{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:07:55Z","timestamp":1777486075998,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,21]],"date-time":"2024-01-21T00:00:00Z","timestamp":1705795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministerio de Educacion, Cultura y Deporte (Spain)","award":["FPU15\/02870"],"award-info":[{"award-number":["FPU15\/02870"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Electromyography-based wearable biosensors are used for prosthetic control. Machine learning prosthetic controllers are based on classification and regression models. The advantage of the regression approach is that it permits us to obtain a smoother and more natural controller. However, the existing training methods for regression-based solutions is the same as the training protocol used in the classification approach, where only a finite set of movements are trained. In this paper, we present a novel training protocol for myoelectric regression-based solutions that include a feedback term that allows us to explore more than a finite set of movements and is automatically adjusted according to real-time performance of the subject during the training session. Consequently, the algorithm distributes the training time efficiently, focusing on the movements where the performance is worse and optimizing the training for each user. We tested and compared the existing and new training strategies in 20 able-bodied participants and 4 amputees. The results show that the novel training procedure autonomously produces a better training session. As a result, the new controller outperforms the one trained with the existing method: for the able-bodied participants, the average number of targets hit is increased from 86% to 95% and the path efficiency from 40% to 84%, while for the subjects with limb deficiencies, the completion rate is increased from 58% to 69% and the path efficiency from 24% to 56%.<\/jats:p>","DOI":"10.3390\/computers13010029","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T05:42:31Z","timestamp":1705902151000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Interactive Training Model for Myoelectric Regression Control Based on Human\u2013Machine Cooperative Performance"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7416-5313","authenticated-orcid":false,"given":"Carles","family":"Igual","sequence":"first","affiliation":[{"name":"Instituto de Telecomunicaciones y Aplicaciones Multimedia (ITEAM), Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto","family":"Castillo","sequence":"additional","affiliation":[{"name":"Center for Diabetes Tecnology, University of Virginia, Charlottesville, VA 22901, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3408-4014","authenticated-orcid":false,"given":"Jorge","family":"Igual","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunicaciones y Aplicaciones Multimedia (ITEAM), Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wu, Z., Zhao, R., Ni, Y., Jing, X., and Gao, S. (2022). A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human-Machine Interactivities and Biomedical Applications. Biosensors, 12.","DOI":"10.3390\/bios12070516"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Muzumdar, A. (2004). Powered Upper Limb Prostheses: Control, Implementation and Clinical Application, Springer.","DOI":"10.1007\/978-3-642-18812-1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1109\/TNSRE.2012.2196711","article-title":"Control of upper limb prostheses: Terminology and proportional myoelectric control\u2014A review","volume":"20","author":"Fougner","year":"2012","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s12984-018-0361-3","article-title":"Evaluation of EMG pattern recognition for upper limb prosthesis control: A case study in comparison with direct myoelectric control","volume":"15","author":"Resnik","year":"2018","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Merletti, R., and Parker, P.A. (2004). Electromyography: Physiology, Engineering, and Non-Invasive Applications, John Wiley & Sons.","DOI":"10.1002\/0471678384"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/TNSRE.2007.910282","article-title":"An Analysis of EMG Electrode Configuration for Targeted Muscle Reinnervation Based Neural Machine Interface","volume":"16","author":"Huang","year":"2008","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2984","DOI":"10.1007\/s11999-014-3528-7","article-title":"Targeted Muscle Reinnervation: A Novel Approach to Postamputation Neuroma Pain","volume":"472","author":"Souza","year":"2014","journal-title":"Clin. Orthop. Relat. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1038\/nature04970","article-title":"Neuronal ensemble control of prosthetic devices by a human with tetraplegia","volume":"442","author":"Hochberg","year":"2006","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/TNSRE.2013.2294685","article-title":"Demonstration of a Semi-Autonomous Hybrid Brain\u2013Machine Interface Using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic","volume":"22","author":"McMullen","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_10","unstructured":"(2023, December 10). Ottobock Website. Available online: https:\/\/www.ottobock.de."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1109\/TNSRE.2015.2417775","article-title":"High-Density Electromyograph and Motor Skill Learning for Robust Long-Term Control of a 7-DoF Robot Arm","volume":"24","author":"Ison","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Phinyomark, A., Khushaba, R.N., and Scheme, E. (2018). Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors. Sensors, 18.","DOI":"10.3390\/s18051615"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/TNSRE.2007.891391","article-title":"The Optimal Controller Delay for Myoelectric Prostheses","volume":"15","author":"Farrell","year":"2007","journal-title":"IEEE Trans. Neural Syst. Rehabil. 