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Some traditional methods, such as the inverse dynamics model and the electromyography (EMG)-driven neuromusculoskeletal (NMS) model, depend on ground reaction force (GRF) measurements and involve complex optimization solution processes, respectively. Recently, machine learning methods have been popularly used to predict joint torque with surface electromyography (sEMG) signals and kinematic information as inputs. This study aims to predict lower limb joint torque in the sagittal plane during walking, using a long short-term memory (LSTM) model and Gaussian process regression (GPR) model, respectively, with seven characteristics extracted from the sEMG signals of five muscles and three joint angles as inputs. The majority of the normalized root mean squared error (NRMSE) values in both models are below 15%, most Pearson correlation coefficient (R) values exceed 0.85, and most decisive factor (R2) values surpass 0.75. These results indicate that the joint prediction of torque is feasible using machine learning methods with sEMG signals and joint angles as inputs.<\/jats:p>","DOI":"10.3390\/s23239576","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T13:45:49Z","timestamp":1701524749000},"page":"9576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Lower Limb Joint Torque Prediction Using Long Short-Term Memory Network and Gaussian Process Regression"],"prefix":"10.3390","volume":"23","author":[{"given":"Mengsi","family":"Wang","sequence":"first","affiliation":[{"name":"School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenlei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6139-0292","authenticated-orcid":false,"given":"Jiyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinglong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0522-1243","authenticated-orcid":false,"given":"Qing","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1053\/apmr.2000.7174","article-title":"The influence of lower extremity joint torque on gait characteristics in elderly men","volume":"81","author":"Burnfield","year":"2000","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1053\/apmr.2002.33225","article-title":"Effectiveness of a lateral-wedge insole on knee varus torque in patients with knee osteoarthritis","volume":"83","author":"Kerrigan","year":"2002","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s12555-020-0632-1","article-title":"Model identification and human-robot coupling control of lower limb exoskeleton with biogeography-based learning particle swarm optimization","volume":"20","author":"Guo","year":"2022","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8693","DOI":"10.1109\/TNNLS.2022.3152255","article-title":"Gait prediction and variable admittance control for lower limb exoskeleton with measurement delay and extended-state-observer","volume":"34","author":"Chen","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1109\/TMECH.2023.3235054","article-title":"Output Constrained Control of Lower Limb Exoskeleton Based on Knee Motion Probabilistic Model With Finite-Time Extended State Observer","volume":"28","author":"Chen","year":"2023","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1109\/TMECH.2019.2893055","article-title":"A practical and adaptive method to achieve EMG-based torque estimation for a robotic exoskeleton","volume":"24","author":"Gui","year":"2019","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Winter, D.A. (2009). Biomechanics and Motor Control of Human Movement, John Wiley & Sons.","DOI":"10.1002\/9780470549148"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/S0966-6362(02)00068-1","article-title":"Biomechanics and muscle coordination of human walking: Part I: Introduction to concepts, power transfer, dynamics and simulations","volume":"16","author":"Zajac","year":"2002","journal-title":"Gait Posture"},{"key":"ref_9","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."},{"key":"ref_10","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":"ref_11","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1123\/jab.20.4.367","article-title":"Neuromusculoskeletal modeling: Estimation of muscle forces and joint moments and movements from measurements of neural command","volume":"20","author":"Buchanan","year":"2004","journal-title":"J. Appl. Biomech."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1140\/epjp\/s13360-023-03708-9","article-title":"Bifurcation control of a fractional-order PD control strategy for a delayed fractional-order prey\u2013predator system","volume":"138","author":"Lu","year":"2023","journal-title":"Eur. Phys. J. Plus"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6125","DOI":"10.1007\/s11063-022-11130-y","article-title":"Bifurcation mechanism for fractional-order three-triangle multi-delayed neural networks","volume":"55","author":"Xu","year":"2022","journal-title":"Neural Process. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"18878","DOI":"10.1002\/mma.9597","article-title":"Novel extended mixed controller design for bifurcation control of fractional-order Myc\/E2F\/miR-17-92 network model concerning delay","volume":"46","author":"Li","year":"2023","journal-title":"Math. Methods Appl. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shi, Y., Dong, W., Lin, W., He, L., Wang, X., Li, P., and Gao, Y. (2022). Human Joint Torque Estimation Based on Mechanomyography for Upper Extremity Exosuit. Electronics, 11.","DOI":"10.3390\/electronics11091335"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Al-Timemy, A.H., Zonnino, A., and Sergi, F. (December, January 29). Estimating wrist joint torque using regression ensemble of bagged trees under multiple wrist postures. Proceedings of the 2020 8th IEEE RAS\/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA.","DOI":"10.1109\/BioRob49111.2020.9224456"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Molinaro, D.D., Kang, I., Camargo, J., and Young, A.J. (December, January 29). Biological hip torque estimation using a robotic hip exoskeleton. Proceedings of the 2020 8th IEEE RAS\/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA.","DOI":"10.1109\/BioRob49111.2020.9224334"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Song, Q., Sun, B., Lei, J., Gao, Z., Yu, Y., Liu, M., and Ge, Y. (2006, January 20\u201323). Prediction of human elbow torque from EMG using SVM based on AWR information acquisition platform. Proceedings of the 2006 IEEE International Conference on Information Acquisition, Veihai, China.","DOI":"10.1109\/ICIA.2006.305933"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Anwar, T., and Al Jumaily, A. (2016, January 4\u20137). EMG signal based knee joint torque estimation. Proceedings of the 2016 International Conference on Systems in Medicine and Biology (ICSMB), Kharagpur, India.","DOI":"10.1109\/ICSMB.2016.7915117"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4257","DOI":"10.1007\/s12206-022-0743-0","article-title":"Identification method of nonlinear maneuver model for unmanned surface vehicle from sea trial data based on support vector machine","volume":"36","author":"Wu","year":"2022","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/TASE.2020.3033664","article-title":"Ankle joint torque estimation using an EMG-driven neuromusculoskeletal model and an artificial neural network model","volume":"18","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Peng, L., Hou, Z.G., and Wang, W. (2015, January 25\u201329). A dynamic EMG-torque model of elbow based on neural networks. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7318986"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, X., Lu, Z., Jiang, Z., and Zhang, T. (2020, January 10\u201313). A novel wrist joint torque prediction method based on EMG and LSTM. Proceedings of the 2020 10th Institute of Electrical and Electronics Engineers International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Xi\u2019an, China.","DOI":"10.1109\/CYBER50695.2020.9279119"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106107","DOI":"10.1016\/j.engappai.2023.106107","article-title":"Using Gaussian Process Regression (GPR) models with the Mat\u00e9rn covariance function to predict the dynamic viscosity and torque of SiO2\/Ethylene glycol nanofluid: A machine learning approach","volume":"122","author":"Dai","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nguyen-Tuong, D., Seeger, M., and Peters, J. (2008, January 11\u201313). Computed torque control with nonparametric regression models. Proceedings of the 2008 American Control Conference, Seattle, WA, USA.","DOI":"10.1109\/ACC.2008.4586493"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.neucom.2018.11.086","article-title":"A Gaussian process regression based on variable parameters fuzzy dominance genetic algorithm for B-TFPMM torque estimation","volume":"335","author":"Pei","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/TNSRE.2021.3104761","article-title":"A neural network estimation of ankle torques from electromyography and accelerometry","volume":"29","author":"Siu","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3572","DOI":"10.1109\/TNNLS.2018.2854699","article-title":"Neural adaptive backstepping control of a robotic manipulator with prescribed performance constraint","volume":"30","author":"Guo","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106869","DOI":"10.1016\/j.ymssp.2020.106869","article-title":"Neural adaptive control of single-rod electrohydraulic system with lumped uncertainty","volume":"146","author":"Guo","year":"2021","journal-title":"Mech. Syst. Signal Proc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TNSRE.2022.3156786","article-title":"Lower-limb joint torque prediction using LSTM neural networks and transfer learning","volume":"30","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Truong, M.T.N., Ali, A.E.A., Owaki, D., and Hayashibe, M. (2023). EMG-Based Estimation of Lower Limb Joint Angles and Moments Using Long Short-Term Memory Network. Sensors, 23.","DOI":"10.3390\/s23063331"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, J., and Yin, Y. (2020). Dependent-Gaussian-process-based learning of joint torques using wearable smart shoes for exoskeleton. Sensors, 20.","DOI":"10.3390\/s20133685"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ullauri, J.B., Peternel, L., Ugurlu, B., Yamada, Y., and Morimoto, J. (2015, January 27\u201331). On the EMG-based torque estimation for humans coupled with a force-controlled elbow exoskeleton. Proceedings of the 2015 International Conference on Advanced Robotics (ICAR), Istanbul, Turkey.","DOI":"10.1109\/ICAR.2015.7251472"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, M., Chen, Z., Zhan, H., Zhang, J., Wu, X., Jiang, D., and Guo, Q. (2023, January 8\u201310). Lower limb joint torque estimation by neural network and Sparse Gaussian Process with RIO Kernel. Proceedings of the 2008 8th International Conference on Advanced Robotics and Mechatronics, Sanya, China.","DOI":"10.1109\/ICARM58088.2023.10218774"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1038\/s41597-021-00881-3","article-title":"Lower limb kinematic, kinetic, and EMG data from young healthy humans during walking at controlled speeds","volume":"8","author":"Moreira","year":"2021","journal-title":"Sci. Data"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9013","DOI":"10.1007\/s00521-019-04147-3","article-title":"A novel feature extraction method for machine learning based on surface electromyography from healthy brain","volume":"31","author":"Li","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8783","DOI":"10.1109\/ACCESS.2020.2964678","article-title":"A study of computing zero crossing methods and an improved proposal for EMG signals","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bhattacharya, A., Sarkar, A., and Basak, P. (2017, January 6\u20137). Time domain multi-feature extraction and classification of human hand movements using surface EMG. Proceedings of the 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS.2017.8014594"},{"key":"ref_39","first-page":"51","article-title":"Mean and median frequency of EMG signal to determine muscle force based on time-dependent power spectrum","volume":"19","author":"Thongpanja","year":"2013","journal-title":"Elektron. Elektrotechnika"},{"key":"ref_40","unstructured":"Hochreiter, S., and Schmidhuber, J. (1996, January 3\u20135). LSTM can solve hard long time lag problems. Proceedings of the 9th International Conference on Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: Continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/TII.2021.3081531","article-title":"Robust deep Gaussian process-based probabilistic electrical load forecasting against anomalous events","volume":"18","author":"Cao","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_43","unstructured":"Qiu, X., Meyerson, E., and Miikkulainen, R. (2019). Quantifying point-prediction uncertainty in neural networks via residual estimation with an i\/o kernel. arXiv."},{"key":"ref_44","unstructured":"Titsias, M. (2009, January 16\u201318). Variational learning of inducing variables in sparse Gaussian processes. Proceedings of the Artificial Intelligence and Statistics, PMLR, Clearwater Beach, FL, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K. (2006). Gaussian Processes for Machine Learning, Springer.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1109\/TASLP.2019.2905167","article-title":"Statistical parametric speech synthesis using deep Gaussian processes","volume":"27","author":"Koriyama","year":"2019","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9576\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:36:38Z","timestamp":1760132198000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":46,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23239576"],"URL":"https:\/\/doi.org\/10.3390\/s23239576","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]}}}