{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T02:30:59Z","timestamp":1781663459097,"version":"3.54.5"},"reference-count":71,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T00:00:00Z","timestamp":1678147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Consejer\u00eda JCyL","award":["BU052P20"],"award-info":[{"award-number":["BU052P20"]}]},{"name":"Consejer\u00eda JCyL","award":["PID2020-116011RB-C22 MyGait"],"award-info":[{"award-number":["PID2020-116011RB-C22 MyGait"]}]},{"name":"Consejer\u00eda JCyL","award":["P022\/BMD-7236 MINA-CM"],"award-info":[{"award-number":["P022\/BMD-7236 MINA-CM"]}]},{"name":"Spanish Ministry of Science and Innovation","award":["BU052P20"],"award-info":[{"award-number":["BU052P20"]}]},{"name":"Spanish Ministry of Science and Innovation","award":["PID2020-116011RB-C22 MyGait"],"award-info":[{"award-number":["PID2020-116011RB-C22 MyGait"]}]},{"name":"Spanish Ministry of Science and Innovation","award":["P022\/BMD-7236 MINA-CM"],"award-info":[{"award-number":["P022\/BMD-7236 MINA-CM"]}]},{"name":"Comunidad de Madrid","award":["BU052P20"],"award-info":[{"award-number":["BU052P20"]}]},{"name":"Comunidad de Madrid","award":["PID2020-116011RB-C22 MyGait"],"award-info":[{"award-number":["PID2020-116011RB-C22 MyGait"]}]},{"name":"Comunidad de Madrid","award":["P022\/BMD-7236 MINA-CM"],"award-info":[{"award-number":["P022\/BMD-7236 MINA-CM"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Surface electromyography (sEMG) plays a crucial role in several applications, such as for prosthetic controls, human\u2013machine interfaces (HMI), rehabilitation, and disease diagnosis. These applications are usually occurring in real-time, so the classifier tends to run on a wearable device. This edge processing paradigm imposes strict requirements on the complexity classifier. To date, research on hand gesture recognition (GR) based on sEMG uses discriminant classifiers, such as support vector machines and neural networks. These classifiers can achieve good precision; they cannot detect when an error in classification has happened. This paper proposes a novel hand gesture multiclass model based on partial least square (PLS) class modelling that uses an encoding matrix called error correcting output codes (ECOC). A dataset of eight different gestures was classified using this method where all errors were detected, proving the feasibility of PLS-ECOC as a fault-tolerant classifier. Considering the PLS-ECOC model as a classifier, its accuracy, precision, and F1 are 87.5, 91.87, and 86.34%, respectively, similar to those obtained by other authors. The strength of our work lies in the extra information provided by the PLS-ECOC that allows the application to be fault tolerant while keeping a small-size model and low complexity, making it suitable for embedded real-time classification.<\/jats:p>","DOI":"10.3390\/a16030149","type":"journal-article","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T01:58:22Z","timestamp":1678240702000},"page":"149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Electromyography Gesture Model Classifier for Fault-Tolerant-Embedded Devices by Means of Partial Least Square Class Modelling Error Correcting Output Codes (PLS-ECOC)"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7391-8494","authenticated-orcid":false,"given":"Pablo","family":"Sarabia","sequence":"first","affiliation":[{"name":"B105 Electronic Systems Lab, Universidad Polit\u00e9cnica de Madrid, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9269-5900","authenticated-orcid":false,"given":"Alvaro","family":"Araujo","sequence":"additional","affiliation":[{"name":"B105 Electronic Systems Lab, Universidad Polit\u00e9cnica de Madrid, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5936-7929","authenticated-orcid":false,"given":"Luis Antonio","family":"Sarabia","sequence":"additional","affiliation":[{"name":"Department Mathematics and Computation, Faculty Sciences, University of Burgos, Pza. Misael Ba\u00f1uelos s\/n, 09001 Burgos, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4751-8929","authenticated-orcid":false,"given":"Mar\u00eda de la Cruz","family":"Ortiz","sequence":"additional","affiliation":[{"name":"Department Chemistry, Faculty Sciences, University of Burgos, Pza. Misael Ba\u00f1uelos s\/n, 09001 Burgos, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.bspc.2015.02.009","article-title":"Current state of digital signal processing in myoelectric interfaces and related applications","volume":"18","author":"Hakonen","year":"2015","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dunai, L., Novak, M., and Garc\u00eda Espert, C. (2021). Human Hand Anatomy-Based Prosthetic Hand. Sensors, 21.","DOI":"10.20944\/preprints202010.0568.v1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10731","DOI":"10.1016\/j.eswa.2012.02.192","article-title":"Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals","volume":"39","author":"Khushaba","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1024494031121","article-title":"Rehabilitation robotics: Performance-based progressive robot-assisted therapy","volume":"15","author":"Krebs","year":"2003","journal-title":"Auton. Robot."