{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:03:22Z","timestamp":1774314202014,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T00:00:00Z","timestamp":1651276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human Machine Interfaces (HMI) principles are for the development of interfaces for assistance or support systems in physiotherapy or rehabilitation processes. One of the main problems is the degree of customization when applying some rehabilitation therapy or when adapting an assistance system to the individual characteristics of the users. To solve this inconvenience, it is proposed to implement a database of surface Electromyography (sEMG) of a channel in healthy individuals for pattern recognition through Neural Networks of contraction in the muscular region of the biceps brachii. Each movement is labeled using the One-Hot Encoding technique, which activates a state machine to control the position of an anthropomorphic manipulator robot and validate the response time of the designed HMI. Preliminary results show that the learning curve decreases when customizing the interface. The developed system uses muscle contraction to direct the position of the end effector of a virtual robot. The classification of Electromyography (EMG) signals is obtained to generate trajectories in real time by designing a test platform in LabVIEW.<\/jats:p>","DOI":"10.3390\/s22093424","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T07:08:58Z","timestamp":1651475338000},"page":"3424","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot"],"prefix":"10.3390","volume":"22","author":[{"given":"Francisco","family":"P\u00e9rez-Reynoso","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n, Innovaci\u00f3n y Desarrollo Tecnol\u00f3gico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Quer\u00e9taro 76230, Mexico"}]},{"given":"Ne\u00edn","family":"Farrera-Vazquez","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n, Innovaci\u00f3n y Desarrollo Tecnol\u00f3gico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Quer\u00e9taro 76230, Mexico"}]},{"given":"C\u00e9sar","family":"Capetillo","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n, Innovaci\u00f3n y Desarrollo Tecnol\u00f3gico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Quer\u00e9taro 76230, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5622-9283","authenticated-orcid":false,"given":"Nestor","family":"M\u00e9ndez-Lozano","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n, Innovaci\u00f3n y Desarrollo Tecnol\u00f3gico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Quer\u00e9taro 76230, Mexico"}]},{"given":"Carlos","family":"Gonz\u00e1lez-Guti\u00e9rrez","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n, Innovaci\u00f3n y Desarrollo Tecnol\u00f3gico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Quer\u00e9taro 76230, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4998-1554","authenticated-orcid":false,"given":"Emmanuel","family":"L\u00f3pez-Neri","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n, Innovaci\u00f3n y Desarrollo Tecnol\u00f3gico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Quer\u00e9taro 76230, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Palumbo, A., Vizza, P., Calabrese, B., and Ielpo, N. (2021). Biopotential Signal Monitoring Systems in Rehabilitation: A Review. Sensors, 21.","DOI":"10.3390\/s21217172"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Laksono, P., Matsushita, K., Suhaimi, M., Kitamura, T., Njeri, W., Muguro, J., and Sasaki, M. (2020). Mapping Three Electromyography Signals Generated by Human Elbow and Shoulder Movements to Two Degree of Freedom Upper-Limb Robot Control. Robotics, 9.","DOI":"10.3390\/robotics9040083"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1021\/acsaelm.0c01129","article-title":"High-Fidelity Recording of EMG Signals by Multichannel On-Skin Electrode Arrays from Target Muscles for Effective Human\u2013Machine Interfaces","volume":"3","author":"Zhu","year":"2021","journal-title":"ACS Appl. Electron. Mater."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1007\/s11370-020-00328-5","article-title":"Comprehensive review on brain-controlled mobile robots and robotic arms based on electroencephalography signals","volume":"13","author":"Aljalal","year":"2020","journal-title":"Intell. Serv. Robot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3389\/fneur.2017.00107","article-title":"Advanced Myoelectric Control for Robotic Hand-Assisted Training: Outcome from a Stroke Patient","volume":"8","author":"Lu","year":"2017","journal-title":"Front. Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101791","DOI":"10.1016\/j.bspc.2019.101791","article-title":"Novel algorithm for conventional myocontrol of upper limbs prosthetics","volume":"57","author":"Benchabane","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TNSRE.2015.2410176","article-title":"Real-Time Task Discrimination for Myoelectric Control Employing Task-Specific Muscle Synergies","volume":"24","author":"Rasool","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.bbe.2017.03.001","article-title":"Comparative evaluation of EMG signal features for myoelectric controlled human arm prosthetics","volume":"37","author":"Karabulut","year":"2017","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hwang, H.-J., Hahne, J.M., and M\u00fcller, K.-R. (2017). Real-time robustness evaluation of regression based myoelectric control against arm position change and donning\/doffing. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0186318"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/TNSRE.2014.2305111","article-title":"The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges","volume":"22","author":"Farina","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1682\/JRRD.2010.09.0177","article-title":"Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use","volume":"48","author":"Scheme","year":"2011","journal-title":"J. Rehabil. Res. Dev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1109\/TNSRE.2009.2039590","article-title":"Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis","volume":"18","author":"Hargrove","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/TBME.2012.2188799","article-title":"Spatial Filtering for Robust Myoelectric Control","volume":"59","author":"Hahne","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1109\/TNSRE.2015.2492619","article-title":"Improving the Robustness of Myoelectric Pattern Recognition for Upper Limb Prostheses by Covariate Shift Adaptation","volume":"24","author":"Vidovic","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_15","unstructured":"Jiang, N., Tian, L., Fang, P., Dai, Y., and Li, G. (2013, January 3\u20137). Motion recognition for simultaneous control of multifunctional transradial prostheses. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Smith, L.H., and Hargrove, L.J. (2013, January 3\u20137). Comparison of surface and intramuscular EMG pattern recognition for simultaneous wrist\/hand motion classification. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610477"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Prahm, C., Eckstein, K., Ortiz-Catalan, M., Dorffner, G., Kaniusas, E., and Aszmann, O.C. (2016). Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control. BMC Res. Notes, 9.","DOI":"10.1186\/s13104-016-2232-y"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Abbaspour, S., Naber, A., Ortiz-Catalan, M., GholamHosseini, H., and Lind\u00e9n, M. (2021). Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements. Sensors, 21.","DOI":"10.3390\/s21165677"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12984-016-0212-z","article-title":"A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees","volume":"14","author":"Li","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.bspc.2014.02.005","article-title":"A comparison of upper-limb motion pattern recognition using EMG signals during dynamic and isometric muscle contractions","volume":"11","author":"Tsai","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_21","unstructured":"Webster, J.G., and Clark, J.W. (1998). Medical Instrumentation: Application and Design, Wiley. [18th ed.]."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Reynoso, F.D., Rodr\u00edguez-Guerrero, L., Salgado-Ram\u00edrez, J.C., and Ortega-Palacios, R. (2021). Human\u2013Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot. 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