{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:13:24Z","timestamp":1768810404373,"version":"3.49.0"},"reference-count":67,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,5]],"date-time":"2020-08-05T00:00:00Z","timestamp":1596585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["Finance Code 001"],"award-info":[{"award-number":["Finance Code 001"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.<\/jats:p>","DOI":"10.3390\/s20164359","type":"journal-article","created":{"date-parts":[[2020,8,5]],"date-time":"2020-08-05T06:02:21Z","timestamp":1596607341000},"page":"4359","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet"],"prefix":"10.3390","volume":"20","author":[{"given":"Jos\u00e9 Jair Alves","family":"Mendes Junior","sequence":"first","affiliation":[{"name":"Graduate Program in Electrical Engineering and Industrial Informatics (CPGEI), Federal University of Technology\u2013Paran\u00e1 (UTFPR), Curitiba (PR) 80230-901, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Melissa La Banca","family":"Freitas","sequence":"additional","affiliation":[{"name":"Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology\u2013Paran\u00e1 (UTFPR), Ponta Grossa (PR) 84017-220, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6233-6077","authenticated-orcid":false,"given":"Daniel Prado","family":"Campos","sequence":"additional","affiliation":[{"name":"Graduate Program in Biomedical Engineering (PPGEB), Federal University of Technology\u2013Paran\u00e1 (UTFPR), Curitiba (PR) 80230-901, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felipe Adalberto","family":"Farinelli","sequence":"additional","affiliation":[{"name":"Graduate Program in Electrical Engineering and Industrial Informatics (CPGEI), Federal University of Technology\u2013Paran\u00e1 (UTFPR), Curitiba (PR) 80230-901, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4783-5350","authenticated-orcid":false,"suffix":"Jr.","given":"Sergio Luiz","family":"Stevan","sequence":"additional","affiliation":[{"name":"Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology\u2013Paran\u00e1 (UTFPR), Ponta Grossa (PR) 84017-220, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4380-7499","authenticated-orcid":false,"given":"S\u00e9rgio Francisco","family":"Pichorim","sequence":"additional","affiliation":[{"name":"Graduate Program in Electrical Engineering and Industrial Informatics (CPGEI), Federal University of Technology\u2013Paran\u00e1 (UTFPR), Curitiba (PR) 80230-901, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cheok, M.J., Omar, Z., and Jaward, M.H. (2017). A review of hand gesture and sign language recognition techniques. Int. J. Mach. Learn. Cyber., 1\u201323.","DOI":"10.1007\/s13042-017-0705-5"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.procs.2016.08.044","article-title":"Assistive Technology for Deaf People Based on Android Platform","volume":"94","author":"Abdallah","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1093\/deafed\/5.3.266","article-title":"The impact of sign language on the cognitive development of deaf children: The case of theories of mind","volume":"5","author":"Courtin","year":"2000","journal-title":"J. Deaf Stud. Deaf Educ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1007\/s10900-018-0511-3","article-title":"American Sign Language Interpreters Perceptions of Barriers to Healthcare Communication in Deaf and Hard of Hearing Patients","volume":"43","author":"Hommes","year":"2018","journal-title":"J. Community Health"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101053","DOI":"10.1016\/j.csl.2019.101053","article-title":"Trajectory-based recognition of dynamic Persian sign language using hidden Markov model","volume":"61","author":"Azar","year":"2020","journal-title":"Comput. Speech Lang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e03554","DOI":"10.1016\/j.heliyon.2020.e03554","article-title":"A comparison of Arabic sign language dynamic gesture recognition models","volume":"6","author":"Almasre","year":"2020","journal-title":"Heliyon"},{"key":"ref_7","unstructured":"Al-Ahdal, M.E., and Nooritawati, M.T. (2012, January 18\u201320). Review in Sign Language Recognition Systems. Proceedings of the 2012 IEEE Symposium on Computers Informatics (ISCI), Penang, Malaysia."