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Kwon DY, Kwon Y, Choi JA, Ko J, Kim JW. Quantitative Analysis of Postural Balance in Faller and Nonfaller Patients with Parkinson's Disease. PARKINSON'S DISEASE 2023; 2023:9688025. [PMID: 37383299 PMCID: PMC10299878 DOI: 10.1155/2023/9688025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/24/2023] [Accepted: 05/29/2023] [Indexed: 06/30/2023]
Abstract
Background Postural instability has been identified as a fall risk factor with a significant impact on the quality of life of patients with Parkinson's disease (PD). The aim of this study was to compare the center of pressure (COP) between faller and nonfaller patients with PD during static standing. Methods Thirty-two faller patients and 32 nonfaller patients with PD participated in this study. All patients performed the static balance test on a force plate. COP data were recorded during quiet standing. Mean distance, sway area, mean velocity, mean frequency, and peak power were derived from the COP data. Statistical analysis was performed using independent t-tests to compare faller and nonfaller patients. Results Fallers presented a greater average distance, wider sway area, faster average speed, and greater peak power than nonfallers (p < 0.05). In contrast, no significant group differences were observed in peak frequency and mean frequency (p > 0.05). Conclusions Although falls occur during dynamic activities, our study demonstrated that even a safe and simple static postural balance test could significantly differentiate between faller and nonfaller patients. Thus, these results suggest that quantitatively assessed static postural sway variables would be useful for distinguishing prospective fallers among PD patients.
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Affiliation(s)
- Do Young Kwon
- Department of Neurology, Korea University College of Medicine, Ansan-si 15355, Republic of Korea
| | - Yuri Kwon
- Department of Biomedical Engineering, Konkuk University, Chungju-si, Chungcheongbuk-do 27478, Republic of Korea
- Research Institute of Biomedical Engineering, Konkuk University, Chungju-si, Chungcheongbuk-do 27478, Republic of Korea
| | - Ji-An Choi
- Department of Biomedical Engineering, Konkuk University, Chungju-si, Chungcheongbuk-do 27478, Republic of Korea
| | - Junghyuk Ko
- Division of Mechanical Engineering, College of Engineering, Korea Maritime and Ocean University, Busan 49112, Republic of Korea
| | - Ji-Won Kim
- Department of Biomedical Engineering, Konkuk University, Chungju-si, Chungcheongbuk-do 27478, Republic of Korea
- Research Institute of Biomedical Engineering, Konkuk University, Chungju-si, Chungcheongbuk-do 27478, Republic of Korea
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Cohen M, Herman T, Ganz N, Badichi I, Gurevich T, Hausdorff JM. Multidisciplinary Intensive Rehabilitation Program for People with Parkinson's Disease: Gaps between the Clinic and Real-World Mobility. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3806. [PMID: 36900826 PMCID: PMC10001519 DOI: 10.3390/ijerph20053806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/12/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Intensive rehabilitation programs improve motor and non-motor symptoms in people with Parkinson's disease (PD), however, it is not known whether transfer to daily-living walking occurs. The effects of multidisciplinary-intensive-outpatient rehabilitation (MIOR) on gait and balance in the clinic and on everyday walking were examined. Forty-six (46) people with PD were evaluated before and after the intensive program. A 3D accelerometer placed on the lower back measured daily-living walking during the week before and after the intervention. Participants were also stratified into "responders" and "non-responders" based on daily-living-step-counts. After the intervention, gait and balance significantly improved, e.g., MiniBest scores (p < 0.001), dual-task gait speed increased (p = 0.016) and 6-minute walk distance increased (p < 0.001). Many improvements persisted after 3 months. In contrast, daily-living number of steps and gait quality features did not change in response to the intervention (p > 0.1). Only among the "responders", a significant increase in daily-living number of steps was found (p < 0.001). These findings demonstrate that in people with PD improvements in the clinic do not necessarily carry over to daily-living walking. In a select group of people with PD, it is possible to ameliorate daily-living walking quality, potentially also reducing fall risk. Nevertheless, we speculate that self-management in people with PD is relatively poor; therefore, to maintain health and everyday walking abilities, actions such as long-term engaging in physical activity and preserving mobility may be needed.