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bouteraa, Y., Ben Abdallah, I., Ibrahim, A., and Ahanger, T.A. (2020). Development of an IoT-Based Solution Incorporating Biofeedback and Fuzzy Logic Control for Elbow Rehabilitation. Appl. Sci., 10.","DOI":"10.3390\/app10217793"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2996","DOI":"10.1016\/j.clinph.2021.08.022","article-title":"Electromyographic findings in primary lateral sclerosis during disease progression","volume":"132","author":"Gromicho","year":"2021","journal-title":"Clin. Neurophysiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103815","DOI":"10.1016\/j.dsp.2022.103815","article-title":"Swallowing disorders analysis using surface EMG biomarkers and classification models","volume":"133","year":"2023","journal-title":"Digit. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"012004","DOI":"10.1088\/1742-6596\/313\/1\/012004","article-title":"Patellofemoral pain syndrome: Electromyography in a frequency domain analysis","volume":"313","author":"Catelli","year":"2011","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.compbiomed.2013.01.020","article-title":"Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders","volume":"43","author":"Subasi","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Salinas, S.A., Elgalhud, M.A.T.A., Tambakis, L., Salunke, S.V., Patel, K., Ghenniwa, H., Ouda, A., McIsaac, K., Grolinger, K., and Trejos, A.L. (2022, January 25\u201329). Comparison of Machine Learning Techniques for Activities of Daily Living Classification with Electromyographic Data. Proceedings of the 2022 International Conference on Rehabilitation Robotics (ICORR), Rotterdam, The Netherlands.","DOI":"10.1109\/ICORR55369.2022.9896565"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"011003","DOI":"10.1088\/1741-2552\/ac4f9a","article-title":"Deep learning for biosignal control: Insights from basic to real-time methods with recommendations","volume":"19","author":"Dillen","year":"2022","journal-title":"J. Neural Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9198","DOI":"10.1109\/JSEN.2022.3165988","article-title":"Surface Electromyography as a Natural Human\u2013Machine Interface: A Review","volume":"22","author":"Zheng","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_13","first-page":"512","article-title":"Deep Learning for EMG-based Human-Machine Interaction: A Review, IEEE\/CAA","volume":"8","author":"Xiong","year":"2021","journal-title":"J. Autom. Sin."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"011001","DOI":"10.1088\/1741-2552\/14\/1\/011001","article-title":"Support vector machines to detect physiological patterns for EEG and EMG-based human\u2013computer interaction: A review","volume":"14","author":"Quitadamo","year":"2017","journal-title":"J. Neural Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"98450","DOI":"10.1109\/ACCESS.2022.3206782","article-title":"Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review","volume":"10","author":"Diab","year":"2022","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1109\/JSSC.2022.3179601","article-title":"A 184-\u03bcW Error-Tolerant Real-Time Hand Gesture Recognition System With Hybrid Tiny Classifiers Utilizing Edge CNN","volume":"58","author":"Lu","year":"2023","journal-title":"IEEE J. Solid-State Circuits"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Song, L., Zhang, J., Song, Y., and Yan, M. (2022). Multi-Category Gesture Recognition Modeling Based on sEMG and IMU Signals. Sensors, 22.","DOI":"10.3390\/s22155855"},{"key":"ref_18","unstructured":"Morrison, D.F. (1990). Multivariate Statistical Methods, McGraw-Hill. [3rd ed.]."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1080\/01621459.1989.10478752","article-title":"Regularized discriminant analysis","volume":"84","author":"Friedman","year":"1989","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1002\/cem.785","article-title":"Partial Least Squares for discrimination","volume":"17","author":"Barker","year":"2003","journal-title":"J. Chemom."},{"key":"ref_21","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, CRC Press."},{"key":"ref_22","unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1142\/S0219622013500338","article-title":"A multi-class support vector machine: Theory and model","volume":"12","author":"Sun","year":"2013","journal-title":"Int. J. Inf. Technol. Decis. Mak."},{"key":"ref_24","unstructured":"Solla, S., Leen, T., and M\u00fcller, K.-R. (2000). Advances in Neural Information Processing Systems 12, Proceedings of the 29 December 1999 Conference, MIT Press."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","article-title":"A logical calculus of the ideas imminent in nervous activity","volume":"5","author":"McCulloch","year":"1943","journal-title":"Bull. Math. Biophys."},{"key":"ref_26","unstructured":"Werbos, P. (1974). Beyond Regression. [Ph.D. Thesis, Harvard University]. Available online: https:\/\/www.researchgate.net\/publication\/35657389."