{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:34:41Z","timestamp":1776890081888,"version":"3.51.2"},"reference-count":59,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,5]]},"DOI":"10.1109\/icai52203.2021.9445257","type":"proceedings-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T16:48:10Z","timestamp":1622825290000},"page":"140-145","source":"Crossref","is-referenced-by-count":23,"title":["A Brief Review of Strategies Used for EMG Signal Classification"],"prefix":"10.1109","author":[{"given":"Shehla","family":"Inam","sequence":"first","affiliation":[]},{"given":"Sana Al","family":"Harmain","sequence":"additional","affiliation":[]},{"given":"Shehzaib","family":"Shafique","sequence":"additional","affiliation":[]},{"given":"Mafia","family":"Afzal","sequence":"additional","affiliation":[]},{"given":"Arfa","family":"Rabail","sequence":"additional","affiliation":[]},{"given":"Faisal","family":"Amin","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Waqar","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"1","article-title":"Selection of Spectrogram’s Best Window Size in EMG Signal during Core Lifting Task","volume":"10","author":"shair","year":"0","journal-title":"Journal of Telecommunication Electronic and Computer Engineering"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.5755\/j01.eee.19.3.3697"},{"key":"ref33","doi-asserted-by":"crossref","DOI":"10.3390\/app7111163","article-title":"Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand","author":"trongmun jiralerspong","year":"2017","journal-title":"Applied Sciences"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3390\/s18082497"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ISSPIT.2017.8388653"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.24191\/ieesr.v9i1.1247"},{"key":"ref37","first-page":"287","article-title":"The effects of load, flexion, twisting and window size on the stationarity of trunk","author":"kim","year":"2012","journal-title":"International Journal of Industrial Ergonomics"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2009.09.006"},{"key":"ref35","first-page":"10731","article-title":"Toward improved control of prosthetic fingers using surface electromyogram","author":"rami","year":"2012","journal-title":"Expert Systems with Applications"},{"key":"ref34","first-page":"157","article-title":"Surface EMG analysis on normal subjects based on isometric voluntary contraction","author":"p","year":"2009","journal-title":"Journal of Electromyography and Kinesiology"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"175","DOI":"10.5114\/biolsport.2018.74193","article-title":"Muscular activity patterns of female and male athletes during the flat bench press","volume":"35","author":"przemys?aw pietraszewski","year":"2018","journal-title":"Biology of Sport"},{"key":"ref27","article-title":"EMG and Grip Force correlation at varying wrist angles for Dominant vs. nondominant hand","author":"r a","year":"2018","journal-title":"IEEE International Students’ Conference on Electrical Electronics and Computer Science (SCEECS)"},{"key":"ref29","article-title":"Comparison of surface and intramuscular EMG pattern recognition for simultaneous wrist\/hand motion classification","author":"hargrove","year":"2013","journal-title":"35th Annual International IEEE EMBS Conference"},{"key":"ref2","first-page":"11","article-title":"Techniques of EMG signal analysis: detection, processing, classification and applications","author":"raez mb","year":"2006","journal-title":"Biological Procedures Online"},{"key":"ref1","article-title":"Electromyography (EMG)","author":"by danielle moores","year":"0","journal-title":"Healthline"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.09.185"},{"key":"ref22","first-page":"48","article-title":"Lower Extremity EMG in Male and Female College Soccer Players during Single-Leg Landing","volume":"14","author":"j c g","year":"2005","journal-title":"Human Kinetics Journals"},{"key":"ref21","article-title":"EMG-based hand gesture recognition for realtime biosignal interfacing","author":"jonghwa kim","year":"2008","journal-title":"Proceedings of the 13th international conference on Intelligent user interfaces - IUI '08"},{"key":"ref24","article-title":"Stacked Sparse Autoencoders for EMG-Based Classification of Hand Motions: A Comparative Multi Day Analyses between Surface and Intramuscular EMG","author":"m z u r","year":"2018","journal-title":"Applied Sciences"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.procs.2018.10.333","article-title":"Automated EMG Signal Classification for Diagnosis of Neuromuscular Disorders Using DWT and Bagging","volume":"140","author":"a s","year":"2018","journal-title":"Procedia Computer Science"},{"key":"ref26","article-title":"EMG signal analysis for comprehension of genders differences behavior during pre-motor activity","author":"jebara","year":"2016","journal-title":"Internatio)nal Symposium on Signal Image Video and Communications (ISIVC)"},{"key":"ref25","article-title":"Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors","author":"a p","year":"2018","journal-title":"SENSORS"},{"key":"ref50","first-page":"4832","article-title":"EMG feature evaluation for improving myoelectric pattern recognition robustness","author":"a p","year":"2013","journal-title":"Expert Systems with Application"},{"key":"ref51","first-page":"1526","article-title":"Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions","volume":"23","author":"a w","year":"2018","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"ref59","first-page":"13","author":"n r","year":"2020","journal-title":"classification of