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Affiliation(s)
- Moriya Cohen
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Ezra Lemarpeh Center, Bnei Brak 5111501, Israel
| | - Talia Herman
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
| | - Natalie Ganz
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
| | | | - Tanya Gurevich
- Movement Disorders Unit, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Neurology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
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Ju F, Wang Y, Xie B, Mi Y, Zhao M, Cao J. The Use of Sports Rehabilitation Robotics to Assist in the Recovery of Physical Abilities in Elderly Patients with Degenerative Diseases: A Literature Review. Healthcare (Basel) 2023; 11:healthcare11030326. [PMID: 36766901 PMCID: PMC9914201 DOI: 10.3390/healthcare11030326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
The increase in the number of elderly patients with degenerative diseases has brought additional medical and financial pressures, which are adding to the burden on society. The development of sports rehabilitation robotics (SRR) is becoming increasingly sophisticated at the technical level of its application; however, few studies have analyzed how it works and how effective it is in aiding rehabilitation, and fewer individualized exercise rehabilitation programs have been developed for elderly patients. The purpose of this study was to analyze the working methods and the effects of different types of SRR and then to suggest the feasibility of applying SRR to enhance the physical abilities of elderly patients with degenerative diseases. The researcher's team searched 633 English-language journal articles, which had been published over the past five years, and they selected 38 of them for a narrative literature review. Our summary found the following: (1) The current types of SRR are generally classified as end-effector robots, smart walkers, intelligent robotic rollators, and exoskeleton robots-exoskeleton robots were found to be the most widely used. (2) The current working methods include assistant tools as the main intermediaries-i.e., robots assist patients to participate; patients as the main intermediaries-i.e., patients dominate the assistant tools to participate; and sensors as the intermediaries-i.e., myoelectric-driven robots promote patient participation. (3) Better recovery was perceived for elderly patients when using SRR than is generally achieved through the traditional single-movement recovery methods, especially in strength, balance, endurance, and coordination. However, there was no significant improvement in their speed or agility after using SRR.
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Affiliation(s)
- Fangyuan Ju
- Department of Physical Education, Yangzhou University, Yangzhou 225012, China
| | - Yujie Wang
- Department of Physical Education, Yangzhou University, Yangzhou 225012, China
| | - Bin Xie
- Department of Physical Education, Yangzhou University, Yangzhou 225012, China
| | - Yunxuan Mi
- Department of Physical Education, Yangzhou University, Yangzhou 225012, China
| | - Mengyun Zhao
- Department of Physical Education, Yangzhou University, Yangzhou 225012, China
| | - Junwei Cao
- Department of Business, Yangzhou University, Yangzhou 225012, China
- Correspondence:
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Brand YE, Schwartz D, Gazit E, Buchman AS, Gilad-Bachrach R, Hausdorff JM. Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22187094. [PMID: 36146441 PMCID: PMC9502704 DOI: 10.3390/s22187094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/31/2022] [Accepted: 09/12/2022] [Indexed: 05/14/2023]
Abstract
Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision−recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor.
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Affiliation(s)
- Yonatan E. Brand
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Dafna Schwartz
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
| | - Aron S. Buchman
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Ran Gilad-Bachrach
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Edmond J. Safra Center for Bioinformatics, Tel-Aviv University, Tel Aviv 6997801, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University, Chicago, IL 60612, USA
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Correspondence:
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Atri R, Urban K, Marebwa B, Simuni T, Tanner C, Siderowf A, Frasier M, Haas M, Lancashire L. Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22186831. [PMID: 36146181 PMCID: PMC9502239 DOI: 10.3390/s22186831] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/25/2022] [Accepted: 09/02/2022] [Indexed: 06/01/2023]
Abstract
Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the unsupervised and "open world" nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these "walk-like" events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.
