{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:24:28Z","timestamp":1792171468644,"version":"4.4.0"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52365039"],"award-info":[{"award-number":["52365039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023TSYCLJ0051"],"award-info":[{"award-number":["2023TSYCLJ0051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XJU2024BS103"],"award-info":[{"award-number":["XJU2024BS103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tianshan Talent Training Program","award":["52365039"],"award-info":[{"award-number":["52365039"]}]},{"name":"Tianshan Talent Training Program","award":["2023TSYCLJ0051"],"award-info":[{"award-number":["2023TSYCLJ0051"]}]},{"name":"Tianshan Talent Training Program","award":["XJU2024BS103"],"award-info":[{"award-number":["XJU2024BS103"]}]},{"name":"The Excellent Doctoral Graduate Innovation Program","award":["52365039"],"award-info":[{"award-number":["52365039"]}]},{"name":"The Excellent Doctoral Graduate Innovation Program","award":["2023TSYCLJ0051"],"award-info":[{"award-number":["2023TSYCLJ0051"]}]},{"name":"The Excellent Doctoral Graduate Innovation Program","award":["XJU2024BS103"],"award-info":[{"award-number":["XJU2024BS103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The estimation of multijoint angles is of great significance in the fields of lower limb rehabilitation, motion control, and exoskeleton robotics. Accurate joint angle estimation helps assess joint function, assist in rehabilitation training, and optimize robotic control strategies. However, estimating multijoint angles in different movement patterns, such as walking, obstacle crossing, squatting, and knee flexion\u2013extension, using surface electromyography (sEMG) signals remains a challenge. In this study, a model is proposed for the continuous motion estimation of multijoint angles in the lower limb (CB-TCN: temporal convolutional network + convolutional block attention module + temporal convolutional network). The model integrates temporal convolutional networks (TCNs) with convolutional block attention modules (CBAMs) to enhance feature extraction and improve prediction accuracy. The model effectively captures temporal features in lower limb movements, while enhancing attention to key features through the attention mechanism of CBAM. To enhance the model\u2019s generalization ability, this study adopts a sliding window data augmentation method to expand the training samples and improve the model\u2019s adaptability to different movement patterns. Through experimental validation on 8 subjects across four typical lower limb movements, walking, obstacle crossing, squatting, and knee flexion\u2013extension, the results show that the CB-TCN model outperforms traditional models in terms of accuracy and robustness. Specifically, the model achieved R2 values of up to 0.9718, RMSE as low as 1.2648\u00b0, and NRMSE values as low as 0.05234 for knee angle prediction during walking. These findings indicate that the model combining TCN and CBAM has significant advantages in predicting lower limb joint angles. The proposed approach shows great promise for enhancing lower limb rehabilitation and motion analysis.<\/jats:p>","DOI":"10.3390\/s25030719","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T11:50:47Z","timestamp":1737719447000},"page":"719","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7478-8189","authenticated-orcid":false,"given":"Yonglin","family":"Han","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5798-6526","authenticated-orcid":false,"given":"Qing","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2695","DOI":"10.1109\/TNSRE.2021.3136088","article-title":"Wearable lower-limb exoskeleton for children with cerebral palsy: A systematic review of mechanical design, actuation type, control strategy, and clinical evaluation","volume":"29","author":"Sarajchi","year":"2021","journal-title":"IEEE Trans. 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