{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:40:34Z","timestamp":1787499634961,"version":"build-2736575974"},"reference-count":208,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T00:00:00Z","timestamp":1584057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["2014-04920"],"award-info":[{"award-number":["2014-04920"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000240","name":"New Brunswick Innovation Foundation","doi-asserted-by":"publisher","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}],"id":[{"id":"10.13039\/501100000240","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.<\/jats:p>","DOI":"10.3390\/s20061613","type":"journal-article","created":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T08:13:27Z","timestamp":1584519207000},"page":"1613","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":124,"title":["Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5399-4318","authenticated-orcid":false,"given":"Evan","family":"Campbell","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada"},{"name":"Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0170-3245","authenticated-orcid":false,"given":"Angkoon","family":"Phinyomark","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4421-1016","authenticated-orcid":false,"given":"Erik","family":"Scheme","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada"},{"name":"Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1097\/PHM.0b013e3180383cc5","article-title":"Electromyography-Controlled Exoskeletal Upper-Limb\u2013Powered Orthosis for Exercise Training After Stroke","volume":"86","author":"Stein","year":"2007","journal-title":"Am. J. Phys. Med. Rehabil."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1177\/0954411918808322","article-title":"First prototype of EMG-controlled power hand orthosis for restoring hand extension in stroke patients","volume":"232","author":"Fardipour","year":"2018","journal-title":"Proc. Inst. Mech. Eng. Part H J. Eng. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"316","DOI":"10.4103\/0256-4602.83552","article-title":"A Review of Control Methods for Electric Power Wheelchairs Based on Electromyography Signals with Special Emphasis on Pattern Recognition","volume":"28","author":"Phinyomark","year":"2011","journal-title":"IETE Tech. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/TBME.2006.889175","article-title":"Myoelectric Signal Classification for Phoneme-Based Speech Recognition","volume":"54","author":"Scheme","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_5","unstructured":"Mart\u00ednez-Trinidad, J.F., Carrasco Ochoa, J.A., and Kittler, J. (2006). Practical Considerations for Real-Time Implementation of Speech-Based Gender Detection. Progress in Pattern Recognition, Image Analysis and Applications, Springer."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Summa, S., Gori, R., Freda, L., Castelli, E., and Petrarca, M. (2019). Development of a Dynamic Oriented Rehabilitative Integrated System (DORIS) and Preliminary Tests. Sensors, 19.","DOI":"10.3390\/s19153402"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7110","DOI":"10.3390\/s110707110","article-title":"Steering a Tractor by Means of an EMG-Based Human-Machine Interface","volume":"11","year":"2011","journal-title":"Sensors"},{"key":"ref_8","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":"Reaz","year":"2006","journal-title":"Biol. Proced. Online"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"545","DOI":"10.3389\/fphys.2019.00545","article-title":"The Relationship Between Blood Flow and Motor Unit Firing Rates in Response to Fatiguing Exercise Post-stroke","volume":"10","author":"Murphy","year":"2019","journal-title":"Front. Physiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1682\/JRRD.2010.09.0177","article-title":"Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use","volume":"48","author":"Scheme","year":"2011","journal-title":"J. Rehabil. Res. Dev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"10150","DOI":"10.1109\/ACCESS.2019.2891350","article-title":"Intelligent EMG Pattern Recognition Control Method for Upper-Limb Multifunctional Prostheses: Advances, Current Challenges, and Future Prospects","volume":"7","author":"Samuel","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"58","DOI":"10.3389\/fnbot.2018.00058","article-title":"Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses","volume":"12","author":"Kyranou","year":"2018","journal-title":"Front. Neurorobotics"},{"key":"ref_13","unstructured":"Naik, G. (2020). Surface Electromyography (EMG) Signal Processing, Classification, and Practical Considerations. Biomedical Signal Processing: Advances in Theory, Algorithms and Applications, Springer."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.bspc.2007.07.009","article-title":"Myoelectric control systems\u2014A survey","volume":"2","author":"Oskoei","year":"2007","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S1050-6411(00)00027-4","article-title":"Development of recommendations for SEMG sensors and sensor placement procedures","volume":"10","author":"Hermens","year":"2000","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1109\/TBME.2011.2159216","article-title":"The Effects of Electrode Size and Orientation on the Sensitivity of Myoelectric Pattern Recognition Systems to Electrode Shift","volume":"58","author":"Young","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Phinyomark, A., Quaine, F., and Laurillau, Y. (2014). The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human\u2014Computer Interface. Applications, Challenges, and Advancements in Electromyography Signal Processing, IGI Global.","DOI":"10.4018\/978-1-4666-6090-8.ch015"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Campbell, E., Phinyomark, A., Al-Timemy, A.H., Khushaba, R.N., Petri, G., and Scheme, E. (2019, January 20\u201323). Differences in EMG Feature Space between Able-Bodied and Amputee Subjects for Myoelectric Control. Proceedings of the 9th International IEEE\/EMBS Conference on Neural Engineering (NER), San Francisco, CA, USA.","DOI":"10.1109\/NER.2019.8717161"},{"key":"ref_20","unstructured":"Campbell, E., Phinyomark, A., and Scheme, E. (2003). A Comparison of Amputee and Able-Bodied Inter-Subject Variability In Myoelectric Control. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1109\/TNSRE.2011.2163529","article-title":"Resolving the Limb Position Effect in Myoelectric Pattern Recognition","volume":"19","author":"Fougner","year":"2011","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Radmand, A., Scheme, E., and Englehart, K. (2014, January 26\u201330). A characterization of the effect of limb position on EMG features to guide the development of effective prosthetic control schemes. Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6943678"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.eswa.2016.05.031","article-title":"Combined influence of forearm orientation and muscular contraction on EMG pattern recognition","volume":"61","author":"Khushaba","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1097\/JPO.0b013e318289950b","article-title":"Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition Based Myoelectric Control","volume":"25","author":"Scheme","year":"2013","journal-title":"J. Prosthet. Orthot."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"12431","DOI":"10.3390\/s130912431","article-title":"Surface Electromyography Signal Processing and Classification Techniques","volume":"13","author":"Chowdhury","year":"2013","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1016\/j.jbiomech.2010.01.027","article-title":"Filtering the surface EMG signal: Movement artifact and baseline noise contamination","volume":"43","author":"Luca","year":"2010","journal-title":"J. Biomech."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1109\/TBME.2008.2010392","article-title":"The Effect of ECG Interference on Pattern-Recognition- Based Myoelectric Control for Targeted Muscle Reinnervated Patients","volume":"56","author":"Hargrove","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1088\/0967-3334\/28\/4\/006","article-title":"Real time ECG artifact removal for myoelectric prosthesis control","volume":"28","author":"Zhou","year":"2007","journal-title":"Physiol. Meas."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"45","DOI":"10.2478\/v10048-011-0009-y","article-title":"Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification","volume":"11","author":"Phinyomark","year":"2011","journal-title":"Meas. Sci. Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"27","DOI":"10.5755\/j01.eee.122.6.1816","article-title":"Feature extraction and reduction of wavelet transform coefficients for EMG pattern classification","volume":"122","author":"Phinyomark","year":"2012","journal-title":"Elektron. Elektrotech."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1142\/S0219477511000466","article-title":"Wavelet-based denoising algorithm for robust EMG pattern recognition","volume":"10","author":"Phinyomark","year":"2011","journal-title":"Fluct. Noise Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s11517-006-0100-y","article-title":"Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion","volume":"44","author":"Wang","year":"2006","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_33","first-page":"569","article-title":"Improving myoelectric signal classification using wavelet packets and principal components analysis","volume":"1","author":"Englehart","year":"1999","journal-title":"Proc. IEEE"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.bspc.2006.03.003","article-title":"EMG signal filtering based on empirical mode decomposition","volume":"1","author":"Andrade","year":"2006","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.medengphy.2012.10.009","article-title":"Filtering of surface EMG using ensemble empirical mode decomposition","volume":"35","author":"Zhang","year":"2013","journal-title":"Med. Eng. Phys."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TNSRE.2015.2454503","article-title":"Single-channel EMG classification with ensemble-empirical-mode- decomposition-based ICA for diagnosing neuromuscular disorders","volume":"24","author":"Naik","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s11760-018-1374-x","article-title":"Local binary patterns for noise-tolerant sEMG classification","volume":"13","author":"Tabatabaei","year":"2019","journal-title":"Signal Image Video Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1007\/s11517-015-1443-z","article-title":"A novel approach for SEMG signal classification with adaptive local binary patterns","volume":"54","author":"Kaya","year":"2016","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tabatabaei, S.M., and Chalechale, A. (2018, January 25\u201326). One Dimensional Second Order Derivative Local Binary Pattern for Hand Gestures Classification Using sEMG Signals. Proceedings of the 8th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran.","DOI":"10.1109\/ICCKE.2018.8566385"},{"key":"ref_40","unstructured":"Liu, L., Liu, P., Clancy, E.A., Scheme, E., and Englehart, K.B. (September, January 28). Whitening of the electromyogram for improved classification accuracy in prosthesis control. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA."},{"key":"ref_41","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":"IEEE Trans. Biomed. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Campbell, E.D., Phinyomark, A., and Scheme, E. (2019, January 11\u201314). Linear Discriminant Analysis with Bayesian Risk Parameters for Myoelectric Control. Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP) (GlobalSIP 2019), Ottawa, ON, Canada.","DOI":"10.1109\/GlobalSIP45357.2019.8969237"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1109\/TBME.2013.2296274","article-title":"Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control","volume":"61","author":"Goebel","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Al-Timemy, A.H., Bugmann, G., and Escudero, J. (2018). Adaptive Windowing Framework for Surface Electromyogram- Based Pattern Recognition System for Transradial Amputees. Sensors, 18.","DOI":"10.3390\/s18082402"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Robertson, J.W., Englehart, K.B., and Scheme, E.J. (2018, January 17\u201321). Rejection of Systemic and Operator Errors in a Real-Time Myoelectric Control Task. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513529"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Robertson, J., Scheme, E., and Englehart, K. (2018). Effects of Confidence-Based Rejection on Usability and Error in Pattern Recognition-Based Myoelectric Control. IEEE J. Biomed. Health Inform., 1.","DOI":"10.1109\/JBHI.2018.2878907"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/1475-925X-9-72","article-title":"Evaluation of EMG processing techniques using information theory","volume":"9","author":"Politti","year":"2010","journal-title":"Biomed. Eng. Online"},{"key":"ref_49","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 Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1088\/0967-3334\/24\/2\/307","article-title":"Evaluation of the forearm EMG signal features for the control of a prosthetic hand","volume":"24","author":"Boostani","year":"2003","journal-title":"Physiol. Meas."