{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T11:05:50Z","timestamp":1783940750667,"version":"3.55.0"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:00:00Z","timestamp":1783814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Higher Education Institutions","award":["2023SJZD143"],"award-info":[{"award-number":["2023SJZD143"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Sit-to-walk (STW) is a short-duration, high-load, multijoint transition requiring rapid lower-limb neuromuscular coordination across seat-off, load transfer, and gait initiation. Surface electromyography (sEMG)-based prediction of knee and ankle joint moments may support motor function evaluation and inform future assistive-control applications, but existing models remain limited in modeling cross-muscle sEMG feature interactions and mitigating phase-dependent prediction errors. This study developed an anatomy-informed framework for sEMG-driven moment prediction during STW. The model encoded sEMG channels from thigh and shank muscles into separate anatomical branches. Cross-Attention was used to model task-relevant intersegmental interactions, and BiLSTM was applied to capture short-term temporal dependencies. Eighteen healthy participants performed STW trials while sEMG, three-dimensional kinematics, and ground reaction forces were synchronously collected. Knee and ankle moments were calculated using inverse dynamics and used as reference targets. Among six models, the Cross-Attention model achieved the lowest test-set overall error, with an Overall nRMSE Fixed of 4.51%; the knee peak error in the P3 unloading phase was 16.17%. Ablation experiments indicated that Cross-Attention, BiLSTM temporal modeling, anatomical branch separation, and joint-specific output mapping contributed to prediction performance. This framework provides an interpretable approach for sEMG-driven multijoint moment prediction in complex non-stationary movements.<\/jats:p>","DOI":"10.3390\/bioengineering13070798","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T09:51:09Z","timestamp":1783936269000},"page":"798","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Anatomy-Informed Cross-Attention Framework for sEMG-Driven Knee and Ankle Moment Prediction During Sit-to-Walk Transitions"],"prefix":"10.3390","volume":"13","author":[{"given":"Jiarong","family":"Wu","sequence":"first","affiliation":[{"name":"School of Physical Education, Soochow University, Suzhou 215021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Physical Education, Soochow University, Suzhou 215021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuxia","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Physical Education, Soochow University, Suzhou 215021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9712-4557","authenticated-orcid":false,"given":"Wanli","family":"Zang","sequence":"additional","affiliation":[{"name":"School of Physical Education, Soochow University, Suzhou 215021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1038\/s41597-024-03740-z","article-title":"A Motion Capture Dataset on Human Sitting to Walking Transitions","volume":"11","author":"Perera","year":"2024","journal-title":"Sci. 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