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Bottou","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0031-3203(76)90014-5","article-title":"Pattern recognition by means of disjoint principal components models","volume":"8","author":"Wold","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/S0003-2670(00)86468-5","article-title":"UNEQ: A disjoint modelling technique for pattern recognition based on normal distribution","volume":"184","author":"Derde","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","article-title":"Support Vector Data Description","volume":"54","author":"Tax","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.aca.2005.11.038","article-title":"Sensitivity and specificity of PLS-class modelling for five sensory characteristics of dry-cured ham using visible and near infrared spectroscopy","volume":"558","author":"Ortiz","year":"2006","journal-title":"Anal. Chim. Acta"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104614","DOI":"10.1016\/j.chemolab.2022.104614","article-title":"Simultaneous class-modelling in chemometrics: A generalization of Partial Least Squares class modelling for more than two classes by using error correcting output code matrices","volume":"227","author":"Valencia","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jaramillo-Y\u00e1nez, A., Benalc\u00e1zar, M.E., and Mena-Maldonado, E. (2020). Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review. Sensors, 20.","DOI":"10.3390\/s20092467"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/978-3-031-13150-9_10","article-title":"EMG Pattern Recognition: A Systematic Review","volume":"Volume 521","author":"Garg","year":"2023","journal-title":"Lecture Notes in Networks and Systems ISMS 2021"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nazmi, N., Rahman, M.A.A., Yamamoto, S.I., Ahmad, S.A., Zamzuri, H., and Mazlan, S.A. (2016). A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions. Sensors, 16.","DOI":"10.3390\/s16081304"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Aviles, M., S\u00e1nchez-Reyes, L.M., Fuentes-Aguilar, R.Q., Toledo-P\u00e9rez, D.C., and Rodr\u00edguez-Res\u00e9ndiz, J. (2022). A Novel Methodology for Classifying EMG Movements Based on SVM and Genetic Algorithms. Micromachines, 13.","DOI":"10.3390\/mi13122108"},{"key":"ref_37","unstructured":"Sarabia Ortiz, P. (2020). Design and Evaluation of Electromyography Signal Processing Techniques Using Resource-Constrained Devices. [Master\u2019s Thesis, E.T.S.I. Telecomunicaci\u00f3n, UPM, Universidad Polit\u00e9cnica de Madrid]. Available online: https:\/\/oa.upm.es\/72388\/."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1088\/1741-2552\/abc902","article-title":"A Survey on Deep Learning-based Non-Invasive Brain Signals: Recent Advances and New Frontiers","volume":"18","author":"Zhang","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1109\/TNSRE.2022.3183023","article-title":"Motor Imagery Decoding in the Presence of Distraction Using Graph Sequence Neural Networks","volume":"30","author":"Cai","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"066012","DOI":"10.1088\/1741-2552\/ac9e75","article-title":"Improving Pre-movement Pattern Detection with Filter Bank Selection","volume":"19","author":"Jia","year":"2022","journal-title":"J. Neural Eng."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Phinyomark, A., and Scheme, E. (2018). EMG Pattern Recognition in the Era of Big Data and Deep Learning. Big Data Cogn. Comput., 2.","DOI":"10.3390\/bdcc2030021"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/S1350-4533(99)00066-1","article-title":"Classification of the myoelectric signal using time-frequency based representations","volume":"21","author":"Englehart","year":"1999","journal-title":"Med. Eng. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Arozi, M., Caesarendra, W., Ariyanto, M., Munadi, M., Setiawan, J.D., and Glowacz, A. (2020). Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements. Symmetry, 12.","DOI":"10.3390\/sym12040541"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101920","DOI":"10.1016\/j.bspc.2020.101920","article-title":"Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach","volume":"59","author":"Freitasa","year":"2020","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, X., Liu, A., Wang, Z.J., and Peng, H. (2013). Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis. J. Appl. Math., 401976.","DOI":"10.1155\/2013\/401976"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104423","DOI":"10.1016\/j.chemolab.2021.104423","article-title":"A modified entropy-based performance criterion for class-modelling with multiple classes","volume":"217","author":"Valencia","year":"2021","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"115017","DOI":"10.1016\/j.eswa.2021.115017","article-title":"A robust SVM-based approach with feature selection and outliers detection for classification problems","volume":"178","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.clinph.2022.11.019","article-title":"Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach","volume":"146","author":"Tannemaat","year":"2023","journal-title":"Clin. Neurophysiol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Rodriguez-Zurrunero, R., Tirado-Andr\u00e9s, F., and Araujo, A. (2018, January 1\u20134). YetiOS: An Adaptive Operating System for Wireless Sensor Networks. Proceedings of the 2018 IEEE 43rd Conference on Local Computer Networks Workshops (LCN Workshops), Chicago, IL, USA.","DOI":"10.1109\/LCNW.2018.8628500"},{"key":"ref_50","unstructured":"(MATLAB, 2022). MATLAB, version 9.9.0.2037887 (R2020b)."