the upper and lower extremities along with truncal movement. This method demonstrates potential to be used in conjunction with current clinical standards for motor symptom assessment, and may also be promising for the remote assessment of PD patients and in cases where experienced clinicians may not be available. In conclusion, the intelligent use of this technology for the measurement of bradykinesia (among other symptoms) has vast implications for optimizing treatment in Parkinson's disease, ultimately leading to an improvement in quality of life.
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Affiliation(s)
- Sara Memar
- Robarts Research Institute, London, ON, Canada.
| | - Mehdi Delrobaei
- Center for Research and Technology (CREATECH), Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Marcus Pieterman
- Department of Clinical Neurological Sciences, Western University, London, ON, Canada
| | - Kenneth McIsaac
- Department of Electrical and Computer Engineering, Western University, London, ON, Canada
| | - Mandar Jog
- Department of Clinical Neurological Sciences, Western University, London, ON, Canada
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16
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Hasan H, Athauda DS, Foltynie T, Noyce AJ. Technologies Assessing Limb Bradykinesia in Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2017; 7:65-77. [PMID: 28222539 PMCID: PMC5302048 DOI: 10.3233/jpd-160878] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Background: The MDS-UPDRS (Movement Disorders Society – Unified Parkinson’s Disease Rating Scale) is the most widely used scale for rating impairment in PD. Subscores measuring bradykinesia have low reliability that can be subject to rater variability. Novel technological tools can be used to overcome such issues. Objective: To systematically explore and describe the available technologies for measuring limb bradykinesia in PD that were published between 2006 and 2016. Methods: A systematic literature search using PubMed (MEDLINE), IEEE Xplore, Web of Science, Scopus and Engineering Village (Compendex and Inspec) databases was performed to identify relevant technologies published until 18 October 2016. Results: 47 technologies assessing bradykinesia in PD were identified, 17 of which offered home and clinic-based assessment whilst 30 provided clinic-based assessment only. Of the eligible studies, 7 were validated in a PD patient population only, whilst 40 were tested in both PD and healthy control groups. 19 of the 47 technologies assessed bradykinesia only, whereas 28 assessed other parkinsonian features as well. 33 technologies have been described in additional PD-related studies, whereas 14 are not known to have been tested beyond the pilot phase. Conclusion: Technology based tools offer advantages including objective motor assessment and home monitoring of symptoms, and can be used to assess response to intervention in clinical trials or routine care. This review provides an up-to-date repository and synthesis of the current literature regarding technology used for assessing limb bradykinesia in PD. The review also discusses the current trends with regards to technology and discusses future directions in development.
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Affiliation(s)
- Hasan Hasan
- UCL Institute of Neurology, Queen Square, London, UK
| | - Dilan S Athauda
- UCL Institute of Neurology, Queen Square, London, UK.,Sobell Department of Motor Neuroscience and Movement Disorders, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Thomas Foltynie
- UCL Institute of Neurology, Queen Square, London, UK.,Sobell Department of Motor Neuroscience and Movement Disorders, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Alastair J Noyce
- UCL Institute of Neurology, Queen Square, London, UK.,Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University London, London, UK.,Reta Lila Weston Institute of Neurological studies, UCL Institute of Neurology, London, UK
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17
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Delrobaei M, Memar S, Pieterman M, Stratton TW, McIsaac K, Jog M. Towards remote monitoring of Parkinson's disease tremor using wearable motion capture systems. J Neurol Sci 2017; 384:38-45. [PMID: 29249375 DOI: 10.1016/j.jns.2017.11.004] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Revised: 10/10/2017] [Accepted: 11/02/2017] [Indexed: 01/13/2023]
Abstract
The management of movement disorders is shifting from a centralized-clinical assessment towards remote monitoring and individualized therapy. While a variety of treatment options are available, ranging from pharmaceutical drugs to invasive neuromodulation, the clinical effects are inconsistent and often poorly measured. For instance, the lack of remote monitoring has been a major limitation to optimize therapeutic interventions for patients with Parkinson's Disease (PD). In this work, we focus on the assessment of full-body tremor as the most recognized PD symptom. Forty PD and twenty two healthy participants were recruited. The main assessment tool was an inertial measurement unit (IMU)-based motion capture system to quantify full-body tremor and to separate tremor-dominant from non-tremor-dominant PD patients as well as from healthy controls. We developed a new measure and evaluated its clinical utility by correlating the results with the Unified Parkinson's Disease Rating Scale (UPDRS) scores as the gold standard. Significant correlation was observed between the UPDRS and the tremor severity scores for the selected tasks. The results suggest that it is feasible and clinically meaningful to utilize the suggested objective tremor score for the assessment of PD patients. Furthermore, this portable assessment tool could potentially be used in the home environment to monitor PD tremor and facilitate optimizing therapeutic interventions.
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Affiliation(s)
- Mehdi Delrobaei
- Center for Research and Technology (CREATECH), Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Sara Memar
- Lawson Health Research Institute, London, ON, Canada.
| | | | - Tyler W Stratton
- Laboratory of Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; St. Michael's Hospital, Toronto, ON, Canada.
| | - Kenneth McIsaac
- Department of Electrical and Computer Engineering, Western University, London, ON, Canada.
| | - Mandar Jog
- Lawson Health Research Institute, London, ON, Canada; Department of Clinical Neurological Sciences, Western University, London, ON, Canada.
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18
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Rovini E, Maremmani C, Cavallo F. How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review. Front Neurosci 2017; 11:555. [PMID: 29056899 PMCID: PMC5635326 DOI: 10.3389/fnins.2017.00555] [Citation(s) in RCA: 184] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 09/21/2017] [Indexed: 01/15/2023] Open
Abstract
Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. OBJECTIVES This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). DATA SOURCES The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. STUDY ELIGIBILITY CRITERIA Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. RESULTS Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.
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Affiliation(s)
- Erika Rovini
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Carlo Maremmani
- U.O. Neurologia, Ospedale delle Apuane (AUSL Toscana Nord Ovest), Massa, Italy
| | - Filippo Cavallo
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
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19
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Pan MK, Kuo SH, Tai CH, Liou JY, Pei JC, Chang CY, Wang YM, Liu WC, Wang TR, Lai WS, Kuo CC. Neuronal firing patterns outweigh circuitry oscillations in parkinsonian motor control. J Clin Invest 2016; 126:4516-4526. [PMID: 27797341 DOI: 10.1172/jci88170] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 09/22/2016] [Indexed: 11/17/2022] Open
Abstract
Neuronal oscillations at beta frequencies (20-50 Hz) in the cortico-basal ganglia circuits have long been the leading theory for bradykinesia, the slow movements that are cardinal symptoms in Parkinson's disease (PD). The beta oscillation theory helped to drive a frequency-based design in the development of deep brain stimulation therapy for PD. However, in contrast to this theory, here we have found that bradykinesia can be completely dissociated from beta oscillations in rodent models. Instead, we observed that bradykinesia is causatively regulated by the burst-firing pattern of the subthalamic nucleus (STN) in a feed-forward, or efferent-only, mechanism. Furthermore, STN burst-firing and beta oscillations are two independent mechanisms that are regulated by different NMDA receptors in STN. Our results shift the understanding of bradykinesia pathophysiology from an interactive oscillatory theory toward a feed-forward mechanism that is coded by firing patterns. This distinct mechanism may improve understanding of the fundamental concepts of motor control and enable more selective targeting of bradykinesia-specific mechanisms to improve PD therapy.
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