{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T12:52:07Z","timestamp":1782478327937,"version":"3.54.5"},"reference-count":53,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.engappai.2026.115488","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T12:33:36Z","timestamp":1782304416000},"page":"115488","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P3","title":["A physiology-inspired bidirectional state space network for continuous knee joint angle prediction from surface electromyography"],"prefix":"10.1016","volume":"181","author":[{"given":"Bin","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liuyi","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huashun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liao","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipeng","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115488_b1","article-title":"Timemachine: A time series is worth 4 mambas for long-term forecasting","volume":"Vol. 392","author":"Ahamed","year":"2024"},{"key":"10.1016\/j.engappai.2026.115488_b2","doi-asserted-by":"crossref","DOI":"10.1109\/JSEN.2024.3423795","article-title":"Sctnet: Shifted windows and convolution layers transformer for continuous angle estimation of finger joints using semg","author":"An","year":"2024","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.engappai.2026.115488_b3","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s12984-015-0090-9","article-title":"On the analysis of movement smoothness","volume":"12","author":"Balasubramanian","year":"2015","journal-title":"J. NeuroEng. Rehabil."},{"key":"10.1016\/j.engappai.2026.115488_b4","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.bspc.2019.02.011","article-title":"A review on EMG-based motor intention prediction of continuous human upper limb motion for human\u2013robot collaboration","volume":"51","author":"Bi","year":"2019","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.engappai.2026.115488_b5","article-title":"A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions","volume":"119","author":"Camargo","year":"2021"},{"key":"10.1016\/j.engappai.2026.115488_b6","article-title":"sEMG-based gesture recognition via multi-feature fusion network","author":"Chen","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.engappai.2026.115488_b7","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.bspc.2017.10.002","article-title":"Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks","volume":"40","author":"Chen","year":"2018","journal-title":"Biomed. Signal Process. Control."},{"issue":"3","key":"10.1016\/j.engappai.2026.115488_b8","doi-asserted-by":"crossref","first-page":"458","DOI":"10.3390\/s17030458","article-title":"Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation","volume":"17","author":"Du","year":"2017","journal-title":"Sensors"},{"issue":"12","key":"10.1016\/j.engappai.2026.115488_b9","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1152\/japplphysiol.00280.2015","article-title":"Inappropriate interpretation of surface EMG signals and muscle fiber characteristics impedes understanding of the control of neuromuscular function","volume":"119","author":"Enoka","year":"2015","journal-title":"J. Appl. Physiol."},{"issue":"2","key":"10.1016\/j.engappai.2026.115488_b10","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1152\/physiol.00040.2015","article-title":"Principles of motor unit physiology evolve with advances in technology","volume":"31","author":"Farina","year":"2016","journal-title":"Physiology"},{"key":"10.1016\/j.engappai.2026.115488_b11","series-title":"Proceedings of the 33rd International Conference on Machine Learning","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","volume":"48","author":"Gal","year":"2016"},{"key":"10.1016\/j.engappai.2026.115488_b12","unstructured":"Gu, A., Dao, T., 2024. Mamba: Linear-time sequence modeling with selective state spaces. In: First Conference on Language Modeling."},{"key":"10.1016\/j.engappai.2026.115488_b13","series-title":"EEGMamba: Bidirectional state space model with mixture of experts for EEG multi-task classification","author":"Gui","year":"2024"},{"issue":"4","key":"10.1016\/j.engappai.2026.115488_b14","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/THMS.2016.2641389","article-title":"Toward an enhanced human\u2013machine interface for upper-limb prosthesis control with combined EMG and NIRS signals","volume":"47","author":"Guo","year":"2017","journal-title":"IEEE Trans. Human\u2013Machine Syst."},{"issue":"843","key":"10.1016\/j.engappai.2026.115488_b15","first-page":"136","article-title":"The heat of shortening and the dynamic constants of muscle","volume":"126","author":"Hill","year":"1938","journal-title":"Proc. R. Soc. Lond. Ser. B-Biol. Sci."},{"issue":"6","key":"10.1016\/j.engappai.2026.115488_b16","doi-asserted-by":"crossref","first-page":"529","DOI":"10.3200\/35-09-004-RC","article-title":"Sensitivity of smoothness measures to movement duration, amplitude, and arrests","volume":"41","author":"Hogan","year":"2009","journal-title":"J. Mot. Behav."