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Palermo, F., Cognolato, M., Gijsberts, A., M\u00fcller, H., Caputo, B., and Atzori, M. (2017, January 17\u201320). Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data. Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR), London, UK.","DOI":"10.1109\/ICORR.2017.8009405"},{"key":"ref_52","unstructured":"Sarabia Ortiz, P. (\u201csEMG 4 Channel 8 Gestures Dataset\u201d, Mendeley Data, 2023). \u201csEMG 4 Channel 8 Gestures Dataset\u201d, Mendeley Data, version 1."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1002\/cem.1310","article-title":"Principles of Proper Validation: Use and abuse of re-sampling for validation","volume":"24","author":"Esbensen","year":"2010","journal-title":"J. Chem."},{"key":"ref_54","unstructured":"Bates, S., Hastie, T., and Tibshirani, R. (2022). Cross-validation: What does it estimate and how well does it do it?. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/00401706.1969.10490666","article-title":"Computer aided design of experiments","volume":"11","author":"Kennard","year":"1969","journal-title":"Technometrics"},{"key":"ref_56","unstructured":"(PLSToolbox, 2021). PLSToolbox, Version 8.9.2 R23162."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lavine, B.K., Brown, S.D.D., and Booksh, K.S. (2015). Chemometrics and Bruce: Some Fond Memories, American Chemical Society. 40 Years of Chemometrics\u2014From Bruce Kowalski to the Future.","DOI":"10.1021\/bk-2015-1199"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/0169-7439(93)85002-X","article-title":"SIMPLS: An alternative approach to partial least squares regression","volume":"18","year":"1993","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Saunders, C., Grobelnik, M., Gunn, S., and Shawe-Taylor, J. (2005). Overview and Recent Advances in Partial Least Squares in Subspace, Latent Structure and Feature Selection, Springer. Available online: https:\/\/link.springer.com\/chapter\/10.1007\/11752790_2.","DOI":"10.1007\/11752790"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.chemolab.2018.08.014","article-title":"A computational approach to partial least squares model inversion in the framework of the process analytical technology and quality by design initiatives","volume":"182","author":"Ruiz","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wand, M.P., and Jones, M.C. (1995). Kernel Smoothing, Monographs on Statistical an Applied Probability, 60, Springer-Science-Business Media.","DOI":"10.1007\/978-1-4899-4493-1"},{"key":"ref_62","first-page":"12","article-title":"Quality performance metrics in multivariate classification methods for qualitative analysis","volume":"80","year":"2016","journal-title":"Trends Anal. Chem."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.chemolab.2017.12.004","article-title":"Multivariate comparison of classification performance measures","volume":"174","author":"Ballabio","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3799","DOI":"10.1016\/j.eswa.2009.11.040","article-title":"A novel measure for evaluating classifiers","volume":"37","author":"Wei","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Delgado, R., and N\u00fa\u00f1ez-Gonz\u00e1lez, J.D. (2019). Enhancing Confusion Entropy (CEN) for binary and multiclass classification. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0210264"},{"key":"ref_66","unstructured":"S\u00e1nchez, M.S., Valencia, O., Ruiz, S., Ortiz, M.C., and Sarabia, L.A. (2023, January 04). DMCEN a MATLAB Function to Evaluate the Entropy Improvement Provided by a Multivariate K-Class-Model. Available online: https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/112175-dmcen?s_tid=srchtitle."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"e6746","DOI":"10.1002\/cpe.6746","article-title":"Classification of EMG signals taken from arm with hybrid CNN-SVM architecture","volume":"34","author":"Tuncer","year":"2022","journal-title":"Concurr. Computat. Pract. Exper."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Gopal, P., Gesta, A., and Mohebbi, A. (2022). A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models. Sensors, 22.","DOI":"10.3390\/s22103650"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1080\/00224065.1980.11980968","article-title":"Simultaneous optimization of several response variables","volume":"12","author":"Derringer","year":"1980","journal-title":"J. Qual. Technol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.chemolab.2005.11.005","article-title":"Vectorial optimization as a methodological alternative to desirability function","volume":"83","author":"Ortiz","year":"2006","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"ref_71","unstructured":"Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms, Wiley."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/3\/149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:50:03Z","timestamp":1760122203000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/3\/149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,7]]},"references-count":71,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["a16030149"],"URL":"https:\/\/doi.org\/10.3390\/a16030149","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,7]]}}}