{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:08:57Z","timestamp":1787497737695,"version":"build-2736575974"},"reference-count":59,"publisher":"American Association for the Advancement of Science (AAAS)","funder":[{"DOI":"10.13039\/501100010877","name":"Science, Technology and Innovation Commission of Shenzhen Municipality","doi-asserted-by":"publisher","award":["XMHT20230115002"],"award-info":[{"award-number":["XMHT20230115002"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010877","name":"Science, Technology and Innovation Commission of Shenzhen Municipality","doi-asserted-by":"publisher","award":["KQTD20210811090217009"],"award-info":[{"award-number":["KQTD20210811090217009"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012228","name":"Development and Reform Commission of Shenzhen Municipality","doi-asserted-by":"publisher","award":["KCXFZ20230731093501003"],"award-info":[{"award-number":["KCXFZ20230731093501003"]}],"id":[{"id":"10.13039\/501100012228","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["2024YFE0216500"],"award-info":[{"award-number":["2024YFE0216500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100010877","name":"Science, Technology and Innovation Commission of Shenzhen Municipality","doi-asserted-by":"publisher","award":["JCYJ20240813154923031"],"award-info":[{"award-number":["JCYJ20240813154923031"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Cyborg Bionic Syst"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Lower limb musculoskeletal dynamics simulation has been widely used to estimate the lower limb mechanics, but challenges such as heavy reliance on force plates, poor model generalization, and high computational load hindered its application in real-time robot control systems requiring rapid feedback and inference. This study proposed the Marker-GMformer model, a marker trajectories-driven deep learning model designed for efficient and accurate continuous prediction of lower limb kinematics and dynamics. By integrating prior knowledge with global\u2013local and spatial\u2013temporal features from the inputted marker coordinate time series, Marker-GMformer maintained high performance while reducing computational complexity. The model also demonstrated strong generalization, accurately predicting multi-joint kinematics, moments, and ground reaction forces (GRFs) across 13 different motion patterns. The predicted results were compared to those from musculoskeletal multibody dynamics simulations and force plates. Excellent performance was achieved with average Pearson correlation coefficients (\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mi>\u03c1<\/mml:mi>\n                        <mml:mo>\u2265<\/mml:mo>\n                        <mml:mn>0.97<\/mml:mn>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ) and low root mean square errors (RMSE = 1.95\u00b0 for angles, RMSE = 0.036 body weight for GRFs, and RMSE = 0.099 N\u00b7m\/kg for moments) across all patterns. The findings underscored the substantial promise of the proposed method for enabling real-time monitoring of human lower limb mechanics and delivering timely feedback to optimize the control of assistive robots.\n                  <\/jats:p>","DOI":"10.34133\/cbsystems.0476","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T13:08:43Z","timestamp":1764162523000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":5,"title":["Continuous Lower Limb Biomechanics Prediction via Prior-Informed Lightweight Marker-GMformer"],"prefix":"10.34133","volume":"7","author":[{"given":"Hao","family":"Zhou","sequence":"first","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, \rChinese Academy of Sciences, Shenzhen 518055, China."