{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T17:01:59Z","timestamp":1788282119948,"version":"build-2803163510"},"reference-count":28,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ningbo Key Research and Development Program","award":["2024Z199"],"award-info":[{"award-number":["2024Z199"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface electromyography (sEMG) enables non-invasive acquisition of neuromuscular activity and has shown strong potential for motion intention recognition in human\u2013machine interaction. However, achieving reliable and real-time decoding remains critical for interactive upper-limb assistance. This study presents a structured sEMG-based framework for motion intention recognition in upper-limb assistance tasks, integrating multi-channel acquisition, standardized preprocessing, time-domain feature extraction, and supervised learning. sEMG signals from four representative motions were collected, and eight time-domain features were extracted from denoised and segmented signal windows. A compact feature subset was identified through systematic evaluation. Five classifiers were benchmarked under consistent validation conditions, with Random Forest achieving the best performance and further optimized via K-fold cross-validation. The proposed method achieved an average intra-subject accuracy of 95.23% across eight subjects and 95.72% in online interactive validation. These results demonstrate that time-domain feature fusion combined with ensemble learning provides robust and efficient motion discrimination, highlighting its potential for real-time assistive and rehabilitation applications.<\/jats:p>","DOI":"10.3390\/s26103021","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T15:39:32Z","timestamp":1778513972000},"page":"3021","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["sEMG-Based Motion Intention Recognition for Interactive Upper Limb Nursing Assistance"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1591-2811","authenticated-orcid":false,"given":"Zekun","family":"Peng","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongfei","family":"Feng","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangda","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8354-6452","authenticated-orcid":false,"given":"Jiaxing","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Yangzhou University, Yangzhou 225009, China"},{"name":"Yangzhou Command AI Technology Co., Ltd., Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Fang","sequence":"additional","affiliation":[{"name":"Yangzhou Command AI Technology Co., Ltd., Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Olatunji, S.A., Shim, J.S., Syed, A., Tsai, Y.L., Pereira, A.E., Mahajan, H.P., Mudar, R.A., and Rogers, W.A. (2025). Robotic support for older adults with cognitive and mobility impairments. Front. Robot. AI, 12.","DOI":"10.3389\/frobt.2025.1545733"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4130","DOI":"10.1109\/TASE.2026.3653476","article-title":"Trajectory planning method for fracture reduction of parallel robots based on DMP and APF","volume":"23","author":"Su","year":"2026","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e42652","DOI":"10.2196\/42652","article-title":"Robots in assisted living facilities: Scoping review","volume":"6","author":"Trainum","year":"2023","journal-title":"JMIR Aging"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bo, V., Garrell Zulueta, A., and Sanfeliu Cottes, A. (2026, January 16\u201319). Fast or accurate? How intention-recognition models shape human perception of a mobile robot. Proceedings of the 2026 ACM\/IEEE International Conference on Human-Robot Interaction, Edinburgh, UK.","DOI":"10.1145\/3776734.3794445"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Maria, K., Fotios, L., Akilesh, R., Sevastopoulos, C., Nambiappan, H.R., Chaitanya, K.K., Babu, A.R., Mathew, J., and Makedon, F. (2021). A survey of robots in healthcare. Technologies, 9.","DOI":"10.3390\/technologies9010008"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1080\/17483107.2023.2170477","article-title":"Research of intent recognition in rehabilitation robots: A systematic review","volume":"19","author":"Luo","year":"2024","journal-title":"Disabil. Rehabil.-Assist."},{"key":"ref_7","first-page":"1075","article-title":"Intuitive human-robot-environment interaction with EMG Signals: A Review","volume":"11","author":"Xiong","year":"2024","journal-title":"IEEE-CAA J. Autom."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gantenbein, J., Dittli, J., Meyer, J.T., Gassert, R., and Lambercy, O. (2022). Intention detection strategies for robotic upper-limb orthoses: A scoping review considering usability, daily life application, and user evaluation. Front. Neurorobot., 16.","DOI":"10.3389\/fnbot.2022.815693"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Grimmelsmann, N., Mechtenberg, M., Schenck, W., Meyer, H.G., and Schneider, A. (2023). sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical\/physiological measures and a reduced set of optimization parameters. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0289549"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ding, Z., Yang, C., Wang, Z., Yin, X., and Jiang, F. (2021). Online adaptive prediction of human motion intention based on sEMG. Sensors, 21.","DOI":"10.3390\/s21082882"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Song, T., Yan, Z., Guo, S., Li, Y., Li, X., and Xi, F. (2023). Review of sEMG for robot control: Techniques and applications. Appl. Sci., 13.","DOI":"10.3390\/app13179546"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Boyer, M., Bouyer, L., Roy, J.S., and Campeau-Lecours, A. (2023). Reducing noise, artifacts and interference in single-channel EMG signals: A review. Sensors, 23.","DOI":"10.20944\/preprints202302.0052.v1"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Piyathilaka, L., Sul, J.H., Dunu, A.S., Jayawardena, A., and Moratuwage, D. (2026). Advances in EMG signal processing and pattern recognition: Techniques, challenges, and emerging applications. Electronics, 15.","DOI":"10.3390\/electronics15030590"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100866","DOI":"10.1016\/j.irbm.2024.100866","article-title":"Comprehensive review of feature extraction techniques for sEMG signal classification: From handcrafted features to deep learning approaches","volume":"45","author":"Rida","year":"2024","journal-title":"IRBM"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9022","DOI":"10.3390\/s150409022","article-title":"Comparison of sEMG-based feature extraction and motion classification methods for upper-limb movement","volume":"15","author":"Guo","year":"2015","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1109\/TNSRE.2017.2687520","article-title":"A framework of temporal-spatial descriptors-based feature extraction for improved myoelectric pattern recognition","volume":"25","author":"Khushaba","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Ma, M.Y. (2023). Research on sEMG feature generation and classification performance based on EBGAN. Electronics, 12.","DOI":"10.3390\/electronics12041040"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104774","DOI":"10.1016\/j.bspc.2023.104774","article-title":"Characterization of signal features for real-time sEMG onset detection","volume":"84","author":"Cho","year":"2023","journal-title":"Biomed. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, X., Qu, Y., Zhang, G., Wang, Z., Chen, C., and Xu, X. (2025). Review of sEMG for exoskeleton robots: Motion intention recognition techniques and applications. Sensors, 25.","DOI":"10.3390\/s25082448"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zheng, G., Lin, J., Zhang, J., Jia, H., Tang, J., and Shi, L. (2026). IFIANet: Frequency attention network for time\u2013frequency in sEMG-based motion intent recognition. Sensors, 26.","DOI":"10.3390\/s26010169"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xia, Y., Qiu, D., Zhang, C., and Liu, J. (2025). sEMG-based gesture recognition using multi-stream adaptive CNNs with integrated residual modules. Front. Bioeng. Biotechnol., 13.","DOI":"10.3389\/fbioe.2025.1487020"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Abbaspour, S., Naber, A., Ortiz-Catalan, M., GholamHosseini, H., and Lind\u00e9n, M. (2021). Real-time and offline evaluation of myoelectric pattern recognition for the decoding of hand movements. Sensors, 21.","DOI":"10.3390\/s21165677"},{"key":"ref_23","first-page":"512","article-title":"Deep learning for EMG-based human-machine interaction: A review","volume":"8","author":"Xiong","year":"2021","journal-title":"IEEE-CAA J. Autom."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kok, C.L., Ho, C.K., Tan, F.K., and Koh, Y.Y. (2024). Machine learning-based feature extraction and classification of EMG signals for intuitive prosthetic control. Appl. Sci., 14.","DOI":"10.3390\/app14135784"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"30867","DOI":"10.1038\/s41598-024-81369-z","article-title":"An efficient surface electromyography-based gesture recognition algorithm based on multiscale fusion convolution and channel attention","volume":"14","author":"Jiang","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1016\/j.aej.2020.01.015","article-title":"Intelligent human computer interaction based on non redundant EMG signal","volume":"59","author":"Sun","year":"2020","journal-title":"Alex. Eng. J."},{"key":"ref_27","first-page":"85","article-title":"Kinematic analysis of the variable height rotating mechanism for end-traction upper limb rehabilitation robot","volume":"38","author":"Liang","year":"2022","journal-title":"Int. J. Robot. Autom."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yu, L., Feng, Y., Dong, F., Wang, H., Li, H., Shen, T., Liang, D., and Vladareanu, V. (2023). Design and analysis of a lower limb rehabilitation robot with movement in three-dimensional space. International Conference on Innovation of Emerging Information and Communication Technology, Springer Nature.","DOI":"10.1007\/978-3-031-53237-5_4"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/10\/3021\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T15:50:07Z","timestamp":1778514607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/10\/3021"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,11]]},"references-count":28,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26103021"],"URL":"https:\/\/doi.org\/10.3390\/s26103021","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,11]]}}}