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Article

Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

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Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China
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Department of Neurological Rehabilitation Medicine, The First Rehabilitation Hospital of Shanghai, Kongjiang Branch, Shanghai 200093, China
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Human Phenome Institute, Fudan University, Shanghai 201203, China
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Department of Industrial Design, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, AZ, The Netherlands
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Center of Rehabilitation Therapy, The First Rehabilitation Hospital of Shanghai, Shanghai 200090, China
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e-Media Research Lab, Campus Group T, KU Leuven, 3000 Leuven, Belgium
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ESAT-STADIUS, Department of Electrical Engineering, KU Leuven, 3000 Leuven, Belgium
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Future Robotics Organization, Waseda University, 1-104, Totsuka-tyou, Shinjuku-ku, Tokyo 169-8050, Japan
*
Authors to whom correspondence should be addressed.
Academic Editor: Massimo Sacchetti
Sensors 2021, 21(3), 799; https://doi.org/10.3390/s21030799
Received: 10 December 2020 / Revised: 12 January 2021 / Accepted: 18 January 2021 / Published: 26 January 2021
(This article belongs to the Special Issue Wearable Sensors for Healthcare)
Commonly used sensors like accelerometers, gyroscopes, surface electromyography sensors, etc., which provide a convenient and practical solution for human activity recognition (HAR), have gained extensive attention. However, which kind of sensor can provide adequate information in achieving a satisfactory performance, or whether the position of a single sensor would play a significant effect on the performance in HAR are sparsely studied. In this paper, a comparative study to fully investigate the performance of the aforementioned sensors for classifying four activities (walking, tooth brushing, face washing, drinking) is explored. Sensors are spatially distributed over the human body, and subjects are categorized into three groups (able-bodied people, stroke survivors, and the union of both). Performances of using accelerometer, gyroscope, sEMG, and their combination in each group are evaluated by adopting the Support Vector Machine classifier with the Leave-One-Subject-Out Cross-Validation technique, and the optimal sensor position for each kind of sensor is presented based on the accuracy. Experimental results show that using the accelerometer could obtain the best performance in each group. The highest accuracy of HAR involving stroke survivors was 95.84 ± 1.75% (mean ± standard error), achieved by the accelerometer attached to the extensor carpi ulnaris. Furthermore, taking the practical application of HAR into consideration, a novel approach to distinguish various activities of stroke survivors based on a pre-trained HAR model built on healthy subjects is proposed, the highest accuracy of which is 77.89 ± 4.81% (mean ± standard error) with the accelerometer attached to the extensor carpi ulnaris. View Full-Text
Keywords: daily activity recognition; single wearable sensor; stroke; sensor placement daily activity recognition; single wearable sensor; stroke; sensor placement
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MDPI and ACS Style

Meng, L.; Zhang, A.; Chen, C.; Wang, X.; Jiang, X.; Tao, L.; Fan, J.; Wu, X.; Dai, C.; Zhang, Y.; Vanrumste, B.; Tamura, T.; Chen, W. Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People. Sensors 2021, 21, 799. https://doi.org/10.3390/s21030799

AMA Style

Meng L, Zhang A, Chen C, Wang X, Jiang X, Tao L, Fan J, Wu X, Dai C, Zhang Y, Vanrumste B, Tamura T, Chen W. Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People. Sensors. 2021; 21(3):799. https://doi.org/10.3390/s21030799

Chicago/Turabian Style

Meng, Long, Anjing Zhang, Chen Chen, Xingwei Wang, Xinyu Jiang, Linkai Tao, Jiahao Fan, Xuejiao Wu, Chenyun Dai, Yiyuan Zhang, Bart Vanrumste, Toshiyo Tamura, and Wei Chen. 2021. "Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People" Sensors 21, no. 3: 799. https://doi.org/10.3390/s21030799

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