},{"name":"Southern University of Science and Technology","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinghu","family":"Peng","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, \rChinese Academy of Sciences, Shenzhen 518055, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, \rChinese Academy of Sciences, Shenzhen 518055, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueyan","family":"Lyu","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, \rChinese Academy of Sciences, Shenzhen 518055, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongfei","family":"Zou","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, \rChinese Academy of Sciences, Shenzhen 518055, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Yong","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, \rChinese Academy of Sciences, Shenzhen 518055, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dahua","family":"Shou","sequence":"additional","affiliation":[{"name":"School of Fashion and Textiles, \rThe Hong Kong Polytechnic University, Hong Kong 999077, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanglin","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, \rChinese Academy of Sciences, Shenzhen 518055, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, \rChinese Academy of Sciences, Shenzhen 518055, China."},{"name":"Shenzhen Lower Limb Intelligent Rehabilitation Engineering Research Center","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"0122","DOI":"10.34133\/cbsystems.0122","article-title":"Interlimb and Intralimb synergy modeling for lower limb assistive devices: Modeling methods and feature selection","volume":"5","author":"Liang F","year":"2024","unstructured":"Liang F, Mo L, Sun Y, Guo C, Gao F, Liao WH, Cao J, Li B, Song Z, Wang D, et al. Interlimb and Intralimb synergy modeling for lower limb assistive devices: Modeling methods and feature selection. Cyborg Bionic Syst. 2024;5:0122.","journal-title":"Cyborg Bionic Syst"},{"key":"e_1_3_4_3_2","doi-asserted-by":"crossref","first-page":"0115","DOI":"10.34133\/cbsystems.0115","article-title":"Adaptive gait training of a lower limb rehabilitation robot based on human\u2013robot interaction force measurement","volume":"5","author":"Yu F","year":"2024","unstructured":"Yu F, Liu Y, Wu Z, Tan M, Yu J. Adaptive gait training of a lower limb rehabilitation robot based on human\u2013robot interaction force measurement. Cyborg Bionic Syst. 2024;5:0115.","journal-title":"Cyborg Bionic Syst"},{"key":"e_1_3_4_4_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102776","article-title":"Feasibility assessment of transfer functions describing biomechanics of the human lower limb during the gait cycle","volume":"69","author":"Nacpil EJC","year":"2021","unstructured":"Nacpil EJC, Nacy S, Youssef G. Feasibility assessment of transfer functions describing biomechanics of the human lower limb during the gait cycle. Biomed Signal Process Control. 2021;69: Article 102776.","journal-title":"Biomed Signal Process Control"},{"key":"e_1_3_4_5_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107229","article-title":"Design and performance evaluation method of wearable hip joint power-assisted exoskeleton","volume":"101","author":"Wu B","year":"2025","unstructured":"Wu B, Lv J, Chen X, Fang S, Pan W, Qiang L, Song D. Design and performance evaluation method of wearable hip joint power-assisted exoskeleton. Biomed Signal Process Control. 2025;101: Article 107229.","journal-title":"Biomed Signal Process Control"},{"issue":"5","key":"e_1_3_4_6_2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0155612","article-title":"Standardized loads acting in hip implants","volume":"11","author":"Bergmann G","year":"2016","unstructured":"Bergmann G, Bender A, Dymke J, Duda G, Damm P. Standardized loads acting in hip implants. PLOS ONE. 2016;11(5): Article e0155612.","journal-title":"PLOS ONE"},{"issue":"8","key":"e_1_3_4_7_2","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1016\/0021-9290(93)90058-M","article-title":"Hip joint loading during walking and running, measured in two patients","volume":"26","author":"Bergmann G","year":"1993","unstructured":"Bergmann G, Graichen F, Rohlmann A. Hip joint loading during walking and running, measured in two patients. J Biomech. 