{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T05:33:14Z","timestamp":1769751194523,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T00:00:00Z","timestamp":1637280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801122"],"award-info":[{"award-number":["61801122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801122"],"award-info":[{"award-number":["61801122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU), extensor carpi radialis longus (ECRL) and finger extensor (FE)) were selected to sample sEMG signals. Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) were chosen as signal eigenvalues; Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) were used to construct classification models, and finally, the decoding accuracies of the classification models were obtained under different influencing elements. The experimental results showed that the decoding accuracy of the classification model decreased by an average of 7%, 10%, and 13% considering muscle fatigue, forearm angle and acquisition time, respectively. Furthermore, the acquisition time had the biggest impact on decoding accuracy, with a maximum reduction of nearly 20%.<\/jats:p>","DOI":"10.3390\/s21227713","type":"journal-article","created":{"date-parts":[[2021,11,21]],"date-time":"2021-11-21T21:00:50Z","timestamp":1637528450000},"page":"7713","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time"],"prefix":"10.3390","volume":"21","author":[{"given":"Zengyu","family":"Qing","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, No.2 Xueyuan Road, Fuzhou 350116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0965-1120","authenticated-orcid":false,"given":"Zongxing","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, No.2 Xueyuan Road, Fuzhou 350116, China"}]},{"given":"Yingjie","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, No.2 Xueyuan Road, Fuzhou 350116, China"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Fuzhou University, No.2 Xueyuan Road, Fuzhou 350116, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,19]]},"reference":[{"key":"ref_1","unstructured":"Mohamed, A.K., Marwala, T., and John, L.R. (September, January 30). Single-trial EEG Discrimination between Wrist and Finger Movement Imagery and Execution in a Sensorimotor BCI. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1002\/ana.22613","article-title":"Electrocorticographic control of a prosthetic arm in paralyzed patients","volume":"71","author":"Yanagisawa","year":"2012","journal-title":"Ann. Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1088\/0967-3334\/30\/5\/002","article-title":"Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition for multifunction prosthesis control","volume":"30","author":"Xie","year":"2009","journal-title":"Physiol. Meas."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"39564","DOI":"10.1109\/ACCESS.2019.2906584","article-title":"A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction","volume":"7","author":"Simao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jaramillo-Y\u00e1nez, A., Benalc\u00e1zar, M.E., and Mena-Maldonado, E. (2020). Real-Time Hand Gesture decoding Using Surface Electromyography and Machine Learning: A Systematic Literature Review. Sensors, 20.","DOI":"10.3390\/s20092467"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Saponas, T.S., Tan, D.S., Dan, M., Turner, J., and Landay, J.A. (2010, January 10\u201315). Making Muscle-Computer Interfaces More Practical. Proceedings of the 28th International Conference on Human Factors in Computing Systems, CHI 2010, Atlanta, GA, USA.","DOI":"10.1145\/1753326.1753451"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/S1672-6529(16)60377-3","article-title":"Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand","volume":"14","author":"Wang","year":"2017","journal-title":"J. Bionic Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"37297","DOI":"10.1109\/ACCESS.2021.3062364","article-title":"Electromyography Based Decoding of Dexterous, In-Hand Manipulation of Objects: Comparing Task Execution in Real World and Virtual Reality","volume":"9","author":"Kwon","year":"2021","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TSMCB.2012.2185843","article-title":"An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot","volume":"42","author":"Kiguchi","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00521-018-3699-3","article-title":"Surface EMG data aggregation processing for intelligent prosthetic action recognition","volume":"32","author":"Li","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6343","DOI":"10.1007\/s00521-019-04142-8","article-title":"Surface EMG hand gesture recognition system based on PCA and GRNN","volume":"32","author":"Qi","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1109\/TNSRE.2019.2936622","article-title":"A Learning Scheme for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions","volume":"27","author":"Dwivedi","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kwon, Y., Dwivedi, A., Mcdaid, A.J., and Liarokapis, M. (2018, January 18\u201321). On Muscle Selection for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512624"},{"key":"ref_14","first-page":"1257","article-title":"The Virtual Trackpad: An Electromyography-Based, Wireless, Real-Time, Low-Power, Embedded Hand-Gesture-Recognition System Using an Event-Driven Artificial Neural Network","volume":"64","author":"Liu","year":"2017","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, J., Pan, J., and Li, J. (2017, January 5\u20138). sEMG-based continuous hand gesture decoding using GMM-HMM and threshold model. Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), Macau, China.","DOI":"10.1109\/ROBIO.2017.8324631"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TRO.2016.2558193","article-title":"Design and Implementation of an Anthropomorphic Hand for Replicating Human Grasping Functions","volume":"32","author":"Xiong","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TCYB.2014.2386856","article-title":"A Study on Estimation of Joint Force Through Isometric Index Finger Abduction With the Help of SEMG Peaks for Biomedical Applications","volume":"46","author":"Na","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_18","first-page":"6680417","article-title":"Cross-Individual Gesture decoding Based on Long Short-Term Memory Networks","volume":"2021","author":"Min","year":"2021","journal-title":"Sci. Program."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1142\/S0219843619410019","article-title":"Facilitate sEMG-Based Human\u2013Machine Interaction Through Channel Optimization","volume":"16","author":"Wang","year":"2019","journal-title":"Int. J. Humanoid Robot."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/TNSRE.2019.2896269","article-title":"Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning","volume":"27","author":"Drouin","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/TNSRE.2015.2424371","article-title":"An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control","volume":"24","author":"Adewuyi","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/TNSRE.2009.2023282","article-title":"Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms","volume":"17","author":"Sensinger","year":"2009","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_23","unstructured":"Major, N., and Malinzak, M.D. (2010). Netter\u2019s Correlative Imaging: Musculoskeletal Anatomy, Elsevier Health Science."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.clinbiomech.2009.01.010","article-title":"Surface EMG based muscle fatigue evaluation in biomechanics","volume":"24","author":"Cifrek","year":"2009","journal-title":"Clin. Biomech."},{"key":"ref_25","unstructured":"(2018, January 12\u201313). Upper Limb Elbow Joint Angle Estimation Based on Electromyography Using Artificial Neural Network. Proceedings of the South East Asian Technical University Consortium, Yogyakarta, Indonesia."},{"key":"ref_26","unstructured":"Allard, U.C., Nougarou, F., Fall, C.L., Gigu\u00e8re, P., Gosselin, C., Laviolette, F., and Gosselin, B. (2016, January 9\u201314). A convolutional neural network for robotic arm guidance using sEMG based frequency-features. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots & Systems, Daejeon, Korea."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.bspc.2012.08.005","article-title":"Pattern recognition of number gestures based on a wireless surface EMG system","volume":"8","author":"Xun","year":"2013","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Villarejo, J.J., Costa, R.M., Bastos, T., and Frizera, A. (2014, January 26\u201328). Identification of low level sEMG signals for individual finger prosthesis. Proceedings of the Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), 5th ISSNIP-IEEE, Salvador, Brazil.","DOI":"10.1109\/BRC.2014.6880991"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chang, J., Chablat, D., Bennis, F., and Ma, L. (2016). Estimating the EMG Response Exclusively to Fatigue during Sustained Static Maximum Voluntary Contraction. Advances in Physical Ergonomics and Human Factors, Springer.","DOI":"10.1007\/978-3-319-41694-6_4"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hill, E., Housh, T., Smith, C., Schmidt, R., and Johnson, G. (2016). Muscle- and Mode-Specific Responses of the Forearm Flexors to Fatiguing, Concentric Muscle Actions. Sports, 4.","DOI":"10.3390\/sports4040047"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.bspc.2015.09.001","article-title":"Spectral Collaborative Representation based Classification for Hand Gestures recognition on Electromyography Signals","volume":"24","author":"Boyali","year":"2016","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.bspc.2007.07.009","article-title":"Myoelectric control systems: A survey","volume":"2","author":"Oskoei","year":"2007","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/1743-0003-8-25","article-title":"Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses","volume":"8","author":"Lorrain","year":"2011","journal-title":"J. Neuro Eng. Rehabil."},{"key":"ref_36","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. Neuro Eng. Rehabil."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dwivedi, A., Kwon, Y., Mcdaid, A.J., and Liarokapis, M. (2018, January 26\u201329). EMG Based Decoding of Object Motion in Dexterous. Proceedings of the 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), Enschede, The Netherlands.","DOI":"10.1109\/BIOROB.2018.8487222"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7713\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:32:58Z","timestamp":1760167978000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7713"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,19]]},"references-count":37,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21227713"],"URL":"https:\/\/doi.org\/10.3390\/s21227713","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,19]]}}}