{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T11:09:21Z","timestamp":1779448161089,"version":"3.53.1"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Wearable surface electromyography (sEMG)-based gesture recognition enables intuitive human\u2013machine interaction, but practical deployment is often limited by hardware constraints, model complexity, and inter-subject variability. In this study, we developed a high-performance wrist-worn sEMG acquisition system and a lightweight neural network, ThinNet, to achieve efficient and accurate gesture recognition. The wristband features a ring-shaped differential electrode array and embedded filtering modules, achieving a signal-to-noise ratio (SNR) of 66.96 dB, significantly higher than commercial devices. Using data from 100 participants performing six gestures, ThinNet achieved 90.47% inter-subject accuracy, with peak accuracy reaching 96.80% under a three-tier buffered decision strategy. Systematic analysis demonstrated that the model maintains high performance with only 40% fine-tuning data, indicating excellent data efficiency. Importantly, the framework supports scalability across additional users and practical deployment in real-world applications. These results highlight the combined effectiveness of hardware optimization and algorithm design in advancing wearable sEMG-based gesture recognition systems.<\/jats:p>","DOI":"10.3390\/bioengineering13060593","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T10:07:47Z","timestamp":1779444467000},"page":"593","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Wrist-Wearable sEMG Gesture Recognition System Based on ThinNet Lightweight Neural Network"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0410-1496","authenticated-orcid":false,"given":"Zihao","family":"Wang","sequence":"first","affiliation":[{"name":"Human Phenome Institute, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8140-088X","authenticated-orcid":false,"given":"Long","family":"Meng","sequence":"additional","affiliation":[{"name":"Center of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"Human Phenome Institute, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"61378","DOI":"10.1109\/ACCESS.2019.2914728","article-title":"Intelligent human\u2013computer interaction based on surface EMG gesture recognition","volume":"7","author":"Qi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/TNSRE.2019.2896269","article-title":"Deep learning for electromyographic hand gesture signal classification using transfer learning","volume":"27","author":"Chevariie","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_3","first-page":"1245","article-title":"Simultaneous and proportional control of real-time prosthesis using sEMG: A clinical validation","volume":"28","author":"Hahne","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/JBHI.2019.2941535","article-title":"Exploration of Chinese sign language recognition using wearable sensors based on deep belief net","volume":"24","author":"Yu","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_5","unstructured":"Schabron, B., Alashqar, Z., Fuhrman, N., Jibbe, K., and Desai, J. Artificial neural network to detect human hand gestures for a robotic arm control. Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tiboni, M., Borboni, A., V\u00e9rit\u00e9, F., Bregoli, C., and Amici, C. (2022). Sensors and actuation technologies in exoskeletons: A review. Sensors, 22.","DOI":"10.3390\/s22030884"},{"key":"ref_7","first-page":"2451","article-title":"Eight-sensor inertial gyroscope system for measuring posture and assessing fall risk","volume":"61","author":"Brodie","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Karatsidis, A., Bellusci, G., Schepers, H.M., de Zee, M., Andersen, M.S., and Veltink, P.H. (2016). Estimation of ground reaction forces and moments during gait using only inertial motion capture. Sensors, 17.","DOI":"10.3390\/s17010075"},{"key":"ref_9","first-page":"1412","article-title":"Direct comparison of surface EMG and inertial sensors for wearable gesture recognition","volume":"22","author":"Godfrey","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_10","unstructured":"Wichai, T., Chukamlang, R., and Massagram, W. Electromyography-based hand pose estimation using machine learning. Proceedings of the 21st International Joint Conference on Computer Science and Software Engineering (JCSSE), Phuket, Thailand."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1109\/TBCAS.2023.3314053","article-title":"Novel wearable HD-EMG sensor with shift-robust gesture recognition using deep learning","volume":"17","author":"Chamberland","year":"2023","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4388","DOI":"10.1109\/JIOT.2022.3218739","article-title":"Optimizing the cross-day performance of electromyogram biometric decoder","volume":"10","author":"Jiang","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Islam, M.R., Massicotte, D., Massicotte, P., and Zhu, W.