{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T01:50:46Z","timestamp":1784685046724,"version":"3.55.0"},"reference-count":47,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deep neural networks (DNNs) and transfer learning (TL) have been used to improve surface electromyogram (sEMG)-based force estimation. However, prior studies focused mostly on applying TL within one joint, which limits dataset size and diversity. Herein, we investigated cross-joint TL between two upper-limb joints with four DNN architectures using sliding windows. We used two feedforward and two recurrent DNN models with feature engineering and feature learning, respectively. We found that the dependencies between sEMG and force are short-term (&lt;400 ms) and that sliding windows are sufficient to capture them, suggesting that more complicated recurrent structures may not be necessary. Also, using DNN architectures reduced the required sliding window length. A model pre-trained on elbow data was fine-tuned on hand\u2013wrist data, improving force estimation accuracy and reducing the required training data amount. A convolutional neural network with a 391 ms sliding window fine-tuned using 20 s of training data had an error of 6.03 \u00b1 0.49% maximum voluntary torque, which is statistically lower than both our multilayer perceptron model with TL and a linear regression model using 40 s of training data. The success of TL between two distinct joints could help enrich the data available for future deep learning-related studies.<\/jats:p>","DOI":"10.3390\/s24227301","type":"journal-article","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T05:33:04Z","timestamp":1731648784000},"page":"7301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improved Surface Electromyogram-Based Hand\u2013Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6337-7831","authenticated-orcid":false,"given":"Haopeng","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9964-1885","authenticated-orcid":false,"given":"He","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyun","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200241, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0584-3448","authenticated-orcid":false,"given":"Xinming","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0729-2523","authenticated-orcid":false,"given":"Edward A.","family":"Clancy","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/TNSRE.2014.2305111","article-title":"The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges","volume":"22","author":"Farina","year":"2014","journal-title":"IEEE Trans. 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