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Article

Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson’s Disease

1
Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
2
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
3
Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
4
Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
5
Edmond J. Safra Center for Bioinformatics, Tel-Aviv University, Tel Aviv 6997801, Israel
6
Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University, Chicago, IL 60612, USA
7
Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Heiko Gassner
Sensors 2022, 22(18), 7094; https://doi.org/10.3390/s22187094
Received: 1 August 2022 / Revised: 31 August 2022 / Accepted: 12 September 2022 / Published: 19 September 2022
(This article belongs to the Special Issue Inertial Sensors for Clinically Relevant Mobility Outcome Measures)
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. View Full-Text
Keywords: gait; Parkinson’s disease; machine learning; inertial measurement unit (IMU); wrist; accelerometer gait; Parkinson’s disease; machine learning; inertial measurement unit (IMU); wrist; accelerometer
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MDPI and ACS Style

Brand, Y.E.; Schwartz, D.; Gazit, E.; Buchman, A.S.; Gilad-Bachrach, R.; Hausdorff, J.M. 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 2022, 22, 7094. https://doi.org/10.3390/s22187094

AMA Style

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. 2022; 22(18):7094. https://doi.org/10.3390/s22187094

Chicago/Turabian Style

Brand, Yonatan E., Dafna Schwartz, Eran Gazit, Aron S. Buchman, Ran Gilad-Bachrach, and Jeffrey M. Hausdorff. 2022. "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 22, no. 18: 7094. https://doi.org/10.3390/s22187094

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