1993;26(8):969\u2013990.","journal-title":"J Biomech"},{"issue":"12","key":"e_1_3_4_8_2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1008493","article-title":"OpenSim Moco: Musculoskeletal optimal control","volume":"16","author":"Dembia CL","year":"2020","unstructured":"Dembia CL, Bianco NA, Falisse A, Hicks JL, Delp SL. OpenSim Moco: Musculoskeletal optimal control. PLOS Comput Biol. 2020;16(12): Article e1008493.","journal-title":"PLOS Comput Biol"},{"issue":"1","key":"e_1_3_4_9_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/23335432.2014.993706","article-title":"Scaling of musculoskeletal models from static and dynamic trials","volume":"2","author":"Lund ME","year":"2015","unstructured":"Lund ME, Andersen MS, de Zee M, Rasmussen J. Scaling of musculoskeletal models from static and dynamic trials. Int Biomechan. 2015;2(1):1\u201311.","journal-title":"Int Biomechan"},{"issue":"7","key":"e_1_3_4_10_2","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.3390\/ijerph17072226","article-title":"Immediate effects of medially posted insoles on lower limb joint contact forces in adult acquired flatfoot: A pilot study","volume":"17","author":"Peng Y","year":"2020","unstructured":"Peng Y, Wong DWC, Wang Y, Chen TLW, Tan Q, Chen Z, Jin Z, Zhang M. Immediate effects of medially posted insoles on lower limb joint contact forces in adult acquired flatfoot: A pilot study. Int J Environ Res Public Health. 2020;17(7):2226.","journal-title":"Int J Environ Res Public Health"},{"key":"e_1_3_4_11_2","doi-asserted-by":"crossref","DOI":"10.1007\/s10409-022-22140-x","article-title":"A forward-inverse dynamics modeling framework for human musculoskeletal multibody system","volume":"38","author":"Wang X","year":"2022","unstructured":"Wang X, Guo J, Tian Q. A forward-inverse dynamics modeling framework for human musculoskeletal multibody system. Acta Mech Sinica. 2022;38: Article 522140.","journal-title":"Acta Mech Sinica"},{"key":"e_1_3_4_12_2","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s11044-016-9537-4","article-title":"Prediction of ground reaction forces and moments during sports-related movements","volume":"39","author":"Skals S","year":"2017","unstructured":"Skals S, Jung MK, Damsgaard M, Andersen MS. Prediction of ground reaction forces and moments during sports-related movements. Multibody Syst Dyn. 2017;39:175\u2013195.","journal-title":"Multibody Syst Dyn"},{"issue":"1","key":"e_1_3_4_13_2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0021-9290(01)00158-0","article-title":"The fundamental problem of myoskeletal inverse dynamics and its implications","volume":"35","author":"Hatze H","year":"2002","unstructured":"Hatze H. The fundamental problem of myoskeletal inverse dynamics and its implications. J Biomech. 2002;35(1):109\u2013115.","journal-title":"J Biomech"},{"issue":"11","key":"e_1_3_4_14_2","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TBME.2007.901024","article-title":"OpenSim: Open-source software to create and analyze dynamic simulations of movement","volume":"54","author":"Delp SL","year":"2007","unstructured":"Delp SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT, Guendelman E, Thelen DG. OpenSim: Open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng. 2007;54(11):1940\u20131950.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"8","key":"e_1_3_4_15_2","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1016\/j.simpat.2006.09.001","article-title":"Analysis of musculoskeletal systems in the AnyBody modeling system","volume":"14","author":"Damsgaard M","year":"2006","unstructured":"Damsgaard M, Rasmussen J, Christensen ST, Surma E, De Zee M. Analysis of musculoskeletal systems in the AnyBody modeling system. Simul Model Pract Theory. 2006;14(8):1100\u20131111.","journal-title":"Simul Model Pract Theory"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2022.3158727"},{"key":"e_1_3_4_17_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117340","article-title":"Continuous and simultaneous estimation of lower limb multi-joint angles from sEMG signals based on stacked convolutional and LSTM models","volume":"203","author":"Lu Y","year":"2022","unstructured":"Lu Y, Wang H, Zhou B, Wei C, Xu S. Continuous and simultaneous estimation of lower limb multi-joint angles from sEMG signals based on stacked convolutional and LSTM models. Expert Syst Appl. 2022;203: Article 117340.","journal-title":"Expert Syst Appl"},{"key":"e_1_3_4_18_2","doi-asserted-by":"crossref","first-page":"1208711","DOI":"10.3389\/fbioe.2023.1208711","article-title":"Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks","volume":"11","author":"Liew BX","year":"2023","unstructured":"Liew BX, R\u00fcgamer D, Mei Q, Altai Z, Zhu X, Zhai X, Cortes N. Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks. Front Bioeng Biotechnol. 2023;11:1208711.","journal-title":"Front Bioeng Biotechnol"},{"key":"e_1_3_4_19_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122868","article-title":"Convolutional neural network based ground reaction forces and center of pressure estimation during stair walking using multi-level kinematics","volume":"243","author":"Liu D","year":"2024","unstructured":"Liu D, Ma Y, Wang J, Hou M, Zhang C. Convolutional neural network based ground reaction forces and center of pressure estimation during stair walking using multi-level kinematics. Expert Syst Appl. 2024;243: Article 122868.","journal-title":"Expert Syst Appl"},{"key":"e_1_3_4_20_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-05191-1"},{"key":"e_1_3_4_21_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07382-4"},{"issue":"1","key":"e_1_3_4_22_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12984-023-01287-5","article-title":"Optimizing exoskeleton assistance to improve walking speed and energy economy for older adults","volume":"21","author":"Lakmazaheri A","year":"2024","unstructured":"Lakmazaheri A, Song S, Vuong BB, Biskner B, Kado DM, Collins SH. Optimizing exoskeleton assistance to improve walking speed and energy economy for older adults. J Neuroeng Rehabil. 2024;21(1):1.","journal-title":"J Neuroeng Rehabil"},{"key":"e_1_3_4_23_2","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1109\/TNSRE.2022.3226860","article-title":"Physics-informed deep learning for musculoskeletal modeling: Predicting muscle forces and joint kinematics from surface EMG","volume":"31","author":"Zhang J","year":"2022","unstructured":"Zhang J, Zhao Y, Shone F, Li Z, Frangi AF, Xie SQ, Zhang ZQ. Physics-informed deep learning for musculoskeletal modeling: Predicting muscle forces and joint kinematics from surface EMG. IEEE Trans Neural Syst Rehabil Eng. 2022;31:484\u2013493.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"e_1_3_4_24_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108016","article-title":"Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors","volume":"170","author":"Xiang L","year":"2024","unstructured":"Xiang L, Gu Y, Gao Z, Yu P, Shim V, Wang A, Fernandez J. Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors. Comput Biol Med. 2024;170: Article 108016.","journal-title":"Comput Biol Med"},{"key":"e_1_3_4_25_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106099","article-title":"Prediction on the medial knee contact force in patients with knee valgus using transfer learning approaches: Application to rehabilitation gaits","volume":"150","author":"Zou J","year":"2022","unstructured":"Zou J, Zhang X, Zhang Y, Li J, Jin Z. Prediction on the medial knee contact force in patients with knee valgus using transfer learning approaches: Application to rehabilitation gaits. Comput Biol Med. 2022;150: Article 106099.","journal-title":"Comput Biol Med"},{"key":"e_1_3_4_26_2","article-title":"Dual transformer network for predicting joint angles and torques from multi-channel EMG signals in the lower limbs","author":"Wang Z","year":"2025","unstructured":"Wang Z, Chen C, Chen H, Zhou Y, Wang X, Wu X. Dual transformer network for predicting joint angles and torques from multi-channel EMG signals in the lower limbs. IEEE J Biomed Health Inform. 2025; doi: 10.1109\/JBHI.2025.3555255.","journal-title":"IEEE J Biomed Health Inform"},{"key":"e_1_3_4_27_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102781","article-title":"sEMG-based consecutive estimation of human lower limb movement by using multi-branch neural network","volume":"68","author":"Wang X","year":"2021","unstructured":"Wang X, Dong D, Chi X, Wang S, Miao Y, Gavrilov AI. sEMG-based consecutive estimation of human lower limb movement by using multi-branch neural network. Biomed Signal Process Contr. 