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1109\/86.481972","article-title":"EMG feature evaluation for movement control of upper extremity prostheses","volume":"3","author":"Wheeler","year":"1995","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_52","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_53","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_54","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wang, Y., and Han, R.P.S. (2010, January 10\u201312). Wavelet transform theory and its application in EMG signal processing. Proceedings of the Seventh International Conference on Fuzzy Systems and Knowledge Discovery, Shandong, China.","DOI":"10.1109\/FSKD.2010.5569532"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1109\/TIE.2015.2497212","article-title":"sEMG-Based Identification of Hand Motion Commands Using Wavelet Neural Network Combined With Discrete Wavelet Transform","volume":"63","author":"Duan","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s11517-006-0051-3","article-title":"MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition","volume":"44","author":"Ren","year":"2006","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"1350016","DOI":"10.1142\/S0219477513500168","article-title":"EMG amplitude estimators based on probability distribution for muscle\u2013computer interface","volume":"12","author":"Phinyomark","year":"2013","journal-title":"Fluct. Noise Lett."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Thongpanja, S., Phinyomark, A., Phukpattaranont, P., and Limsakul, C. (2013). Mean and Median Frequency of EMG Signal to Determine Muscle Force Based on Time-Dependent Power Spectrum. Elektron. Elektrotech., 19.","DOI":"10.5755\/j01.eee.19.3.3697"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"263","DOI":"10.2306\/scienceasia1513-1874.2015.41.263","article-title":"The effects of the force of contraction and elbow joint angle on mean and median frequency analysis for muscle fatigue evaluation","volume":"41","author":"Thongpanja","year":"2015","journal-title":"ScienceAsia"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"82","DOI":"10.2478\/v10048-012-0015-8","article-title":"Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification","volume":"12","author":"Phinyomark","year":"2012","journal-title":"Meas. Sci. Rev."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol.-Heart Circ. Physiol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.cmpb.2014.06.013","article-title":"Feature extraction of the first difference of EMG time series for EMG pattern recognition","volume":"117","author":"Phinyomark","year":"2014","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/86.736154","article-title":"EMG pattern recognition based on artificial intelligence techniques","volume":"6","author":"Park","year":"1998","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.cap.2010.11.051","article-title":"Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions","volume":"11","author":"Kim","year":"2011","journal-title":"Curr. Appl. Phys."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/0165-0270(94)00164-C","article-title":"Fractal analysis of the electromyographic interference pattern","volume":"58","author":"Gitter","year":"1995","journal-title":"J. Neurosci. Methods"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1142\/S0219477511000570","article-title":"Electromyography (EMG) signal classification based on detrended fluctuation analysis","volume":"10","author":"Phinyomark","year":"2011","journal-title":"Fluct. Noise Lett."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/1743-0003-7-53","article-title":"Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors","volume":"7","author":"Arjunan","year":"2010","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S1386-5056(97)00029-4","article-title":"Fractal analysis of surface EMG signals from the biceps","volume":"45","author":"Gupta","year":"1997","journal-title":"Int. J. Med. Inform."},{"key":"ref_70","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_71","unstructured":"Asogbon, M.G., Samuel, O.W., Geng, Y., Idowu, P.O., Chen, S., R, N.G., Feng, P., and Li, G. (2018, January 21\u201323). Enhancing the Robustness of EMG-PR Based System against the Combined Influence of Force Variation and Subject Mobility. Proceedings of the 2018 3rd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), Singapore."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/0013-4694(70)90143-4","article-title":"EEG analysis based on time domain properties","volume":"29","author":"Hjorth","year":"1970","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_73","first-page":"650","article-title":"Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees","volume":"24","author":"Khushaba","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Phinyomark, A., Khushaba, R.N., Ib\u00e1\u00f1ez-Marcelo, E., Patania, A., Scheme, E., and Petri, G. (2017). Navigating features: A topologically informed chart of electromyographic features space. J. R. Soc. Interface, 14.","DOI":"10.1098\/rsif.2017.0734"},{"key":"ref_75","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_76","doi-asserted-by":"crossref","unstructured":"Saponas, T.S., Tan, D.S., Morris, D., and Balakrishnan, R. (2008, January 5\u201310). Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Florence, Italy.","DOI":"10.1145\/1357054.1357138"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"11156","DOI":"10.1016\/j.eswa.2012.03.039","article-title":"Fractal analysis features for weak and single-channel upper-limb EMG signals","volume":"39","author":"Phinyomark","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Qingju, Z., and Zhizeng, L. (2006, January 25\u201328). Wavelet de-noising of electromyography. Proceedings of the IEEE International Conference on Mechatronics and Automation, Henan, China.","DOI":"10.1109\/ICMA.2006.257406"},{"key":"ref_79","unstructured":"Du, S., and Vuskovic, M. (2004, January 1\u20133). Temporal vs. spectral approach to feature extraction from prehensile EMG signals. Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, Las Vegas, NV, USA."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1152\/jappl.1995.79.5.1803","article-title":"Automatic assessment of electromyogram quality","volume":"79","author":"Sinderby","year":"1995","journal-title":"J. Appl. Physiol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1109\/TNSRE.2014.2299573","article-title":"Identification of contaminant type in surface electromyography (EMG) signals","volume":"22","author":"McCool","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"645","DOI":"10.2478\/v10178-011-0061-9","article-title":"Critical exponent analysis applied to surface EMG signals for gesture recognition","volume":"18","author":"Phinyomark","year":"2011","journal-title":"Metrol. Meas. Syst."