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Affiliation(s)
- Roozbeh Atri
- Cohen Veterans Bioscience, New York, NY 10018, USA
| | - Kevin Urban
- Cohen Veterans Bioscience, New York, NY 10018, USA
| | - Barbara Marebwa
- The Michael J Fox Foundation for Parkinson’s Research, New York, NY 10163, USA
| | - Tanya Simuni
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Caroline Tanner
- Department of Neurology, Weill Institute for Neurosciences University of California, San Francisco, CA 94143, USA
- Parkinson’s Disease Research Education and Clinical Center, San Francisco Veteran’s Affairs Medical Center, San Francisco, CA 94121, USA
| | - Andrew Siderowf
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Mark Frasier
- The Michael J Fox Foundation for Parkinson’s Research, New York, NY 10163, USA
| | - Magali Haas
- Cohen Veterans Bioscience, New York, NY 10018, USA
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Bogaarts G, Zanon M, Dondelinger F, Derungs A, Lipsmeier F, Gossens C, Lindemann M. Simulating the impact of noise on gait features extracted from smartphone sensor-data for the remote assessment of movement disorders. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6905-6910. [PMID: 34892692 DOI: 10.1109/embc46164.2021.9630594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Signs and symptoms of movement disorders can be remotely measured at home through sensor-based assessment of gait. However, sensor noise may impact the robustness of such assessments, in particular in a Bring-Your-Own-Device setting where the quality of sensors might vary. Here, we propose a framework to study the impact of inertial measurement unit noise on sensor-based gait features. This framework includes synthesizing realistic acceleration signals from the lower back during a gait cycle in OpenSim, estimating the magnitude of sensor noise from five smartphone models, perturbing the synthesized acceleration signal with the estimated noise in a Monte Carlo simulation, and computing gait features. In addition, we show that realistic levels of sensor noise have only a negligible impact on step power, a measure of gait.
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Keren K, Busse M, Fritz NE, Muratori LM, Gazit E, Hillel I, Scheinowitz M, Gurevich T, Inbar N, Omer N, Hausdorff JM, Quinn L. Quantification of Daily-Living Gait Quantity and Quality Using a Wrist-Worn Accelerometer in Huntington's Disease. Front Neurol 2021; 12:719442. [PMID: 34777196 PMCID: PMC8579964 DOI: 10.3389/fneur.2021.719442] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Huntington's disease (HD) leads to altered gait patterns and reduced daily-living physical activity. Accurate measurement of daily-living walking that takes into account involuntary movements (e.g. chorea) is needed. Objective: To evaluate daily-living gait quantity and quality in HD, taking into account irregular movements. Methods: Forty-two individuals with HD and fourteen age-matched non-HD peers completed clinic-based assessments and a standardized laboratory-based circuit of functional activities, wearing inertial measurement units on the wrists, legs, and trunk. These activities were used to train and test an algorithm for the automated detection of walking. Subsequently, 29 HD participants and 22 age-matched non-HD peers wore a tri-axial accelerometer on their non-dominant wrist for 7 days. Measures included gait quantity (e.g., steps per day), gait quality (e.g., regularity) metrics, and percentage of walking bouts with irregular movements. Results: Measures of daily-living gait quantity including step counts, walking time and bouts per day were similar in HD participants and non-HD peers (p > 0.05). HD participants with higher clinician-rated upper body chorea had a greater percentage of walking bouts with irregular movements compared to those with lower chorea (p = 0.060) and non-HD peers (p < 0.001). Even after accounting for irregular movements, within-bout walking consistency was lower in HD participants compared to non-HD peers (p < 0.001), while across-bout variability of these measures was higher (p < 0.001). Many of the daily-living measures were associated with disease-specific measures of motor function. Conclusions: Results suggest that a wrist-worn accelerometer can be used to evaluate the quantity and quality of daily-living gait in people with HD, while accounting for the influence of irregular (choreic-like) movements, and that gait features related to within- and across-bout consistency markedly differ in individuals with HD and non-HD peers.
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Affiliation(s)
- Karin Keren
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Monica Busse
- Centre for Trials Research, Cardiff University, Cardiff, United Kingdom
| | - Nora E. Fritz
- Departments of Health Care Sciences and Neurology, Wayne State University, Detroit, MI, United States
| | - Lisa M. Muratori
- Department of Physical Therapy, School of Health Technology and Management, Stony Brook University, Stony Brook, NY, United States
- George Huntington's Institute, Muenster, Germany
| | - Eran Gazit
- 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
| | - Micky Scheinowitz
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- School of Public Health, Tel Aviv University, Tel Aviv, Israel
| | - Tanya Gurevich
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
- Sackler School of Medicine and Sagol, School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Noit Inbar
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Nurit Omer
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
- Sackler School of Medicine and Sagol, School of Neuroscience, 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
- Sackler School of Medicine and Sagol, School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, United States
| | - Lori Quinn
- Centre for Trials Research, Cardiff University, Cardiff, United Kingdom
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, United States
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