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Riillo, F., Quitadamo, L.R., Cavrini, F., Saggio, G., Pinto, C.A., Pasto, N.C., Sbernini, L., and Gruppioni, E. (2014, January 11\u201312). Evaluating the influence of subject-related variables on EMG-based hand gesture classification. Proceedings of the 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Lisbon, Portugal.","DOI":"10.1109\/MeMeA.2014.6860134"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.1007\/s11517-019-02024-8","article-title":"A cepstrum analysis-based classification method for hand movement surface EMG signals","volume":"57","author":"Yavuz","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2879","DOI":"10.1109\/TBME.2009.2013200","article-title":"Sign Language Recognition Using Intrinsic-Mode Sample Entropy on sEMG and Accelerometer Data","volume":"56","author":"Kosmidou","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/JBHI.2016.2598302","article-title":"A Wearable System for Recognizing American Sign Language in Real-Time Using IMU and Surface EMG Sensors","volume":"20","author":"Wu","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_12","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_13","unstructured":"Criswell, E. (2011). Cram\u2019s Introduction to Surface Electromyography, Jones and Bartlett Publishers."},{"key":"ref_14","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":"Raez","year":"2006","journal-title":"Biol. Proced. Online"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2695","DOI":"10.1109\/TBME.2012.2190734","article-title":"A Sign-Component-Based Framework for Chinese Sign Language Recognition Using Accelerometer and sEMG Data","volume":"59","author":"Li","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s11517-019-02073-z","article-title":"Evaluation of surface EMG-based recognition algorithms for decoding hand movements","volume":"58","author":"Abbaspour","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1109\/TBME.2008.919734","article-title":"Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb","volume":"55","author":"Oskoei","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.procs.2018.07.012","article-title":"Combination of EMG Features and Stability Index for Finger Movements Recognition","volume":"133","author":"Saikia","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4832","DOI":"10.1016\/j.eswa.2013.02.023","article-title":"EMG feature evaluation for improving myoelectric pattern recognition robustness","volume":"40","author":"Phinyomark","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Barioul, R., Fakhfakh, S., Derbel, H., and Kanoun, O. (2019, January 21\u201324). Evaluation of EMG Signal Time Domain Features for Hand Gesture Distinction. Proceedings of the 2019 16th International Multi-Conference on Systems, Signals Devices (SSD), Istanbul, Turkey.","DOI":"10.1109\/SSD.2019.8893277"},{"key":"ref_21","first-page":"31","article-title":"Hand Gesture Recognition based on EMG signals using ANN","volume":"2","author":"Shroffe","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Derr, C., and Sahin, F. (2017, January 5\u20138). Signer-independent classification of American sign language word signs using surface EMG. Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, Canada.","DOI":"10.1109\/SMC.2017.8122683"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhuang, Y., Lv, B., Sheng, X., and Zhu, X. (2017, January 21\u201323). Towards Chinese sign language recognition using surface electromyography and accelerometers. Proceedings of the 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Auckland, New Zealand.","DOI":"10.1109\/M2VIP.2017.8211506"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TSMCA.2011.2116004","article-title":"A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors","volume":"41","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern.\u2014Part A Syst. Hum."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kosmidou, V.E., Hadjileontiadis, L.J., and Panas, S.M. (September, January 30). Evaluation of surface EMG features for the recognition of American Sign Language gestures. Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA.","DOI":"10.1109\/IEMBS.2006.259428"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shin, S., Baek, Y., Lee, J., Eun, Y., and Son, S.H. (December, January 27). Korean sign language recognition using EMG and IMU sensors based on group-dependent NN models. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HA, USA.","DOI":"10.1109\/SSCI.2017.8280908"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Su, R., Chen, X., Cao, S., and Zhang, X. (2016). Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors. Sensors, 16.","DOI":"10.3390\/s16010100"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, J., Tian, Z., Sun, L., Estevez, L., and Jafari, R. (2015, January 9\u201312). Real-time American Sign Language Recognition using wrist-worn motion and surface EMG sensors. Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA.","DOI":"10.1109\/BSN.2015.7299393"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"23303","DOI":"10.3390\/s150923303","article-title":"A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors","volume":"15","author":"Cheng","year":"2015","journal-title":"Sensors"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Amatanon, V., Chanhang, S., Naiyanetr, P., and Thongpang, S. (2014, January 26\u201328). Sign language-Thai alphabet conversion based on Electromyogram (EMG). Proceedings of the The 7th 2014 Biomedical Engineering International Conference, Fukuoka, Japan.","DOI":"10.1109\/BMEiCON.2014.7017398"},{"key":"ref_31","unstructured":"Wibawa, A.D., and Sumpeno, S. (2017, January 26\u201329). Gesture Recognition for Indonesian Sign Language Systems (ISLS) Using Multimodal Sensor Leap Motion and Myo Armband Controllers Based-on Na\u00efve Bayes Classifier. Proceedings of the 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT), Denpasar, Indonesia."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"119","DOI":"10.24003\/emitter.v5i1.173","article-title":"Moment Invariant Features Extraction for Hand Gesture Recognition of Sign Language based on SIBI","volume":"5","author":"Rahagiyanto","year":"2017","journal-title":"EMITTER Int. J. Eng. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bastos, I.L.O., Angelo, M.F., and Loula, A.C. (2015, January 26\u201329). Recognition of Static Gestures Applied to Brazilian Sign Language (Libras). Proceedings of the 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images, Salvador, Brazil.","DOI":"10.1109\/SIBGRAPI.2015.26"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1590\/2446-4740.03816","article-title":"A fully automatic method for recognizing hand configurations of Brazilian sign language","volume":"33","author":"Souza","year":"2017","journal-title":"Res. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Vera-Rodriguez, R., Fierrez, J., and Morales, A. (2019). Evaluating Deep Models for Dynamic Brazilian Sign Language Recognition. In Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Springer International Publishing.","DOI":"10.1007\/978-3-030-13469-3"},{"key":"ref_36","unstructured":"Freitas, M.L.B., Mendes Junior, J.J.A., Campos, D.P., and Stevan, S.L. (2018, January 21\u201325). Hand gestures classification using multichannel sEMG armband. Proceedings of the Anais do XXVI Congresso Brasileiro de Engenharia Biom\u00e9dica, Arma\u00e7\u00e3o dos B\u00fazios, Brazil."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"C\u00f4t\u00e9-Allard, U., Gagnon-Turcotte, G., Laviolette, F., and Gosselin, B. (2019). A Low-Cost, Wireless, 3-D-Printed Custom Armband for sEMG Hand Gesture Recognition. Sensors, 19.","DOI":"10.3390\/s19122811"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Abreu, J.G., Teixeira, J.M., Figueiredo, L.S., and Teichrieb, V. (2016, January 21\u201324). Evaluating Sign Language Recognition Using the Myo Armband. Proceedings of the 2016 XVIII Symposium on Virtual and Augmented Reality (SVR), Gramado, Brazil.","DOI":"10.1109\/SVR.2016.21"},{"key":"ref_39","unstructured":"Costa-Felix, R., Machado, J.C., and Alvarenga, A.V. (2019). Recognition of Libras Static Alphabet with MyoTM and Multi-Layer Perceptron. XXVI Brazilian Congress on Biomedical Engineering, Springer."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Asogbon, M.G., Samuel, O.W., Geng, Y., Chen, S., Mzurikwao, D., Fang, P., and Li, G. (2018, January 25\u201327). Effect of Window Conditioning Parameters on the Classification Performance and Stability of EMG-Based Feature Extraction Methods. Proceedings of the 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), Shenzhen, China.","DOI":"10.1109\/CBS.2018.8612246"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Vaitkevi\u010dius, A., Taroza, M., Bla\u017eauskas, T., Dama\u0161evi\u010dius, R., Maskeli\u016bnas, R., and Wo\u017aniak, M. (2019). Recognition of American Sign Language Gestures in a Virtual Reality Using Leap Motion. Appl. Sci., 9.","DOI":"10.3390\/app9030445"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Saggio, G., Cavallo, P., Ricci, M., Errico, V., Zea, J., and Benalc\u00e1zar, M.E. (2020). Sign Language Recognition Using Wearable Electronics: Implementing k-Nearest Neighbors with Dynamic Time Warping and Convolutional Neural Network Algorithms. Sensors, 20.","DOI":"10.3390\/s20143879"},{"key":"ref_43","first-page":"291","article-title":"American Sign Language Recognition System Using Wearable Sensors with Deep Learning Approach","volume":"15","author":"Chong","year":"2020","journal-title":"J. Korea Inst. Electron. Commun. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"113336","DOI":"10.1016\/j.eswa.2020.113336","article-title":"Hand sign language recognition using multi-view hand skeleton","volume":"150","author":"Rastgoo","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.neunet.2020.01.030","article-title":"Skeleton-based Chinese sign language recognition and generation for bidirectional communication between deaf and hearing people","volume":"125","author":"Xiao","year":"2020","journal-title":"Neural Netw."