},{"key":"ref_83","first-page":"24","article-title":"Evaluation of movement types and electrode positions for EMG pattern classification based on linear and non-linear features","volume":"62","author":"Phinyomark","year":"2011","journal-title":"Eur. J. Sci. Res"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1109\/TIM.2016.2534378","article-title":"Probability density functions of stationary surface EMG signals in noisy environments","volume":"65","author":"Thongpanja","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Van Den Broek, E.L., Schut, M.H., Westerink, J.H., van Herk, J., and Tuinenbreijer, K. (2006, January 7\u201313). Computing emotion awareness through facial electromyography. Proceedings of the European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11754336_6"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1016\/j.jelekin.2008.05.004","article-title":"Fractal analysis of surface electromyography signals: A novel power spectrum-based method","volume":"19","author":"Talebinejad","year":"2009","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Oskoei, M.A., and Hu, H. (2006, January 17\u201320). GA-based feature subset selection for myoelectric classification. Proceedings of the IEEE International Conference on Robotics and Biomimetics, Kunming, China.","DOI":"10.1109\/ROBIO.2006.340145"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1450003","DOI":"10.1142\/S0218348X14500030","article-title":"Applications of variance fractal dimension: A survey","volume":"22","author":"Phinyomark","year":"2014","journal-title":"Fractals"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Khushaba, R.N., Al-Ani, A., Al-Timemy, A., and Al-Jumaily, A. (2016, January 6\u20139). A fusion of time-domain descriptors for improved myoelectric hand control. Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece.","DOI":"10.1109\/SSCI.2016.7850064"},{"key":"ref_90","unstructured":"He, J., Zhang, D., Sheng, X., Meng, J., and Zhu, X. (2013, January 3\u20137). Improved discrete fourier transform based spectral feature for surface electromyogram signal classification. Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Khushaba, R.N., Krasoulis, A., Al-Jumaily, A., and Nazarpour, K. (2018, January 17\u201321). Spatio-Temporal Inertial Measurements Feature Extraction Improves Hand Movement Pattern Recognition without Electromyography. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512638"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.cmpb.2005.04.001","article-title":"Classification of surface EMG signal using relative wavelet packet energy","volume":"79","author":"Hu","year":"2005","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/1350-4533(96)00006-9","article-title":"Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface","volume":"18","author":"Chang","year":"1996","journal-title":"Med. Eng. Phys."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Khushaba, R.N., Shi, L., and Kodagoda, S. (2012, January 2\u20135). Time-dependent spectral features for limb position invariant myoelectric pattern recognition. Proceedings of the 2012 International Symposium on Communications and Information Technologies (ISCIT), Gold Coast, Australia.","DOI":"10.1109\/ISCIT.2012.6380840"},{"key":"ref_95","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_96","first-page":"874","article-title":"Invariant Surface EMG Feature Against Varying Contraction Level for Myoelectric Control Based on Muscle Coordination","volume":"19","author":"He","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1016\/j.medengphy.2014.04.003","article-title":"EMG feature assessment for myoelectric pattern recognition and channel selection: A study with incomplete spinal cord injury","volume":"36","author":"Liu","year":"2014","journal-title":"Med. Eng. Phys."},{"key":"ref_98","unstructured":"Negi, S., Kumar, Y., and Mishra, V.M. (October, January 30). Feature extraction and classification for EMG signals using linear discriminant analysis. Proceedings of the 2nd International Conference on Advances in Computing, Communication, Automation (ICACCA) (Fall), Bareillym, India."},{"key":"ref_99","unstructured":"Ververidis, D., and Kotropoulos, C. (2005, January 4\u20138). Sequential forward feature selection with low computational cost. Proceedings of the 13th European Signal Processing Conference, Antalya, Turkey."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"5684","DOI":"10.1109\/TIP.2015.2479559","article-title":"Dimensionality reduction by integrating sparse representation and Fisher criterion and its applications","volume":"24","author":"Gao","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_101","unstructured":"Lee, D.D., and Seung, H.S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Thompson, B. (2005). Canonical correlation analysis. Encyclopedia of Statistics in Behavioral Science, Wiley.","DOI":"10.1002\/0470013192.bsa068"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s10916-005-5184-7","article-title":"Classification of EMG signals using PCA and FFT","volume":"29","year":"2005","journal-title":"J. Med. Syst."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1126\/science.295.5552.7a","article-title":"The isomap algorithm and topological stability","volume":"295","author":"Balasubramanian","year":"2002","journal-title":"Science"},{"key":"ref_105","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_106","unstructured":"Hinton, G.E., and Zemel, R.S. (1994). Autoencoders, minimum description length and Helmholtz free energy. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02289694","article-title":"Nonmetric multidimensional scaling: A numerical method","volume":"29","author":"Kruskal","year":"1964","journal-title":"Psychometrika"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"C\u00f4t\u00e9-Allard, U., Campbell, E., Phinyomark, A., Laviolette, F., Gosselin, B., and Scheme, E. (2019). Interpreting deep learning features for myoelectric control: A comparison with handcrafted features. arXiv.","DOI":"10.3389\/fbioe.2020.00158"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"E67","DOI":"10.1111\/aor.13004","article-title":"EMG-based estimation of limb movement using deep learning with recurrent convolutional neural networks","volume":"42","author":"Xia","year":"2018","journal-title":"Artif. Organs"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/TNSRE.2012.2226189","article-title":"Validation of a selective ensemble-based classification scheme for myoelectric control using a three-dimensional Fitts\u2019 law test","volume":"21","author":"Scheme","year":"2012","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1016\/j.jelekin.2014.06.009","article-title":"On the usability of intramuscular EMG for prosthetic control: A Fitts\u2019 law approach","volume":"24","author":"Kamavuako","year":"2014","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/1743-0003-11-91","article-title":"A real-time comparison between direct control, sequential pattern recognition control and simultaneous pattern recognition control using a Fitts\u2019 law style assessment procedure","volume":"11","author":"Wurth","year":"2014","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Scheme, E., Fougner, A., Stavdahl, \u00d8., Chan, A.D.C., and Englehart, K. (September, January 31). Examining the adverse effects of limb position on pattern recognition based myoelectric control. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina.","DOI":"10.1109\/IEMBS.2010.5627638"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s11517-012-0979-4","article-title":"Effect of arm position on the prediction of kinematics from EMG in amputees","volume":"51","author":"Jiang","year":"2013","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Chen, L., Geng, Y., and Li, G. (2011, January 15\u201317). Effect of upper-limb positions on motion pattern recognition using electromyography. Proceedings of the 4th International Congress on Image and Signal Processing, Shanghai, China.","DOI":"10.1109\/CISP.2011.6100025"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, D., He, J., and Zhu, X. (2012, January 11\u201314). Effect of dynamic change of arm position on myoelectric pattern recognition. Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), Guangzhou, China.","DOI":"10.1109\/ROBIO.2012.6491176"},{"key":"ref_117","first-page":"1","article-title":"The Ewing Amputation: The First Human Implementation of the Agonist-Antagonist Myoneural Interface","volume":"6","author":"Clites","year":"2018","journal-title":"Plast. Reconstr. Surg."},{"key":"ref_118","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. Neuroeng. Rehabil."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"54","DOI":"10.5607\/en.2010.19.1.54","article-title":"Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures","volume":"19","author":"You","year":"2010","journal-title":"Exp Neurobiol"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Khushaba, R.N., Al-Timemy, A., and Kodagoda, S. (2015, January 25\u201329). Influence of multiple dynamic factors on the performance of myoelectric pattern recognition. Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milano, Italy.","DOI":"10.1109\/EMBC.2015.7318699"},{"key":"ref_121","unstructured":"Chen, W., Hosoda, K., Menegatti, E., Shimizu, M., and Wang, H. (2017). Improvement of EMG Pattern Recognition by Eliminating Posture-Dependent Components. Intelligent Autonomous Systems 14, Springer International Publishing."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1109\/JBHI.2015.2490718","article-title":"Classification of Multiple Finger Motions During Dynamic Upper Limb Movements","volume":"21","author":"Yang","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.bspc.2016.08.017","article-title":"Dynamic training protocol improves the robustness of PR-based myoelectric control","volume":"31","author":"Yang","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s12984-017-0246-x","article-title":"Resolving the effect of wrist position on myoelectric pattern recognition control","volume":"14","author":"Adewuyi","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Thampi, S.M., Mitra, S., Mukhopadhyay, J., Li, K.C., James, A.P., and Berretti, S. (2018). EMG Pattern Classification Using Neural Networks. Intelligent Systems Technologies and Applications, Springer International Publishing.","DOI":"10.1007\/978-3-319-68385-0"},{"key":"ref_126","unstructured":"Geng, Y., Chen, L., Tian, L., and Li, G. (2012, January 5\u20137). Comparison of electromyography and mechanomyogram in control of prosthetic system in multiple limb positions. Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong, China."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/1743-0003-9-74","article-title":"Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees","volume":"9","author":"Geng","year":"2012","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_128","unstructured":"Geng, Y., Zhang, F., Yang, L., Zhang, Y., and Li, G. (September, January 28). Reduction of the effect of arm position variation on real-time performance of motion classification. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1109\/TNSRE.2014.2304470","article-title":"Correlation Analysis of Electromyogram Signals for Multiuser Myoelectric Interfaces","volume":"22","author":"Khushaba","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.bspc.2014.05.001","article-title":"Quantification and solutions of arm movements effect on sEMG pattern recognition","volume":"13","author":"Liu","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Masters, M.R., Smith, R.J., Soares, A.B., and Thakor, N.V. (2014, January 26\u201330). Towards better understanding and reducing the effect of limb position on myoelectric upper-limb prostheses. Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944149"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1097\/JPO.0000000000000041","article-title":"On the Suitability of Integrating Accelerometry Data with Electromyography Signals for Resolving the Effect of Changes in Limb Position during Dynamic Limb Movement","volume":"26","author":"Radmand","year":"2014","journal-title":"J. Prosthet. Orthot."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Boschmann, A., and Platzner, M. (2014, January 26\u201330). Towards robust HD EMG pattern recognition: Reducing electrode displacement effect using structural similarity. Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944635"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.bspc.2016.01.011","article-title":"Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements","volume":"27","author":"Kanitz","year":"2016","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Betthauser, J.L., Hunt, C.L., Osborn, L.E., Kaliki, R.R., and Thakor, N.V. (2016, January 16\u201320). Limb-position robust classification of myoelectric signals for prosthesis control using sparse representations. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7592186"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Yu, Y., Sheng, X., Guo, W., and Zhu, X. (2017, January 26\u201330). Attenuating the impact of limb position on surface EMG pattern recognition using a mixed-LDA classifier. Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), Chicago, IL, USA.","DOI":"10.1109\/ROBIO.2017.8324629"},{"key":"ref_137","first-page":"10","article-title":"Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees","volume":"2017","author":"Yanjuan","year":"2017","journal-title":"BioMed Res. Int."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1109\/TNSRE.2018.2861465","article-title":"Classification of Transient Myoelectric Signals for the Control of Multi-Grasp Hand Prostheses","volume":"26","author":"Kanitz","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1097\/JPO.0000000000000121","article-title":"Multi-position training improves robustness of pattern recognition and reduces limb-position effect in prosthetic control","volume":"29","author":"Beaulieu","year":"2019","journal-title":"J. Prosthet. Orthot."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.eswa.2017.11.049","article-title":"Robust EMG pattern recognition in the presence of confounding factors: Features, classifiers and adaptive learning","volume":"96","author":"Gu","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s12984-017-0284-4","article-title":"Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements","volume":"14","author":"Krasoulis","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_142","unstructured":"Roa Romero, L.M. (2013, January 25\u201328). Evaluating Spatial Characteristics of Upper-Limb Movements from EMG Signals. Proceedings of the XIII Mediterranean Conference on Medical and Biological Engineering and Computing, Seville, Spain."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"74","DOI":"10.2174\/1874431101004020074","article-title":"Classification of Upper Limb Motions from Around-Shoulder Muscle Activities: Hand Biofeedback","volume":"4","author":"Horiuchi","year":"2010","journal-title":"Open Med. Informatics J."},{"key":"ref_144","unstructured":"Horiuchi, Y., Kishi, T., Gonzalez, J., and Yu, W. (2009, January 23\u201326). A study on classification of upper limb motions from around-shoulder muscle activities. Proceedings of the IEEE International Conference on Rehabilitation Robotics, Kyoto, Japan."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Liarokapis, M.V., Artemiadis, P.K., Katsiaris, P.T., Kyriakopoulos, K.J., and Manolakos, E.S. (2012, January 14\u201319). Learning human reach-to-grasp strategies: Towards EMG-based control of robotic arm-hand systems. Proceedings of the IEEE International Conference on Robotics and Automation, St Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6225047"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Liarokapis, M., Artemiadis, P., Katsiaris, P., and Kyriakopoulos, K. (2012, January 24\u201327). Learning Task-Specific Models for Reach to Grasp Movements: Towards EMG-based Teleoperation of Robotic Arm-Hand Systems. Proceedings of the 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Rome, Italy.","DOI":"10.1109\/BioRob.2012.6290724"},{"key":"ref_147","unstructured":"Rivela, D., Scannella, A., Pavan, E.E., Frigo, C.A., Belluco, P., and Gini, G. (November, January 31). Processing of surface EMG through pattern recognition techniques aimed at classifying shoulder joint movements. Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Agia Napa, Cyprus."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s12984-018-0396-5","article-title":"Decoding the grasping intention from electromyography during reaching motions","volume":"15","author":"Batzianoulis","year":"2018","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_149","first-page":"1","article-title":"Decoding natural reach-and-grasp actions from human EEG","volume":"15","author":"Schwarz","year":"2017","journal-title":"J. Neural Eng."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/TBME.2017.2719400","article-title":"Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning","volume":"65","author":"Betthauser","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Cheng, J., Wei, F., Li, C., Liu, Y., Liu, A., and Chen, X. (2018). Position-independent gesture recognition using sEMG signals via canonical correlation analysis. Comput. Biol. Med.","DOI":"10.1016\/j.compbiomed.2018.08.020"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neunet.2014.03.010","article-title":"Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features","volume":"55","author":"Khushaba","year":"2014","journal-title":"Neural Networks"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"101669","DOI":"10.1016\/j.bspc.2019.101669","article-title":"An experimental study on upper limb position invariant EMG signal classification based on deep neural network","volume":"55","author":"Mukhopadhyay","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"101626","DOI":"10.1016\/j.bspc.2019.101626","article-title":"Reducing the effect of wrist variation on pattern recognition of Myoelectric Hand Prostheses Control through Dynamic Time Warping","volume":"55","author":"Powar","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Liu, J., Ren, Y., Xu, D., Kang, S.H., and Zhang, L. (2019). EMG-Based Real-Time Linear-Nonlinear Cascade Regression Decoding of Shoulder, Elbow and Wrist Movements in Able-Bodied Persons and Stroke Survivors. IEEE Trans. Biomed. Eng., 1.","DOI":"10.1109\/TBME.2019.2935182"},{"key":"ref_156","unstructured":"Scheme, E., Biron, K., and Englehart, K. (September, January August). Improving myoelectric pattern recognition positional robustness using advanced training protocols. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0109943","article-title":"Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography","volume":"9","author":"Gazzoni","year":"2014","journal-title":"PLoS ONE"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1109\/TBME.2013.2250502","article-title":"Bilinear Modeling of EMG Signals to Extract User-Independent Features for Multiuser Myoelectric Interface","volume":"60","author":"Matsubara","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"C\u00f4t\u00e9-Allard, U., Fall, C.L., Campeau-Lecours, A., Gosselin, C., Laviolette, F., and Gosselin, B. (2017, January 5\u20138). Transfer learning for sEMG hand gestures recognition using convolutional neural networks. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8122854"},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"C\u00f4\u00e9-Allard, U., Latyr Fall, C., Drouin, A., Campeau-Lecours, A., Gosselin, C., Glette, K., Laviolette, F., and Gosselin, B. (2019). Deep Learning for Electromyographic Hand GestureSignal Classification Using Transfer Learning. arXiv.","DOI":"10.1109\/TNSRE.2019.2896269"},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Du, Y., Jin, W., Wei, W., Hu, Y., and Geng, W. (2017). Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation. Sensors, 17.","DOI":"10.3390\/s17030458"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3389\/fnbot.2019.00043","article-title":"Enhanced Performance for Multi-Forearm Movement Decoding Using Hybrid IMU\u2013sEMG Interface","volume":"13","author":"Shahzad","year":"2019","journal-title":"Front. Neurorobotics"},{"key":"ref_163","unstructured":"Campbell, E., Phinyomark, A., and Scheme, E. (2020). Differences in Perspective on Inertial Measurement Unit Sensor Integration in Myoelectric Control. arXiv."},{"key":"ref_164","first-page":"29A","article-title":"Reproducibility of electromyographic measurements with inserted wire electrodes and surface electrodes","volume":"79","author":"Buskirk","year":"1970","journal-title":"Acta Physiol. Scand."},{"key":"ref_165","first-page":"115","article-title":"Neural factors versus hypertrophy in the time course of muscle strength gain","volume":"58","author":"Moritani","year":"1979","journal-title":"Am. J. Phys. Med."},{"key":"ref_166","first-page":"263","article-title":"Reexamination of the relationship between the surface integrated electromyogram (IEMG) and force of isometric contraction","volume":"57","author":"Moritani","year":"1978","journal-title":"Am. J. Phys. Med."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1007\/BF00690890","article-title":"Motor unit activity and surface electromyogram power spectrum during increasing force of contraction","volume":"56","author":"Moritani","year":"1987","journal-title":"Eur. J. Appl. Physiol. Occup. Physiol."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/TNSRE.2013.2247421","article-title":"Motion Normalized Proportional Control for Improved Pattern Recognition-Based Myoelectric Control","volume":"22","author":"Scheme","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Yatsenko, D., McDonnall, D., and Guillory, K.S. (2007, January 23\u201326). Simultaneous, proportional, multi-axis prosthesis control using multichannel surface EMG. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4353749"},{"key":"ref_170","unstructured":"Rehbaum, H., Jiang, N., Paredes, L., Amsuess, S., Graimann, B., and Farina, D. (September, January 28). Real time simultaneous and proportional control of multiple degrees of freedom from surface EMG: Preliminary results on subjects with limb deficiency. Proceedings of the IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1038\/29528","article-title":"Signal-dependent noise determines motor planning","volume":"394","author":"Harris","year":"1998","journal-title":"Nature"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1152\/jn.2002.88.3.1533","article-title":"Sources of signal-dependent noise during isometric force production","volume":"88","author":"Jones","year":"2002","journal-title":"J. Neurophysiol."},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Al-Timemy, A.H., Bugmann, G., Escudero, J., and Outram, N. (2013, January 3\u20137). A preliminary investigation of the effect of force variation for myoelectric control of hand prosthesis. Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610859"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/S0003-9993(96)90157-4","article-title":"Biofeedback effect on electromyography responses in patients with spinal cord injury","volume":"77","author":"Brucker","year":"1996","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Halaki, M., and Ginn, K. (2012). Normalization of EMG signals: To normalize or not to normalize and what to normalize to?. Computational Intelligence in Electromyography Analysis\u2014A Perspective on Current Applications and Future Challenges, IntechOpen.","DOI":"10.5772\/49957"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1097\/01241398-199211000-00023","article-title":"Gait analysis: Normal and pathological function","volume":"12","author":"Perry","year":"1992","journal-title":"J. Pediatr. Orthop."},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Winter, D. (2017). EMG interpretation. Electromyography in Ergonomics, Routledge.","DOI":"10.1201\/9780203758670-4"},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1177\/036354658401200310","article-title":"An EMG analysis of the shoulder in pitching: A second report","volume":"12","author":"Jobe","year":"1984","journal-title":"Am. J. Sport. Med."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1016\/j.jbiomech.2004.02.005","article-title":"Angle-and gender-specific quadriceps femoris muscle recruitment and knee extensor torque","volume":"37","author":"Pincivero","year":"2004","journal-title":"J. Biomech."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/1050-6411(94)00014-X","article-title":"Normalization of surface EMG amplitude from the upper trapezius muscle in ergonomic studies\u2014A review","volume":"5","author":"Mathiassen","year":"1995","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1123\/jab.13.2.135","article-title":"The use of surface electromyography in biomechanics","volume":"13","year":"1997","journal-title":"J. Appl. Biomech."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/j.jelekin.2008.02.007","article-title":"Innervation zone shift at different levels of isometric contraction in the biceps brachii muscle","volume":"19","author":"Piitulainen","year":"2009","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_183","first-page":"469","article-title":"Power spectral density of the surface myoelectric signal of the biceps brachi as a function of static load. Electromyograph","volume":"25","author":"Gander","year":"1985","journal-title":"Clin. Neurophysiol."},{"key":"ref_184","first-page":"483","article-title":"The behaviour of the mean power frequency of the surface electromyogram in biceps brachii with increasing force and during fatigue. With special regard to the electrode distance","volume":"30","author":"Gerdle","year":"1990","journal-title":"Electromyogr. Clin. Neurophysiol."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"25","DOI":"10.2114\/jpa.15.25","article-title":"The influence of location of electrode on muscle fiber conduction velocity and EMG power spectrum during voluntary isometric contraction measured with surface array electrodes","volume":"15","author":"Li","year":"1996","journal-title":"Appl. Hum. Sci."