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":"618","DOI":"10.1109\/TNSRE.2015.2401134","article-title":"Concurrent adaptation of human and machine improves simultaneous and proportional myoelectric control","volume":"23","author":"Hahne","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/TNSRE.2019.2894464","article-title":"Adaptive Auto-Regressive Proportional Myoelectric Control","volume":"27","author":"Igual","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s12984-019-0545-5","article-title":"Comparison of vibrotactile and joint-torque feedback in a myoelectric upper-limb prosthesis","volume":"16","author":"Thomas","year":"2019","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1186\/s12984-018-0422-7","article-title":"Myocontrol is closed-loop control: Incidental feedback is sufficient for scaling the prosthesis force in routine grasping","volume":"15","author":"Markovic","year":"2018","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/TNSRE.2015.2413393","article-title":"Detection of and compensation for EMG disturbances for powered lower limb prosthesis control","volume":"24","author":"Spanias","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rahimi, A., Benatti, S., Kanerva, P., Benini, L., and Rabaey, J.M. (2016, January 17\u201319). Hyperdimensional biosignal processing: A case study for EMG-based hand gesture recognition. Proceedings of the 2016 IEEE International Conference on Rebooting Computing (ICRC), San Diego, CA, USA.","DOI":"10.1109\/ICRC.2016.7738683"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.bspc.2014.03.006","article-title":"Real-time, simultaneous myoelectric control using visual target-based training paradigm","volume":"13","author":"Ameri","year":"2014","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, Q., Yang, D., Jiang, L., Zhang, H., Liu, H., Kotani, K., Huang, Q., Yang, D., Jiang, L., and Zhang, H. (2017). A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition. Sensors, 17.","DOI":"10.3390\/s17061370"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1109\/TBME.2012.2232293","article-title":"Classification of Simultaneous Movements using Surface EMG Pattern Recognition","volume":"60","author":"Young","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2575","DOI":"10.1109\/TBME.2016.2641584","article-title":"Interface Prostheses With Classifier-Feedback-Based User Training","volume":"64","author":"Fang","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1109\/TBME.2011.2159216","article-title":"The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift","volume":"58","author":"Young","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1109\/TNSRE.2019.2907200","article-title":"Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning","volume":"27","author":"Prahm","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Amsuess, S., Paredes, L.P., Rudigkeit, N., Graimann, B., Herrmann, M.J., and Farina, D. (2013, January 3\u20137). Long term stability of surface EMG pattern classification for prosthetic control. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610327"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1109\/TNSRE.2022.3163149","article-title":"Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects","volume":"30","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hahne, J.M., Rehbaum, H., Biessmann, F., Meinecke, F.C., M\u00fcller, K.R., Jiang, N., Farina, D., and Parra, L.C. (2012, January 23\u201326). Simultaneous and proportional control of 2D wrist movements with myoelectric signals. Proceedings of the 2012 IEEE International Workshop on Machine Learning for Signal Processing, Santander, Spain.","DOI":"10.1109\/MLSP.2012.6349712"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TNSRE.2014.2305520","article-title":"Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control","volume":"22","author":"Hahne","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Olsson, A.E., Male\u0161evi\u0107, N., Bj\u00f6rkman, A., and Antfolk, C. (2021). Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control. J. Neuroeng. Rehabil., 18.","DOI":"10.1186\/s12984-021-00832-4"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4437","DOI":"10.1038\/s41598-017-04255-x","article-title":"User adaptation in Myoelectric Man-Machine Interfaces","volume":"7","author":"Hahne","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/TBME.2011.2170423","article-title":"Identification of Constant-Posture EMGTorque Relationship About the Elbow Using Nonlinear Dynamic Models","volume":"59","author":"Clancy","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2286","DOI":"10.1109\/TNSRE.2020.3016909","article-title":"Evaluation of a Simultaneous Myoelectric Control Strategy for a Multi-DoF Transradial Prosthesis","volume":"28","author":"Piazza","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Igual, C., Camacho, A., Bernabeu, E.J., and Igual, J. (2020). Donning\/Doffing and Arm Positioning Influence in Upper Limb Adaptive Prostheses Control. Appl. Sci., 10.","DOI":"10.3390\/app10082892"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"eaat3630","DOI":"10.1126\/scirobotics.aat3630","article-title":"Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users","volume":"3","author":"Hahne","year":"2018","journal-title":"Sci. Robot."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.1038\/s41598-018-26810-w","article-title":"Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control","volume":"8","author":"Shehata","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s12984-018-0417-4","article-title":"Improving internal model strength and performance of prosthetic hands using augmented feedback","volume":"15","author":"Shehata","year":"2018","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1109\/TNSRE.2020.3003077","article-title":"Guiding the training of users with a pattern similarity biofeedback to improve the performance of myoelectric pattern recognition","volume":"28","author":"Bailly","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/1\/29\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:46:35Z","timestamp":1760103995000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/1\/29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,21]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["computers13010029"],"URL":"https:\/\/doi.org\/10.3390\/computers13010029","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,21]]}}}