},{"key":"10.1016\/j.engappai.2026.115488_b17","series-title":"EEGM2: An efficient mamba-2-based self-supervised framework for long-sequence EEG modeling","author":"Hong","year":"2025"},{"issue":"1","key":"10.1016\/j.engappai.2026.115488_b18","doi-asserted-by":"crossref","first-page":"38762","DOI":"10.1038\/s41598-025-22684-x","article-title":"Adaptive long-range modeling of EEG and ECG with mamba and dynamic graph learning","volume":"15","author":"Hu","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.engappai.2026.115488_b19","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G., 2018. Squeeze-and-excitation networks. In: Proceedings of IEEE CVPR. pp. 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"23","key":"10.1016\/j.engappai.2026.115488_b20","doi-asserted-by":"crossref","first-page":"11503","DOI":"10.1109\/JSEN.2019.2933603","article-title":"Real-time intended knee joint motion prediction by deep-recurrent neural networks","volume":"19","author":"Huang","year":"2019","journal-title":"IEEE Sensors J."},{"issue":"1","key":"10.1016\/j.engappai.2026.115488_b21","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1113\/jphysiol.2003.057174","article-title":"Five basic muscle activation patterns account for muscle activity during human locomotion","volume":"556","author":"Ivanenko","year":"2004","journal-title":"J. Physiol."},{"key":"10.1016\/j.engappai.2026.115488_b22","first-page":"1","article-title":"Exploring the contribution of joint angles and sEMG signals on joint torque prediction accuracy using LSTM-based deep learning techniques","author":"Kaya","year":"2024","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"10.1016\/j.engappai.2026.115488_b23","series-title":"Proceedings of the 36th International Conference on Machine Learning","first-page":"3519","article-title":"Similarity of neural network representations revisited","volume":"vol. 97","author":"Kornblith","year":"2019"},{"key":"10.1016\/j.engappai.2026.115488_b24","series-title":"Advances in Neural Information Processing Systems 30 (NeurIPS)","first-page":"6402","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","author":"Lakshminarayanan","year":"2017"},{"issue":"1","key":"10.1016\/j.engappai.2026.115488_b25","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1038\/s41528-024-00297-0","article-title":"Intelligent upper-limb exoskeleton integrated with soft bioelectronics and deep learning for intention-driven augmentation","volume":"8","author":"Lee","year":"2024","journal-title":"Npj Flex. Electron."},{"key":"10.1016\/j.engappai.2026.115488_b26","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., et al., 2019. Selective kernel networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 510\u2013519.","DOI":"10.1109\/CVPR.2019.00060"},{"issue":"11","key":"10.1016\/j.engappai.2026.115488_b27","doi-asserted-by":"crossref","first-page":"5272","DOI":"10.1109\/JBHI.2023.3304639","article-title":"SEMG-based end-to-end continues prediction of human knee joint angles using the tightly coupled convolutional transformer model","volume":"27","author":"Liang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.engappai.2026.115488_b28","article-title":"sEMG-based knee angle prediction: An efficient framework with xgboost feature selection and multi-attention LSTM","author":"Ling","year":"2025","journal-title":"IEEE Sensors J."},{"issue":"6","key":"10.1016\/j.engappai.2026.115488_b29","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/S0021-9290(03)00010-1","article-title":"An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo","volume":"36","author":"Lloyd","year":"2003","journal-title":"J. Biomech."},{"key":"10.1016\/j.engappai.2026.115488_b30","article-title":"PEMFC degradation prediction and probabilistic state assessment using a multi-scale periodic TimesNet with Laplace loss","author":"Lu","year":"2026","journal-title":"IEEE Trans. Transp. Electrification"},{"issue":"9","key":"10.1016\/j.engappai.2026.115488_b31","article-title":"Enhancing EEG signals classification using LSTM-CNN architecture","volume":"6","author":"Omar","year":"2024","journal-title":"Eng. Rep."},{"issue":"8","key":"10.1016\/j.engappai.2026.115488_b32","doi-asserted-by":"crossref","first-page":"7420","DOI":"10.1016\/j.eswa.2012.01.102","article-title":"Feature reduction and selection for EMG signal classification","volume":"39","author":"Phinyomark","year":"2012","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"10.1016\/j.engappai.2026.115488_b33","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3390\/bdcc2030021","article-title":"EMG pattern recognition in the era of big data and deep learning","volume":"2","author":"Phinyomark","year":"2018","journal-title":"Big Data Cogn. Comput."},{"key":"10.1016\/j.engappai.2026.115488_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129824","article-title":"Enhanced multi-scale TCN ensemble for component chain prediction using evolutionary algorithms","volume":"298","author":"Qin","year":"2026","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.engappai.2026.115488_b35","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1007\/s42235-025-00813-6","article-title":"Deep learning in electromyography signal-based lower limb angle prediction and activity classification","volume":"23","author":"Rani","year":"2026","journal-title":"J. Bionic Eng."