-P. (2024). Surface EMG-based intersession\/intersubject gesture recognition by leveraging lightweight all-convnet and transfer learning. IEEE Trans. Instrum. Meas., 73.","DOI":"10.1109\/TIM.2024.3381288"},{"key":"ref_14","first-page":"1855","article-title":"Inter-subject surface EMG pattern recognition using spatio-temporal graph convolutional networks","volume":"30","author":"Li","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/TNSRE.2023.3342050","article-title":"Cross-user electromyography pattern recognition based on a novel spatial-temporal graph convolutional network","volume":"32","author":"Xu","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4211","DOI":"10.1109\/TNSRE.2023.3324252","article-title":"TraHGR: Transformer for hand gesture recognition via electromyography","volume":"31","author":"Zabihi","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1109\/TMRB.2024.3408896","article-title":"Enhanced EMG-based hand gesture classification in real-world scenarios","volume":"6","author":"Rani","year":"2024","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"ref_18","first-page":"14521","article-title":"A lightweight deep learning model for real-time hand gesture recognition on embedded systems","volume":"23","author":"Yang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, H., and Zhang, Y. (2022). MyoTac: Real-time recognition of tactical sign language based on lightweight deep neural network. Wirel. Commun. Mob. Comput., 2022.","DOI":"10.1155\/2022\/2774430"},{"key":"ref_20","first-page":"275","article-title":"A lightweight EMG-based gesture recognition system for wearable platforms","volume":"13","author":"Benatti","year":"2019","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_21","first-page":"104910","article-title":"Attention-based fusion of sEMG and inertial signals for gesture recognition","volume":"85","author":"Wang","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_22","unstructured":"Yang, A., Zhang, H., Cheng, S., Wang, R., Yuan, M., and Li, G. (2021). A flexible high-density electrode array for robust surface electromyography acquisition. Front. Bioeng. Biotechnol., 9."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/TNSRE.2021.3082551","article-title":"Open access dataset, toolbox and benchmark processing results of high-density surface electromyogram recordings","volume":"29","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_24","unstructured":"Phinyomark, A., and Scheme, E. (2021). EMG pattern recognition in the era of deep learning: A review of state of the art and challenges. Bioengineering, 8."},{"key":"ref_25","first-page":"117410","article-title":"Robustness of sEMG pattern recognition under electrode shift and muscle fatigue","volume":"203","author":"Zhang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_26","first-page":"105822","article-title":"Domain generalization for tolerance to electrode shift in sEMG pattern recognition","volume":"90","author":"Sun","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_27","first-page":"103559","article-title":"Deep learning for electromyographic pattern recognition: A review","volume":"89","author":"Xiong","year":"2021","journal-title":"Med. Eng. Phys."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2520616","DOI":"10.1109\/TIM.2022.3217868","article-title":"User-tailored hand gesture recognition system for wearable prosthesis and armband based on surface electromyogram","volume":"71","author":"Meng","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2350012","DOI":"10.1142\/S0129065724500102","article-title":"sEMG-based inter-session hand gesture recognition via domain adaptation","volume":"34","author":"Guo","year":"2024","journal-title":"Int. J. Neural Syst."},{"key":"ref_30","unstructured":"Kanoga, S., Hoshino, T., Miyamoto, K., and Kanemura, A. (2021). Real-time hand gesture recognition using surface electromyography for robotic arm control. Front. Neurorobot., 15."},{"key":"ref_31","unstructured":"Chen, L., Zhang, X., Wang, J., Wu, J., and Jiang, L. (2023). Deep learning for surface electromyography: A review of applications in rehabilitation robotics. Bioengineering, 10."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/593\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T10:52:16Z","timestamp":1779447136000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/593"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,22]]},"references-count":31,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["bioengineering13060593"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13060593","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,22]]}}}