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/S1050-6411(98)00042-X","article-title":"Repeatability of surface EMG variables during voluntary isometric contractions of the biceps brachii muscle","volume":"9","author":"Rainoldi","year":"1999","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jelekin.2007.03.010","article-title":"Surface EMG analysis on normal subjects based on isometric voluntary contraction","volume":"19","author":"Kaplanis","year":"2009","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1111\/j.1468-0394.2008.00483.x","article-title":"Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction","volume":"26","author":"Hussain","year":"2009","journal-title":"Expert Syst."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.brainresbull.2012.09.012","article-title":"A note on the probability distribution function of the surface electromyogram signal","volume":"90","author":"Nazarpour","year":"2013","journal-title":"Brain Res. Bull."},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Naik, G.R., Kumar, D.K., and Arjunan, S.P. (2011, January 6\u20139). Kurtosis and negentropy investigation of myo electric signals during different MVCs. Proceedings of the ISSNIP Biosignals and Biorobotics Conference 2011, Adelaide, Australia.","DOI":"10.1109\/BRC.2011.5740669"},{"key":"ref_191","unstructured":"Kaplanis, P., Pattichis, C., Hadjileontiadis, L., and Panas, S. (2000, January 29\u201331). Bispectral analysis of surface EMG. Proceedings of the 10th IEEE Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries, MeleCon 2000 (Cat. No. 00CH37099), Lemesos, Cyprus."},{"key":"ref_192","doi-asserted-by":"crossref","unstructured":"Li, X., Fang, P., Tian, L., and Li, G. (2017, January 11\u201315). Increasing the robustness against force variation in EMG motion classification by common spatial patterns. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Korea.","DOI":"10.1109\/EMBC.2017.8036848"},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.bspc.2014.03.006","article-title":"Real-time, simultaneous myoelectric control using visual target-based training paradigm","volume":"13","author":"Ameri","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.bspc.2014.08.004","article-title":"Continuous estimation of finger joint angles under different static wrist motions from surface EMG signals","volume":"14","author":"Pan","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.bspc.2019.02.010","article-title":"Robust feature sets for contraction level invariant control of upper limb myoelectric prosthesis","volume":"51","author":"Iqbal","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"375","DOI":"10.13005\/bpj\/1382","article-title":"Wavelet Packet Entropy Based Control of Myoelectric Prosthesis","volume":"11","author":"Iqbal","year":"2018","journal-title":"Biomed. Pharmacol. J."},{"key":"ref_197","unstructured":"Huang, Y., Wu, H., Liu, H., and Yin, Z. (2017). Towards Finger Gestures and Force Recognition Based on Wrist Electromyography and Accelerometers. Intelligent Robotics and Applications, Springer International Publishing."},{"key":"ref_198","doi-asserted-by":"crossref","unstructured":"Atoufi, B., Kamavuako, E., Hudgins, B., and Englehart, K. (2015, January 25\u201329). Classification of hand and wrist tasks of unknown force levels using muscle synergies. Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, Milan, Italy.","DOI":"10.1109\/EMBC.2015.7318695"},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TKDE.2007.190669","article-title":"SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis","volume":"20","author":"Cai","year":"2008","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_200","doi-asserted-by":"crossref","unstructured":"Powar, O.S., and Chemmangat, K. (2019). Dynamic time warping for reducing the effect of force variation on myoelectric control of hand prostheses. J. Electromyogr. Kinesiol.","DOI":"10.1016\/j.jelekin.2019.07.006"},{"key":"ref_201","doi-asserted-by":"crossref","unstructured":"Li, X., Xu, R., Samuel, O.W., Tian, L., Zou, H., Zhang, X., Chen, S., Fang, P., and Li, G. (2016, January 16\u201320). A new approach to mitigate the effect of force variation on pattern recognition for myoelectric control. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591039"},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.jelekin.2004.09.001","article-title":"Prediction of handgrip forces using surface EMG of forearm muscles","volume":"15","author":"Hoozemans","year":"2005","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"475","DOI":"10.5405\/jmbe.1694","article-title":"Toward proportional control of myoelectric prostheses with muscle synergies","volume":"34","author":"Atoufi","year":"2014","journal-title":"J. Med. Biol. Eng."},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S1050-6411(02)00050-0","article-title":"EMG frequency content changes with increasing force and during fatigue in the quadriceps femoris muscle of men and women","volume":"13","author":"Bilodeau","year":"2003","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/S1050-6411(00)00046-8","article-title":"Mean frequency and signal amplitude of the surface EMG of the quadriceps muscles increase with increasing torque\u2014A study using the continuous wavelet transform","volume":"11","author":"Karlsson","year":"2001","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_206","doi-asserted-by":"crossref","unstructured":"Mizrahi, J. (2011). Classification of Upper Limb Motions from Around-Shoulder Muscle Activities. Advances in Applied Electromyography, IntechOpen. Chapter 3.","DOI":"10.5772\/883"},{"key":"ref_207","doi-asserted-by":"crossref","unstructured":"Luciw, M., Jarocka, E., and Edin, B. (2014). Multi-channel EEG recordings during 3936 grasp and lift trials with varying weight and friction. Sci. Data, 1.","DOI":"10.1038\/sdata.2014.47"},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"Ams\u00fcss, S., Paredes, L.P., Rudigkeit, N., Graimann, B., Herrmann, M.J., and Farina, D. (2013, January 3\u20137). Long term stability of surface EMG pattern classification for prosthetic control. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan.","DOI":"10.1109\/EMBC.2013.6610327"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1613\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:06:47Z","timestamp":1760173607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/6\/1613"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,13]]},"references-count":208,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20061613"],"URL":"https:\/\/doi.org\/10.3390\/s20061613","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202002.0415.v1","asserted-by":"object"}]},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,13]]}}}