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, L., Fu, J., Wu, Y., Li, H., and Zheng, B. (2020). Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals. Sensors, 20.","DOI":"10.3390\/s20030672"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Guo, H., and Sung, Y. (2020). Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture. Sensors, 20.","DOI":"10.3390\/s20061801"},{"key":"ref_48","first-page":"1","article-title":"Technical Features and Functionalities of Myo Armband: An Overview on Related Literature and Advanced Applications of Myoelectric Armbands Mainly Focused on Arm Prostheses","volume":"11","author":"Visconti","year":"2018","journal-title":"Int. J. Smart Sens. Intell. Syst."},{"key":"ref_49","unstructured":"Kamen, G., and Gabriel, D. (2020, August 02). Essentials of Electromyography: Human Kinetics. Available online: https:\/\/us.humankinetics.com\/products\/essentials-of-electromyography."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1109\/TBME.2008.923917","article-title":"A Comparison of the Effects of Electrode Implantation and Targeting on Pattern Classification Accuracy for Prosthesis Control","volume":"55","author":"Farrell","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","unstructured":"Al-Mulla, M.R., Sepulveda, F., and Colley, M. (2012). sEMG Techniques to Detect and Predict Localised Muscle Fatigue. EMG Methods Eval. Muscle Nerve Funct.","DOI":"10.3390\/s110403545"},{"key":"ref_53","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":"Freitas","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_54","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. Biomed. Eng."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Phinyomark, A., N. Khushaba, R., and Scheme, E. (2018). Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors. Sensors, 18.","DOI":"10.3390\/s18051615"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1109\/TMECH.2007.897253","article-title":"Recognition of Electromyographic Signals Using Cascaded Kernel Learning Machine","volume":"12","author":"Liu","year":"2007","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/1743-0003-7-21","article-title":"Study of stability of time-domain features for electromyographic pattern recognition","volume":"7","author":"Tkach","year":"2010","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.jelekin.2012.06.005","article-title":"Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes","volume":"22","author":"Zhang","year":"2012","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kaczmarek, P., Ma\u0144kowski, T., and Tomczy\u0144ski, J. (2019). putEMG\u2014A Surface Electromyography Hand Gesture Recognition Dataset. Sensors, 19.","DOI":"10.3390\/s19163548"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13638-018-1046-0","article-title":"Gesture recognition method based on a single-channel sEMG envelope signal","volume":"2018","author":"Wu","year":"2018","journal-title":"J. Wirel. Commun. Network."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_63","unstructured":"Slutter, M.W.J. (2017). Creating a Feedback System with the Myo Armband, for Home Training for Frail Older Adults. [Bachelor Thesis, University of Twente]."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Pizzolato, S., Tagliapietra, L., Cognolato, M., Reggiani, M., M\u00fcller, H., and Atzori, M. (2017). Comparison of six electromyography acquisition setups on hand movement classification tasks. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0186132"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"101588","DOI":"10.1016\/j.bspc.2019.101588","article-title":"Prosthetic hand control: A multidisciplinary review to identify strengths, shortcomings, and the future","volume":"53","author":"Kumar","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TNSRE.2010.2100828","article-title":"Determining the optimal window length for pattern recognition-based myoelectric control: Balancing the competing effects of classification error and controller delay","volume":"19","author":"Smith","year":"2011","journal-title":"IEEE Trans. Neural. Syst. Rehabil. Eng."},{"key":"ref_67","first-page":"1","article-title":"Statistical Comparisons of Classifiers over Multiple Data Sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4359\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:54:27Z","timestamp":1760176467000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4359"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,5]]},"references-count":67,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20164359"],"URL":"https:\/\/doi.org\/10.3390\/s20164359","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,5]]}}}