{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T18:36:14Z","timestamp":1774722974823,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T00:00:00Z","timestamp":1611619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFE0112000"],"award-info":[{"award-number":["2017YFE0112000"]}]},{"name":"Shanghai Pujiang Program","award":["19PJ1401100"],"award-info":[{"award-number":["19PJ1401100"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2017SHZDZX01"],"award-info":[{"award-number":["2017SHZDZX01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u00b1 1.75% (mean \u00b1 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 \u00b1 4.81% (mean \u00b1 standard error) with the accelerometer attached to the extensor carpi ulnaris.<\/jats:p>","DOI":"10.3390\/s21030799","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T00:38:16Z","timestamp":1611621496000},"page":"799","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8140-088X","authenticated-orcid":false,"given":"Long","family":"Meng","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China"}]},{"given":"Anjing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Neurological Rehabilitation Medicine, The First Rehabilitation Hospital of Shanghai, Kongjiang Branch, Shanghai 200093, China"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China"},{"name":"Human Phenome Institute, Fudan University, Shanghai 201203, China"}]},{"given":"Xingwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China"}]},{"given":"Xinyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China"}]},{"given":"Linkai","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China"},{"name":"Department of Industrial Design, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, AZ, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0514-1323","authenticated-orcid":false,"given":"Jiahao","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China"},{"name":"Human Phenome Institute, Fudan University, Shanghai 201203, China"}]},{"given":"Xuejiao","family":"Wu","sequence":"additional","affiliation":[{"name":"Center of Rehabilitation Therapy, The First Rehabilitation Hospital of Shanghai, Shanghai 200090, China"}]},{"given":"Chenyun","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4522-8354","authenticated-orcid":false,"given":"Yiyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"e-Media Research Lab, Campus Group T, KU Leuven, 3000 Leuven, Belgium"},{"name":"ESAT-STADIUS, Department of Electrical Engineering, KU Leuven, 3000 Leuven, Belgium"}]},{"given":"Bart","family":"Vanrumste","sequence":"additional","affiliation":[{"name":"e-Media Research Lab, Campus Group T, KU Leuven, 3000 Leuven, Belgium"},{"name":"ESAT-STADIUS, Department of Electrical Engineering, KU Leuven, 3000 Leuven, Belgium"}]},{"given":"Toshiyo","family":"Tamura","sequence":"additional","affiliation":[{"name":"Future Robotics Organization, Waseda University, 1-104, Totsuka-tyou, Shinjuku-ku, Tokyo 169-8050, Japan"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China"},{"name":"Human Phenome Institute, Fudan University, Shanghai 201203, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"473","DOI":"10.3389\/fnins.2020.00473","article-title":"Application of Stem Cells in Stroke: A Multifactorial Approach","volume":"14","author":"Singh","year":"2020","journal-title":"Front. 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