2021;68: Article 102781.","journal-title":"Biomed Signal Process Contr"},{"issue":"1","key":"e_1_3_4_28_2","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/s41597-023-02840-6","article-title":"A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities","volume":"10","author":"Scherpereel K","year":"2023","unstructured":"Scherpereel K, Molinaro D, Inan O, Shepherd M, Young A. A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities. Sci Data. 2023;10(1):924.","journal-title":"Sci Data"},{"key":"e_1_3_4_29_2","unstructured":"Thelen DG Seth A Anderson FC Delp SL. Gait 2392 and 2354 Models. OpenSim Documentation. 2013. https:\/\/simtk-confluence.stanford.edu:8443\/display\/OpenSim\/ Gait+2392+and+2354+Models"},{"key":"e_1_3_4_30_2","unstructured":"Nie Y Nguyen NH Sinthong P Kalagnanam J. A time series is worth 64 words: Long-term forecasting with transformers. arXiv. 2022. https:\/\/doi.org\/10.48550\/arXiv.2211.14730"},{"key":"e_1_3_4_31_2","doi-asserted-by":"crossref","unstructured":"Zhou H Zhang S Peng J Zhang S Li J Xiong H Zhang W. Informer: Beyond efficient transformer for long sequence time series forecasting. In: Proceedings of the AAAI conference on artificial intelligence. Vancouver (British Columbia Canada): AAAI; 2021. Vol. 35 p. 11106\u201311115.","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_4_32_2","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani A","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. Attention is all you need. Adv Neural Inf Proces Syst. 2017;30:6000\u20136010.","journal-title":"Adv Neural Inf Proces Syst"},{"key":"e_1_3_4_33_2","unstructured":"Liu Y Hu T Zhang H Wu H Wang S Ma L Long M. itransformer: Inverted transformers are effective for time series forecasting. arXiv. 2023. https:\/\/doi.org\/10.48550\/arXiv.2310.06625"},{"key":"e_1_3_4_34_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110925","article-title":"GraphMLP: A graph MLP-like architecture for 3D human pose estimation","volume":"158","author":"Li W","year":"2025","unstructured":"Li W, Liu M, Liu H, Guo T, Wang T, Tang H, Sebe N. GraphMLP: A graph MLP-like architecture for 3D human pose estimation. Pattern Recogn. 2025;158: Article 110925.","journal-title":"Pattern Recogn"},{"key":"e_1_3_4_35_2","first-page":"24261","article-title":"Mlp-mixer: An all-mlp architecture for vision","volume":"34","author":"Tolstikhin IO","year":"2021","unstructured":"Tolstikhin IO, Houlsby N, Kolesnikov A, Beyer L, Zhai X, Unterthiner T, Yung J, Stener A, Keysers D, Uszkoreit J, et al. Mlp-mixer: An all-mlp architecture for vision. Adv Neural Inf Proces Syst. 2021;34:24261\u201324272.","journal-title":"Adv Neural Inf Proces Syst"},{"key":"e_1_3_4_36_2","unstructured":"Das A Kong W Leach A Mathur S Sen R Yu R. Long-term forecasting with tide: Time-series dense encoder. arXiv. 2023. https:\/\/doi.org\/10.48550\/arXiv.2304.08424"},{"key":"e_1_3_4_37_2","doi-asserted-by":"crossref","unstructured":"Zeng A Chen M Zhang L Xu Q. Are transformers effective for time series forecasting? In: Proceedings of the AAAI conference on artificial intelligence. Washington (DC): AAAI; 2023. Vol. 37 p. 11121\u201311128.","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"e_1_3_4_38_2","unstructured":"Hendrycks D Gimpel K. Gaussian error linear units (GELUs). arXiv. 2016. https:\/\/doi.org\/10.48550\/arXiv.1606.08415"},{"key":"e_1_3_4_39_2","unstructured":"Glorot X Bordes A Bengio Y. Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings. Fort Lauderdale (FL): JMLR; 2011. p. 315\u2013323."},{"key":"e_1_3_4_40_2","unstructured":"Wu H Hu T Liu Y Zhou H Wang J Long M. TimesNet: Temporal 2d-variation modeling for general time series analysis. arXiv. 2022. https:\/\/doi.org\/10.48550\/arXiv.2210.02186."},{"key":"e_1_3_4_41_2","unstructured":"Zhang Y Yan J. Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In: The Eleventh International Conference on Learning Representations. Kigali (Rwanda): OpenReview; 2023."},{"key":"e_1_3_4_42_2","unstructured":"Ying Z Bourgeois D You J Zitnik M Leskovec J. GNNExplainer: Generating explanations for graph neural networks. Adv Neural Inf Process Syst. 2019;32:9240\u20139251."