{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T18:07:10Z","timestamp":1779473230962,"version":"3.53.1"},"reference-count":37,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate interpretation of electromyography (EMG) signals is essential for reliable control of musculoskeletal (MS) models in biomechanics and rehabilitation applications. Conventional preprocessing methods may not account for subject-specific signal characteristics and task-related muscle function.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and validate an adaptive and personalized EMG preprocessing pipeline to enhance the physiological accuracy of EMG-driven musculoskeletal models during elbow flexion-extension tasks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>EMG signals from six upper limb muscles were recorded using a Delsys system while participants performed elbow flexion-extension movements. The signals were preprocessed using individualized spectral filtering and a dual-stage normalization approach. First, dynamic maximum voluntary contraction (MVC) based min\u2013max normalization was applied to standardize signal amplitudes. Second, functional weighting was used to scale each muscle's activation based on its biomechanical contribution to the movement. The processed signals were used as input to an OpenSim elbow model, and resulting joint kinematics were compared to reference motion data captured by an Xsens system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The EMG-driven OpenSim model showed strong agreement with the Xsens data, with correlation coefficients exceeding 0.98 and root mean square error (RMSE) values below 8\u00b0. While a minor systematic offset was observed, joint angle trajectories remained consistent and physiologically plausible across trials.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed subject-specific EMG preprocessing pipeline enhances the accuracy and interpretability of biomechanical models. Future research should explore adaptive signal alignment techniques and AI-based processing methods to improve model robustness in dynamic and wearable scenarios.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329261432854","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T12:19:27Z","timestamp":1774441167000},"page":"371-391","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Personalized EMG preprocessing and normalization for musculoskeletal simulation"],"prefix":"10.1177","volume":"34","author":[{"given":"Dovydas","family":"Cic\u0117nas","sequence":"first","affiliation":[{"name":"Department of Biomechanical Engineering, Faculty of Mechanics, Vilnius Gediminas Technical University, Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jurgita","family":"\u017di\u017eien\u0117","sequence":"additional","affiliation":[{"name":"Department of Biomechanical Engineering, Faculty of Mechanics, Vilnius Gediminas Technical University, Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristina","family":"Daunoravi\u010dien\u0117","sequence":"additional","affiliation":[{"name":"Department of Biomechanical Engineering, Faculty of Mechanics, Vilnius Gediminas Technical University, Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2022.962959"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12984-019-0512-1"},{"key":"e_1_3_2_4_2","first-page":"06","article-title":"Long short-term memory-enabled electromyography-controlled adaptive wearable robotic exoskeleton for upper arm rehabilitation","volume":"10","author":"Samarakoon SMUS","unstructured":"Samarakoon SMUS, Herath HMKKMB, Yasakethu SLP, et al. 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An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. 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Physiology of muscle activation and force generation. Surf Electromyogr Physiol Eng Appl 2016; 26: 1\u201329.","journal-title":"Surf Electromyogr Physiol Eng Appl"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1050-6411(00)00027-4"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00618493"},{"key":"e_1_3_2_38_2","first-page":"152","article-title":"Opensim: simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement","volume":"1","author":"Seth A","year":"2018","unstructured":"Seth A, Hicks JL, Uchida TK, et al. Opensim: simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. 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