{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:09:30Z","timestamp":1772302170618,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T00:00:00Z","timestamp":1705708800000},"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":["51865056"],"award-info":[{"award-number":["51865056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human\u2013machine interface technology is fundamentally constrained by the dexterity of motion decoding. Simultaneous and proportional control can greatly improve the flexibility and dexterity of smart prostheses. In this research, a new model using ensemble learning to solve the angle decoding problem is proposed. Ultimately, seven models for angle decoding from surface electromyography (sEMG) signals are designed. The kinematics of five angles of the metacarpophalangeal (MCP) joints are estimated using the sEMG recorded during functional tasks. The estimation performance was evaluated through the Pearson correlation coefficient (CC). In this research, the comprehensive model, which combines CatBoost and LightGBM, is the best model for this task, whose average CC value and RMSE are 0.897 and 7.09. The mean of the CC and the mean of the RMSE for all the test scenarios of the subjects\u2019 dataset outperform the results of the Gaussian process model, with significant differences. Moreover, the research proposed a whole pipeline that uses ensemble learning to build a high-performance angle decoding system for the hand motion recognition task. Researchers or engineers in this field can quickly find the most suitable ensemble learning model for angle decoding through this process, with fewer parameters and fewer training data requirements than traditional deep learning models. In conclusion, the proposed ensemble learning approach has the potential for simultaneous and proportional control (SPC) of future hand prostheses.<\/jats:p>","DOI":"10.3390\/s24020660","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T11:36:41Z","timestamp":1705923401000},"page":"660","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Ensemble Learning Method for the Continuous Decoding of Hand Joint Angles"],"prefix":"10.3390","volume":"24","author":[{"given":"Hai","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China"}]},{"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 830017, China"}]},{"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China"},{"name":"Shaanxi Key Laboratory of Intelligent Robot, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.neucom.2021.10.104","article-title":"Modelling EMG driven wrist movements using a bio-inspired neural network (in English)","volume":"470","author":"Fang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yamanoi, Y., Ogiri, Y., and Kato, R. (2020). EMG-based posture classification using a convolutional neural network for a myoelectric hand (in English). Biomed. Signal Processing Control, 55.","DOI":"10.1016\/j.bspc.2019.101574"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/TNSRE.2019.2896269","article-title":"Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning","volume":"27","author":"Fall","year":"2019","journal-title":"Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hu, Y., Wong, Y., Wei, W., Du, Y., Kankanhalli, M., and Geng, W. (2018). A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition. PLoS One., 13.","DOI":"10.1371\/journal.pone.0206049"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, J., Gao, F., Wang, J., Wu, Q., Zhang, Q., and Lin, W.A. (2022). Method Combining Multi-Feature Fusion and Optimized Deep Belief Network for EMG-Based Human Gait Classification. Mathematics, 10.","DOI":"10.3390\/math10224387"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.3934\/mbe.2021177","article-title":"Multi-feature gait recognition with DNN based on sEMG signals. Mathematical Biosciences and Engineering","volume":"18","author":"Yao","year":"2021","journal-title":"Math. Biosci. Engineering"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1109\/TMECH.2020.2999532","article-title":"Simultaneous and Proportional Estimation of Multijoint Kinematics From EMG Signals for Myocontrol of Robotic Hands","volume":"25","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Mechatron."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7217","DOI":"10.1109\/LRA.2021.3097272","article-title":"A Bi-Directional LSTM Network for Estimating Continuous Upper Limb Movement From Surface Electromyography","volume":"6","author":"Ma","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Deng, Y., Gao, F., and Chen, H. (2020). Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm. Symmetry, 12.","DOI":"10.3390\/sym12010130"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3501","DOI":"10.1109\/TBME.2020.2989311","article-title":"Adaptive Real-Time Identification of Motor Unit Discharges From Non-Stationary High-Density Surface Electromyographic Signals","volume":"67","author":"Chen","year":"2020","journal-title":"IEEE Trans. Biomed Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Farina, D., Vujaklija, I., Sartori, M., Kapelner, T., Negro, F., Jiang, N., Bergmeister, K., Andalib, A., Principe, J., and Aszmann, O.C. (2017). Man\/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation. Nat. Biomed. Eng., 1.","DOI":"10.1038\/s41551-016-0025"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ameri, A., Akhaee, M.A., Scheme, E., and Englehart, K. (2018). Real-time, simultaneous myoelectric control using a convolutional neural network. PLoS One., 13.","DOI":"10.1371\/journal.pone.0203835"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"026017","DOI":"10.1088\/1741-2552\/aa9666","article-title":"Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization","volume":"15","author":"Lin","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"036015","DOI":"10.1088\/1741-2552\/ab0e2e","article-title":"Regression convolutional neural network for improved simultaneous EMG control","volume":"16","author":"Ameri","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/TBME.2010.2068298","article-title":"Simultaneous and Proportional Force Estimation for Multifunction Myoelectric Prostheses Using Mirrored Bilateral Training","volume":"58","author":"Nielsen","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","unstructured":"Jiang, N., Nielsen, J., Muceli, S., and Farina, D. (2011, January 4\u20136). EMG-based simultaneous and proportional estimation of wrist kinematics and its application in intuitive myoelectric control for unilateral transradial amputees. In Proceedings of Front. Comput. Neurosci. Conf, BC11: Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting, Freiburg, Germany."},{"key":"ref_17","first-page":"1122","article-title":"Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model","volume":"11","author":"Ngeo","year":"2014","journal-title":"IEEE J. Neuroeng. Rehabil."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1186\/s12984-016-0172-3","article-title":"Proportional estimation of finger movements from high-density surface electromyography","volume":"13","author":"Celadon","year":"2016","journal-title":"J. Neuroeng Rehabil"},{"key":"ref_19","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":"Trans Neural Syst Rehabil Eng."},{"key":"ref_20","first-page":"1","article-title":"A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography","volume":"70","author":"Bao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Quivira, F., Koike-Akino, T., Wang, Y., and Erdogmus, D. (2018, January 4\u20137). Translating sEMG signals to continuous hand poses using recurrent neural networks. In Proceedings of 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Montreal, NV, USA.","DOI":"10.1109\/BHI.2018.8333395"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Koch, P., Dreier, M., Larsen, A., Parbs, T.J., Maass, M., Phan, H., and Mertins, A. (2020, January 20\u201324). Regression of Hand Movements from sEMG Data with Recurrent Neural Networks. In Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176278"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.neucom.2021.05.113","article-title":"Simultaneous estimation of joint angle and interaction force towards sEMG-driven human-robot interaction during constrained tasks","volume":"484","author":"Zhang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mao, H., Zheng, Y., Ma, C., Wu, K., Li, G., and Fang, P. (2023). Simultaneous estimation of grip force and wrist angles by surface electromyography and acceleration signals. Biomed. Signal Processing Control., 79.","DOI":"10.1016\/j.bspc.2022.104088"},{"key":"ref_25","unstructured":"Grinsztajn, L., Oyallon, E., and Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on tabular data?. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/10.204774","article-title":"A new strategy for multifunction myoelectric control","volume":"40","author":"Hudgins","year":"1993","journal-title":"IEEE Trans. Bio-Med. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, H., and Tao, Q. (2023, January 5\u20137). Deep Forest Model Combined with Neural Networks for Finger Joint Continuous Angle Decoding. In Proceedings of Intelligent Robotics and Applications: 16th International Conference, Hangzhou, China.","DOI":"10.1007\/978-981-99-6480-2_45"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201916), New York, NY, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_29","unstructured":"Ke, G.L., Meng, Q., Finley, T., Wang, T.F., Chen, W., Ma, W.D., Ye, Q.W., and Liu, T.Y. (2017, January 4\u20139). Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917), Red Hook, NY, USA."},{"key":"ref_30","unstructured":"Dorogush, A.V., Ershov, V., and Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/660\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:46:19Z","timestamp":1760103979000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/660"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,20]]},"references-count":30,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020660"],"URL":"https:\/\/doi.org\/10.3390\/s24020660","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,20]]}}}