human hand movements based on EMG signals using nonlinear dimensionality reduction data fusion techniques and"},{"key":"ref58","first-page":"12","author":"t t","year":"2020","journal-title":"Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition"},{"key":"ref57","first-page":"9","author":"j q","year":"2019","journal-title":"Surface EMG hand gesture recognition system based on PCA and GRNN"},{"key":"ref56","author":"h s","year":"2020","journal-title":"Hand gesture recognition using surface electromyography"},{"key":"ref55","author":"m j","year":"2020","journal-title":"EMG-Based Hand Gesture Classification with Long Short-Term Memory Deep Recurrent Neural Network"},{"key":"ref54","first-page":"100","author":"s a","year":"2019","journal-title":"Evaluation of surface EMG-based recognition algorithms for decoding hand movements"},{"key":"ref53","article-title":"A Multiday Evaluation of Real-Time Intramuscular","author":"a w","year":"2020","journal-title":"SENSORS"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCN.2009.5235366"},{"key":"ref10","first-page":"51","article-title":"Muscle fibre orientation of abdominal muscles and suggested surface EMG electrode positions","volume":"38","author":"ng jk","year":"1998","journal-title":"Electromyography Clinical Neurophysiology"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s00422-008-0278-1"},{"key":"ref40","article-title":"Portable EMG Data Acquisition Module for Upper Limb Prosthesis Application","volume":"18","author":"joshi","year":"2018","journal-title":"IEEE Sensors Journal"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICASET.2018.8376887"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.2147\/MDER.S91102"},{"key":"ref14","article-title":"Continuous myoelectric control for powered prostheses using hidden Markov models","author":"englehart","year":"2005","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"ref15","article-title":"A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand","author":"chu","year":"2006","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"ref16","article-title":"Comparison of surface andintramuscular EMG pattern recognition for simultaneous wrist\/hand motion classification,” in tern recognition for simultaneous wrist\/hand motion classification","author":"hargrove","year":"2013","journal-title":"Engineering in Medicine and Biology Society (EMBC)"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2006.889192"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2013.2248750"},{"key":"ref19","article-title":"Probability Density Functions of Stationary Surface EMG Signals in Noisy Environments","author":"s t","year":"2016","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"ref4","article-title":"Assessing aberrant muscle activity patterns via the analysis of surface EMG data collected during a functional evaluation","author":"f n g","year":"2019","journal-title":"BMC Musculoskeletal Disorders"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinbiomech.2009.01.010"},{"key":"ref6","article-title":"Rehabilitation of Upper Extremity Nerve Injuries Using Surface EMG Biofeedback: Protocols for Clinical Application","author":"agnes sturma","year":"2018","journal-title":"Frontiers in Neuroscience"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICEE.2018.8472613"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2017.10.002"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2003.10.014"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/AQTR.2018.8402774"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/S1050-6411(98)00021-2"},{"key":"ref46","first-page":"193","article-title":"The effects of a fatiguing lifting task on postural sway among males and females","author":"h m b","year":"2018","journal-title":"Human Movement Science"},{"key":"ref45","article-title":"An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier","author":"a m","year":"2018"},{"key":"ref48","first-page":"1","article-title":"Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults From Vibration Signal","volume":"17","author":"geetanjhali","year":"2017","journal-title":"IEEE Sensors Journal"},{"key":"ref47","article-title":"Determination of optimum threshold values for EMG time domain features; a multi-dataset","author":"ernest nlandu kamavuako","year":"2016","journal-title":"Neural Engineering"},{"key":"ref42","article-title":"Exploring the Relation Between EMG Pattern Recognition","author":"j t","year":"2018","journal-title":"Journal of Electrical Engineering & Technology"},{"key":"ref41","article-title":"Effect of Window Conditioning Parameters on the Classification","author":"m g a","year":"2018","journal-title":"Proceedings of the 2018 IEEE"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3390\/app7111163"},{"key":"ref43","article-title":"A Study of Movement Classification of the Lower","author":"d c t -p","year":"2019","journal-title":"Electronics"}],"event":{"name":"2021 International Conference on Artificial Intelligence (ICAI)","location":"Islamabad, Pakistan","start":{"date-parts":[[2021,4,5]]},"end":{"date-parts":[[2021,4,7]]}},"container-title":["2021 International Conference on Artificial Intelligence (ICAI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9445208\/9445198\/09445257.pdf?arnumber=9445257","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T11:42:03Z","timestamp":1652182923000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9445257\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,5]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1109\/icai52203.2021.9445257","relation":{},"subject":[],"published":{"date-parts":[[2021,4,5]]}}}