},{"key":"e_1_3_4_43_2","doi-asserted-by":"crossref","unstructured":"Zhao L Peng X Tian Y Kapadia M Metaxas DN. Semantic graph convolutional networks for 3D human pose regression. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. IEEE; 2019. p. 3425\u20133435.","DOI":"10.1109\/CVPR.2019.00354"},{"key":"e_1_3_4_44_2","doi-asserted-by":"crossref","unstructured":"Liu K Ding R Zou Z Wang L Tang W. A comprehensive study of weight sharing in graph networks for 3D human pose estimation. In: Computer Vision\u2013ECCV 2020: 16th European Conference Glasgow UK August 23\u201328 2020 Proceedings Part X 16. Springer; 2020. p. 318\u2013334.","DOI":"10.1007\/978-3-030-58607-2_19"},{"key":"e_1_3_4_45_2","doi-asserted-by":"crossref","unstructured":"Ci H Wang C Ma X Wang Y. Optimizing network structure for 3D human pose estimation. In: Proceedings of the IEEE\/CVF international conference on computer vision. IEEE; 2019. p. 2262\u20132271.","DOI":"10.1109\/ICCV.2019.00235"},{"key":"e_1_3_4_46_2","unstructured":"Gambietz M Dorschky E Akat A Sch\u00f6ckel M Miehling J Koelewijn AD. SSPIN Npose: A self-supervised PINN for inertial pose and dynamics estimation. arXiv. 2025. https:\/\/doi.org\/10.48550\/arXiv.2506.11786"},{"issue":"1","key":"e_1_3_4_47_2","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1186\/s12984-024-01458-y","article-title":"Multibody dynamics-based musculoskeletal modeling for gait analysis: A systematic review","volume":"21","author":"Abdullah M","year":"2024","unstructured":"Abdullah M, Hulleck AA, Katmah R, Khalaf K, El-Rich M. Multibody dynamics-based musculoskeletal modeling for gait analysis: A systematic review. J Neuroeng Rehabil. 2024;21(1):178.","journal-title":"J Neuroeng Rehabil"},{"issue":"2","key":"e_1_3_4_48_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.birob.2023.100097","article-title":"IMU-based motion capture system for rehabilitation applications: A systematic review","volume":"3","author":"Gu C","year":"2023","unstructured":"Gu C, Lin W, He X, Zhang L, Zhang M. IMU-based motion capture system for rehabilitation applications: A systematic review. Biomimetic Intell Robot. 2023;3(2): Article 100097.","journal-title":"Biomimetic Intell Robot"},{"issue":"23","key":"e_1_3_4_49_2","doi-asserted-by":"crossref","first-page":"6933","DOI":"10.3390\/s20236933","article-title":"Real-time prediction of joint forces by motion capture and machine learning","volume":"20","author":"Giarmatzis G","year":"2020","unstructured":"Giarmatzis G, Zacharaki EI, Moustakas K. Real-time prediction of joint forces by motion capture and machine learning. Sensors. 2020;20(23):6933.","journal-title":"Sensors"},{"key":"e_1_3_4_50_2","doi-asserted-by":"crossref","first-page":"3841","DOI":"10.1007\/s11517-024-03171-3","article-title":"Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces","volume":"62","author":"Peng Y","year":"2024","unstructured":"Peng Y, Wang W, Wang L, Zhou H, Chen Z, Zhang Q, Li G. Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces. Med Biol Eng Comput. 2024;62:3841\u20133853.","journal-title":"Med Biol Eng Comput"},{"issue":"4","key":"e_1_3_4_51_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.birob.2025.100246","article-title":"Joint moment estimation for hip exoskeleton control: A generalized moment feature generation method","volume":"5","author":"Zhang Y","year":"2025","unstructured":"Zhang Y, Xiong J, Xian H, Chen C, Chen X, Liang H, Fu C, Leng Y. Joint moment estimation for hip exoskeleton control: A generalized moment feature generation method. Biomimetic Intell Robot. 