{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:54:16Z","timestamp":1777704856471,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,3,5]]},"abstract":"<jats:p>Communication is an essential component of human nature. It connects humans, allowing them to learn, grow, col-laborate, and resolve conflicts. Several aspects of human society, relationships, and growth would be significantly hampered in the absence of efficient communication. Hand gesture recognition is a way to interact with technology that can be particularly useful for individuals with disabilities. This hand gesture recognition is mainly employed in sign language translation, healthcare, rehabilitation, prosthesis, and Human-Computer Interaction (HCI). The high degree of dexterity is a main challenge for prosthetic limbs. In order to meet this challenge, hand gesture recognition is employed for the prosthetic limb, which can be used for rehabilitation. The objective of this article is to show the methodology for the recognition of hand gestures using Electromyography (EMG) signals. This article uses the pro-posed time domain feature extraction method called Absolute Fluctuation Analysis (AFA) along with the Root Mean Square (RMS) for the feature extraction method. Along with these feature extraction methods, repeated stratified K-fold cross validation is used for the validation of the classifiers such as the XGB classifier, the K-Nearest Neighbour (KNN) classifier, the Decision Tree classifier, the Random Forest classifier, and the SVM classifier, whose mean recognition accuracy is given by 93.26%, 87.42%, 85.26%, 92.23%, and 91.78%, respectively. The recognition accuracy of machine learning classifiers is being compared with state-of-the-art networks such as artificial neural net-works (ANN), long short-term memory (LSTM), bidirectional LSTM, gated recurrent units (GRU), and convolution-al neural networks (CNN), which provide recognition accuracy of 96.65%, 99.16%, 99.94%, and 99.99%, respectively.<\/jats:p>","DOI":"10.3233\/jifs-234196","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T11:45:09Z","timestamp":1706615109000},"page":"7047-7059","source":"Crossref","is-referenced-by-count":2,"title":["Pattern recognition for EMG based forearm orientation and contraction in myoelectric prosthetic hand"],"prefix":"10.1177","volume":"46","author":[{"given":"J. Roselin","family":"Suganthi","sequence":"first","affiliation":[{"name":"Department of ECE, K. Ramakrishnan College of Engineering, Tiruchirappalli, Tamil Nadu, India"}]},{"given":"K.","family":"Rajeswari","sequence":"additional","affiliation":[{"name":"Department of ECE, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-234196_ref1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/0141-5425(82)90021-8","article-title":"Multifunctional prosthesis and orthosis control via microcomputer identification of temporal pattern differences in single-site myoelectric signals","volume":"4","author":"Graupe","year":"1982","journal-title":"Journal of Biomedical Engineering"},{"key":"10.3233\/JIFS-234196_ref2","doi-asserted-by":"crossref","unstructured":"Park G. and Kim H. , Low-Cost Implementation of a Named Entity Recognition System for Voice-Activated Human-Appliance Interfaces in a Smart Home, 10(2) (2018).","DOI":"10.3390\/su10020488"},{"issue":"6","key":"10.3233\/JIFS-234196_ref3","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.3390\/su10061865","article-title":"sEMG-Based Gesture Recognition with Convolution Neural Networks","volume":"10","author":"Ding","year":"2018","journal-title":"Sustainability"},{"key":"10.3233\/JIFS-234196_ref4","doi-asserted-by":"crossref","unstructured":"Farina D. and Aszmann O. , Bionic Limbs: Clinical Reality and Academic Promises, Science Translational Medicine 6(257) (2014).","DOI":"10.1126\/scitranslmed.3010453"},{"key":"10.3233\/JIFS-234196_ref5","doi-asserted-by":"crossref","unstructured":"Ameri A. , Akhaee M.A. , Scheme E. and Englehart K. , Real-time, simultaneous myoelectric control using a convolutional neural network, PLOS ONE 13(9) (2018).","DOI":"10.1371\/journal.pone.0203835"},{"issue":"1","key":"10.3233\/JIFS-234196_ref6","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 Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"1","key":"10.3233\/JIFS-234196_ref7","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1111\/exsy.12008","article-title":"Signal processing evaluation of myoelectricsensor placement in low-level gestures: Sensitivity analysis using independent component analysis","volume":"31","author":"Naik","year":"2012","journal-title":"Expert Systems"},{"issue":"7","key":"10.3233\/JIFS-234196_ref8","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/TBME.2003.813539","article-title":"A robust, real-time control scheme for multifunction myoelectric control","volume":"50","author":"Englehart","year":"2003","journal-title":"IEEETransactions on Biomedical Engineering"},{"issue":"6","key":"10.3233\/JIFS-234196_ref9","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s40846-016-0188-y","article-title":"EMG Feature Set Selection Through Linear Relationship for GraspRecognition","volume":"36","author":"Kakoty","year":"2016","journal-title":"Journal of Medical and Biological Engineering"},{"issue":"1","key":"10.3233\/JIFS-234196_ref10","doi-asserted-by":"crossref","first-page":"861","DOI":"10.3233\/JIFS-171562","article-title":"EMG and IMU based real-time HCI using dynamic handgestures for a multiple-DoF robot arm","volume":"35","author":"Shin","year":"2018","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"4","key":"10.3233\/JIFS-234196_ref11","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/TMECH.2014.2360119","article-title":"A Low-Cost Real-Time Research Platform for EMG Pattern Recognition-Based Prosthetic Hand","volume":"20","author":"Geethanjali","year":"2015","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"issue":"2","key":"10.3233\/JIFS-234196_ref12","doi-asserted-by":"crossref","first-page":"187","DOI":"10.3390\/su10010187","article-title":"The Influencing Factors, Regional Difference and Temporal Variation of Industrial Technology Innovation: Evidence with the FOA-GRNN Model","volume":"10","author":"Zhang","year":"2018","journal-title":"Sustainability"},{"key":"10.3233\/JIFS-234196_ref13","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. 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