},{"issue":"1","key":"10.1016\/j.engappai.2026.115488_b36","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1038\/s41598-024-84883-2","article-title":"A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system","volume":"15","author":"Shelishiyah","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.engappai.2026.115488_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107124","article-title":"Continuous online prediction of lower limb joints angles based on sEMG signals by deep learning approach","volume":"163","author":"Song","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.engappai.2026.115488_b38","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., et al., 2015. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"6","key":"10.1016\/j.engappai.2026.115488_b39","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/j.conb.2009.09.002","article-title":"The case for and against muscle synergies","volume":"19","author":"Tresch","year":"2009","journal-title":"Curr. Opin. Neurobiol."},{"issue":"2","key":"10.1016\/j.engappai.2026.115488_b40","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1038\/5721","article-title":"The construction of movement by the spinal cord","volume":"2","author":"Tresch","year":"1999","journal-title":"Nature Neurosci."},{"key":"10.1016\/j.engappai.2026.115488_b41","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.115488_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102406","article-title":"Human knee abnormality detection from imbalanced sEMG data","volume":"66","author":"Vijayvargiya","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.engappai.2026.115488_b43","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2025.1679101","article-title":"Characterization of muscle synergy similarity and adaptation in hip exoskeleton-assisted locomotion","volume":"13","author":"Wang","year":"2025","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"10","key":"10.1016\/j.engappai.2026.115488_b44","doi-asserted-by":"crossref","first-page":"953","DOI":"10.3390\/machines13100953","article-title":"A dual-task improved transformer framework for decoding lower limb sit-to-stand movement from sEMG and IMU data","volume":"13","author":"Wang","year":"2025","journal-title":"Machines"},{"key":"10.1016\/j.engappai.2026.115488_b45","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., et al., 2018. Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"10.1016\/j.engappai.2026.115488_b46","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.bspc.2017.08.015","article-title":"Continuous estimation of joint angle from electromyography using multiple time-delayed features and random forests","volume":"39","author":"Xiao","year":"2018","journal-title":"Biomed. Signal Process. Control."},{"issue":"10","key":"10.1016\/j.engappai.2026.115488_b47","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1109\/TNSRE.2017.2699598","article-title":"Gaussian process autoregression for simultaneous proportional multi-modal prosthetic control with natural hand kinematics","volume":"25","author":"Xiloyannis","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"4","key":"10.1016\/j.engappai.2026.115488_b48","doi-asserted-by":"crossref","first-page":"489","DOI":"10.3390\/mi15040489","article-title":"Advancements in sensor technologies and control strategies for lower-limb rehabilitation exoskeletons: A comprehensive review","volume":"15","author":"Yao","year":"2024","journal-title":"Micromachines"},{"issue":"2","key":"10.1016\/j.engappai.2026.115488_b49","doi-asserted-by":"crossref","first-page":"144","DOI":"10.3390\/bioengineering12020144","article-title":"Machine learning-and deep learning-based myoelectric control system for upper limb rehabilitation utilizing EEG and EMG signals: A systematic review","volume":"12","author":"Zaim","year":"2025","journal-title":"Bioengineering"},{"key":"10.1016\/j.engappai.2026.115488_b50","article-title":"Cross-attention multi-scale state space model for remaining useful life prediction of aircraft engines","volume":"69","author":"Zhang","year":"2026","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.engappai.2026.115488_b51","series-title":"FusAD: Time-frequency fusion with adaptive denoising for general time series analysis","author":"Zhang","year":"2025"},{"issue":"8","key":"10.1016\/j.engappai.2026.115488_b52","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.3390\/s25082448","article-title":"Review of sEMG for exoskeleton robots: Motion intention recognition techniques and applications","volume":"25","author":"Zhang","year":"2025","journal-title":"Sensors"},{"key":"10.1016\/j.engappai.2026.115488_b53","series-title":"International Conference on Intelligent Robotics and Applications","first-page":"599","article-title":"Continuous estimation algorithm of elbow joint angle based on mamba model","author":"Zhou","year":"2025"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626017720?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626017720?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T12:44:13Z","timestamp":1782477853000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626017720"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":53,"alternative-id":["S0952197626017720"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115488","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A physiology-inspired bidirectional state space network for continuous knee joint angle prediction from surface electromyography","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115488","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115488"}}