2025;5(4): Article 100246.","journal-title":"Biomimetic Intell Robot"},{"key":"e_1_3_4_52_2","doi-asserted-by":"crossref","first-page":"04016161","DOI":"10.1061\/(ASCE)ST.1943-541X.0001649","article-title":"Measuring ground reaction force and quantifying variability in jumping and bobbing actions","volume":"143","author":"McDonald MG","year":"2017","unstructured":"McDonald MG, \u017divanovi\u0107 S. Measuring ground reaction force and quantifying variability in jumping and bobbing actions. J Struct Eng. 2017;143:04016161.","journal-title":"J Struct Eng"},{"key":"e_1_3_4_53_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.humov.2025.103322","article-title":"Inter-subject variability in muscle synergies during squatting movements","volume":"99","author":"Kim Y","year":"2025","unstructured":"Kim Y. Inter-subject variability in muscle synergies during squatting movements. Hum Mov Sci. 2025;99: Article 103322.","journal-title":"Hum Mov Sci"},{"issue":"10","key":"e_1_3_4_54_2","doi-asserted-by":"crossref","first-page":"2762","DOI":"10.1519\/JSC.0b013e3181cc2408","article-title":"Bilateral contact ground reaction forces and contact times during plyometric drop jumping","volume":"24","author":"Ball NB","year":"2010","unstructured":"Ball NB, Stock CG, Scurr JC. Bilateral contact ground reaction forces and contact times during plyometric drop jumping. J Strength Cond Res. 2010;24(10):2762\u20132769.","journal-title":"J Strength Cond Res"},{"key":"e_1_3_4_55_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.105005","article-title":"A spatiotemporal motion prediction network based on multi-level feature disentanglement","volume":"146","author":"Chen S","year":"2024","unstructured":"Chen S, Bo Y, Wu X. A spatiotemporal motion prediction network based on multi-level feature disentanglement. Image Vis Comput. 2024;146: Article 105005.","journal-title":"Image Vis Comput"},{"key":"e_1_3_4_56_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.127864","article-title":"Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework","volume":"278","author":"Tang Y","year":"2023","unstructured":"Tang Y, Yang K, Zhang S, Zhang Z. Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework. Energy. 2023;278: Article 127864.","journal-title":"Energy"},{"key":"e_1_3_4_57_2","doi-asserted-by":"crossref","unstructured":"Wen Q Zhou T Zhang C Chen W Ma Z Yan J Sun L. Transformers in time series: A survey. arXiv. 2022. https:\/\/doi.org\/10.48550\/arXiv.2202.07125","DOI":"10.24963\/ijcai.2023\/759"},{"issue":"5","key":"e_1_3_4_58_2","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.26603\/001c.88003","article-title":"Reliability and concurrent validity of a markerless, single camera, portable\u00a03D motion capture system for assessment of glenohumeral mobility","volume":"18","author":"Pottorf O","year":"2023","unstructured":"Pottorf O, Vapne D, Ghigiarelli J, Haase K. Reliability and concurrent validity of a markerless, single camera, portable\u00a03D motion capture system for assessment of glenohumeral mobility. Int J Sports Phys Ther. 2023;18(5):1176\u20131185.","journal-title":"Int J Sports Phys Ther"},{"issue":"1","key":"e_1_3_4_59_2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s12984-023-01186-9","article-title":"A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation","volume":"20","author":"Lam WW","year":"2023","unstructured":"Lam WW, Tang YM, Fong KN. A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation. J Neuroeng Rehabil. 2023;20(1):57.","journal-title":"J Neuroeng Rehabil"},{"key":"e_1_3_4_60_2","doi-asserted-by":"crossref","unstructured":"Tang Z Qiu Z Hao Y Hong R Yao T. 3D human pose estimation with spatio-temporal criss-cross attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2023. p. 4790\u20134799.","DOI":"10.1109\/CVPR52729.2023.00464"}],"container-title":["Cyborg and Bionic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/spj.science.org\/doi\/pdf\/10.34133\/cbsystems.0476","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T08:00:46Z","timestamp":1768464046000},"score":1,"resource":{"primary":{"URL":"https:\/\/spj.science.org\/doi\/10.34133\/cbsystems.0476"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":59,"alternative-id":["10.34133\/cbsystems.0476"],"URL":"https:\/\/doi.org\/10.34133\/cbsystems.0476","relation":{},"ISSN":["2692-7632"],"issn-type":[{"value":"2692-7632","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-07-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-27","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-10","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"0476"}}