{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:14:23Z","timestamp":1772302463249,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61903062"],"award-info":[{"award-number":["61903062"]}]},{"name":"National Natural Science Foundation of China","award":["62272081"],"award-info":[{"award-number":["62272081"]}]},{"name":"National Natural Science Foundation of China","award":["2021-MS-111"],"award-info":[{"award-number":["2021-MS-111"]}]},{"name":"National Natural Science Foundation of China","award":["DUT22YG240"],"award-info":[{"award-number":["DUT22YG240"]}]},{"name":"National Natural Science Foundation of China","award":["DUT22YG128"],"award-info":[{"award-number":["DUT22YG128"]}]},{"name":"Natural Science Foundation of Liaoning Province, China","award":["61903062"],"award-info":[{"award-number":["61903062"]}]},{"name":"Natural Science Foundation of Liaoning Province, China","award":["62272081"],"award-info":[{"award-number":["62272081"]}]},{"name":"Natural Science Foundation of Liaoning Province, China","award":["2021-MS-111"],"award-info":[{"award-number":["2021-MS-111"]}]},{"name":"Natural Science Foundation of Liaoning Province, China","award":["DUT22YG240"],"award-info":[{"award-number":["DUT22YG240"]}]},{"name":"Natural Science Foundation of Liaoning Province, China","award":["DUT22YG128"],"award-info":[{"award-number":["DUT22YG128"]}]},{"name":"Fundamental Research Funds for the Central Universities, China","award":["61903062"],"award-info":[{"award-number":["61903062"]}]},{"name":"Fundamental Research Funds for the Central Universities, China","award":["62272081"],"award-info":[{"award-number":["62272081"]}]},{"name":"Fundamental Research Funds for the Central Universities, China","award":["2021-MS-111"],"award-info":[{"award-number":["2021-MS-111"]}]},{"name":"Fundamental Research Funds for the Central Universities, China","award":["DUT22YG240"],"award-info":[{"award-number":["DUT22YG240"]}]},{"name":"Fundamental Research Funds for the Central Universities, China","award":["DUT22YG128"],"award-info":[{"award-number":["DUT22YG128"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable exoskeletons can help people with mobility impairments by improving their rehabilitation. As electromyography (EMG) signals occur before movement, they can be used as input signals for the exoskeletons to predict the body\u2019s movement intention. In this paper, the OpenSim software is used to determine the muscle sites to be measured, i.e., rectus femoris, vastus lateralis, semitendinosus, biceps femoris, lateral gastrocnemius, and tibial anterior. The surface electromyography (sEMG) signals and inertial data are collected from the lower limbs while the human body is walking, going upstairs, and going uphill. The sEMG noise is reduced by a wavelet-threshold-based complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) reduction algorithm, and the time-domain features are extracted from the noise-reduced sEMG signals. Knee and hip angles during motion are calculated using quaternions through coordinate transformations. The random forest (RF) regression algorithm optimized by cuckoo search (CS), shortened as CS-RF, is used to establish the prediction model of lower limb joint angles by sEMG signals. Finally, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) are used as evaluation metrics to compare the prediction performance of the RF, support vector machine (SVM), back propagation (BP) neural network, and CS-RF. The evaluation results of CS-RF are superior to other algorithms under the three motion scenarios, with optimal metric values of 1.9167, 1.3893, and 0.9815, respectively.<\/jats:p>","DOI":"10.3390\/s23125404","type":"journal-article","created":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T02:02:28Z","timestamp":1686189748000},"page":"5404","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Prediction of Joint Angles Based on Human Lower Limb Surface Electromyography"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1510-5289","authenticated-orcid":false,"given":"Hongyu","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China"},{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhibo","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China"},{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daoyong","family":"Peng","sequence":"additional","affiliation":[{"name":"Neurology Department, Dalian Municipal Central Hospital, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Wang","sequence":"additional","affiliation":[{"name":"Neurology Department, Dalian Municipal Central Hospital, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhelong","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China"},{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6846-546X","authenticated-orcid":false,"given":"Sen","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China"},{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Shi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China"},{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghao","family":"Chu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China"},{"name":"School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, D.X., Wu, X., Du, W., Wang, C., and Xu, T. (2016). Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors. Sensors, 16.","DOI":"10.3390\/s16101579"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1080\/00140139.2015.1081988","article-title":"Exoskeletons for industrial application and their potential effects on physical work load","volume":"59","author":"Bosch","year":"2016","journal-title":"Ergonomics"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/TRO.2008.915453","article-title":"Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art","volume":"24","author":"Dollar","year":"2008","journal-title":"IEEE Trans. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"041004","DOI":"10.1088\/1741-2552\/ac1176","article-title":"Myoelectric control of robotic lower limb prostheses: A review of electromyography interfaces, control paradigms, challenges and future directions","volume":"18","author":"Fleming","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.inffus.2021.11.006","article-title":"Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges","volume":"80","author":"Qiu","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.inffus.2019.03.002","article-title":"Adaptive gait detection based on foot-mounted inertial sensors and multi-sensor fusion","volume":"52","author":"Zhao","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.inffus.2022.10.003","article-title":"Analysis and Evaluation of Hemiplegic Gait Based on Wearable Sensor Network","volume":"90","author":"Zhao","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Qiu, S., Wang, H., Li, J., Zhao, H., Wang, Z., Wang, J., Wang, Q., Plettemeier, D., B\u00e4rhold, M., and Bauer, T. (2020). Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits. Sensors, 20.","DOI":"10.3390\/s20041193"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4190","DOI":"10.1109\/JIOT.2021.3102856","article-title":"Sensor Combination Selection Strategy for Kayak Cycle Phase Segmentation Based on Body Sensor Networks","volume":"9","author":"Qiu","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102074","DOI":"10.1016\/j.bspc.2020.102074","article-title":"A review of the key technologies for sEMG-based human-robot interaction systems","volume":"62","author":"Li","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1251\/bpo115","article-title":"Techniques of EMG signal analysis: Detection, processing, classification and applications","volume":"8","author":"Reaz","year":"2006","journal-title":"Biol. Proced. Online"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/TASE.2020.2993399","article-title":"Environmental Context Prediction for Lower Limb Prostheses with Uncertainty Quantification","volume":"18","author":"Zhong","year":"2021","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_13","unstructured":"Wan, D., Zhang, L., Bai, Y., and Xie, Y. (2020, January 24\u201325). Research on Identification Algorithm Based on ECG Signal and Improved Convolutional Neural Network. Proceedings of the International Conference on Computer Big Data and Artificial Intelligence, Changsha, China."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Laport, F., Iglesia, D., Dapena, A., Castro, P.M., and Vazquez-Araujo, F.J. (2021). Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces. Sensors, 21.","DOI":"10.3390\/s21062220"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4172\/2168-9695.1000e107","article-title":"EMG-based Robot Control Interfaces: Past, Present and Future","volume":"1","author":"Artemiadis","year":"2012","journal-title":"Adv. Robot. Autom."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Coker, J., Chen, H., Schall Jr, M.C., Gallagher, S., and Zabala, M. (2021). EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee. Sensors, 21.","DOI":"10.3390\/s21113622"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s12984-022-01019-1","article-title":"EMG-Driven Control in Lower LimbProstheses: A Topic-Based Systematic Review","volume":"19","author":"Cimolato","year":"2022","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1007\/s12541-017-0202-5","article-title":"Evaluating the performance of Kalman filter on elbow joint angle prediction based on electromyography","volume":"18","author":"Triwiyanto","year":"2017","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"685596","DOI":"10.3389\/fpubh.2021.685596","article-title":"Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals","volume":"9","author":"Liang","year":"2021","journal-title":"Front. Public Health"},{"key":"ref_20","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"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"515","DOI":"10.3233\/BMR-160525","article-title":"Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters","volume":"30","author":"Raj","year":"2017","journal-title":"J. Back Musculoskelet. Rehabil."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2100310","DOI":"10.1109\/JTEHM.2020.2972523","article-title":"MyoNet: A Transfer-Learning-Based LRCN for Lower Limb Movement Recognition and Knee Joint Angle Prediction for Remote Monitoring of Rehabilitation Progress From sEMG","volume":"8","author":"Gautam","year":"2020","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Seth, A., Hicks, J.L., Uchida, T.K., Habib, A., Dembia, C.L., Dunne, J.J., Ong, C.F., Demers, M.S., Rajagopal, A., and Millard, M. (2018). OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput. Biol., 14.","DOI":"10.1371\/journal.pcbi.1006223"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ai, Q., Zhang, Y., Qi, W., Liu, Q., and Chen, K. (2017). Research on Lower Limb Motion Recognition Based on Fusion of sEMG and Accelerometer Signals. Symmetry, 9.","DOI":"10.3390\/sym9080147"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bisi, S., De Luca, L., Shrestha, B., Yang, Z., and Gandhi, V. (2018). Development of an EMG-Controlled Mobile Robot. Robotics, 7.","DOI":"10.3390\/robotics7030036"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.bspc.2014.06.009","article-title":"Improved complete ensemble EMD: A suitable tool for biomedical signal processing","volume":"14","author":"Colominas","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2050039","DOI":"10.1142\/S021947752050039X","article-title":"Noise Removal from EMG Signal Using Adaptive Enhanced Squirrel Search Algorithm","volume":"19","author":"Nagasirisha","year":"2020","journal-title":"Fluct. Noise Lett."},{"key":"ref_28","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."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1007\/s12541-014-0580-x","article-title":"Human shoulder motion extraction using EMG signals","volume":"15","author":"Jang","year":"2014","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s00521-013-1367-1","article-title":"Cuckoo search: Recent advances and applications","volume":"24","author":"Yang","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.ipm.2017.02.004","article-title":"Twitter sentiment analysis using hybrid cuckoo search method","volume":"53","author":"Pandey","year":"2017","journal-title":"Inf. Process. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104936","DOI":"10.1016\/j.bspc.2023.104936","article-title":"Decoding transient sEMG data for intent motion recognition in transhumeral amputees","volume":"85","author":"Tigrini","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e623","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"PeerJ Comput. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5404\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:50:01Z","timestamp":1760125801000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5404"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,7]]},"references-count":33,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23125404"],"URL":"https:\/\/doi.org\/10.3390\/s23125404","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,7]]}}}