{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:18:22Z","timestamp":1780553902847,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021YFB3200600"],"award-info":[{"award-number":["2021YFB3200600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2020YFC2004500"],"award-info":[{"award-number":["2020YFC2004500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["62073307"],"award-info":[{"award-number":["62073307"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["62273321"],"award-info":[{"award-number":["62273321"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["62204243"],"award-info":[{"award-number":["62204243"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["61774157"],"award-info":[{"award-number":["61774157"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["81771388"],"award-info":[{"award-number":["81771388"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2019-I2M-5-019"],"award-info":[{"award-number":["2019-I2M-5-019"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["8091A140106"],"award-info":[{"award-number":["8091A140106"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021SHZDZX"],"award-info":[{"award-number":["2021SHZDZX"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["2021YFB3200600"],"award-info":[{"award-number":["2021YFB3200600"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["2020YFC2004500"],"award-info":[{"award-number":["2020YFC2004500"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["62073307"],"award-info":[{"award-number":["62073307"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["62273321"],"award-info":[{"award-number":["62273321"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["62204243"],"award-info":[{"award-number":["62204243"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["61774157"],"award-info":[{"award-number":["61774157"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["81771388"],"award-info":[{"award-number":["81771388"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["2019-I2M-5-019"],"award-info":[{"award-number":["2019-I2M-5-019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["8091A140106"],"award-info":[{"award-number":["8091A140106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["2021SHZDZX"],"award-info":[{"award-number":["2021SHZDZX"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["2021YFB3200600"],"award-info":[{"award-number":["2021YFB3200600"]}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["2020YFC2004500"],"award-info":[{"award-number":["2020YFC2004500"]}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["62073307"],"award-info":[{"award-number":["62073307"]}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["62273321"],"award-info":[{"award-number":["62273321"]}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["62204243"],"award-info":[{"award-number":["62204243"]}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["61774157"],"award-info":[{"award-number":["61774157"]}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["81771388"],"award-info":[{"award-number":["81771388"]}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["2019-I2M-5-019"],"award-info":[{"award-number":["2019-I2M-5-019"]}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["8091A140106"],"award-info":[{"award-number":["8091A140106"]}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["2021SHZDZX"],"award-info":[{"award-number":["2021SHZDZX"]}]},{"name":"CAS Joint Fund for Equipment Pre-Research","award":["2021YFB3200600"],"award-info":[{"award-number":["2021YFB3200600"]}]},{"name":"CAS Joint Fund for Equipment Pre-Research","award":["2020YFC2004500"],"award-info":[{"award-number":["2020YFC2004500"]}]},{"name":"CAS Joint Fund for Equipment Pre-Research","award":["62073307"],"award-info":[{"award-number":["62073307"]}]},{"name":"CAS Joint Fund for Equipment Pre-Research","award":["62273321"],"award-info":[{"award-number":["62273321"]}]},{"name":"CAS Joint Fund for Equipment Pre-Research","award":["62204243"],"award-info":[{"award-number":["62204243"]}]},{"name":"CAS Joint Fund for Equipment Pre-Research","award":["61774157"],"award-info":[{"award-number":["61774157"]}]},{"name":"CAS Joint Fund for Equipment Pre-Research","award":["81771388"],"award-info":[{"award-number":["81771388"]}]},{"name":"CAS Joint Fund for Equipment Pre-Research","award":["2019-I2M-5-019"],"award-info":[{"award-number":["2019-I2M-5-019"]}]},{"name":"CAS Joint Fund for Equipment Pre-Research","award":["8091A140106"],"award-info":[{"award-number":["8091A140106"]}]},{"name":"CAS Joint Fund for Equipment Pre-Research","award":["2021SHZDZX"],"award-info":[{"award-number":["2021SHZDZX"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2021YFB3200600"],"award-info":[{"award-number":["2021YFB3200600"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2020YFC2004500"],"award-info":[{"award-number":["2020YFC2004500"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["62073307"],"award-info":[{"award-number":["62073307"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["62273321"],"award-info":[{"award-number":["62273321"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["62204243"],"award-info":[{"award-number":["62204243"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["61774157"],"award-info":[{"award-number":["61774157"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["81771388"],"award-info":[{"award-number":["81771388"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2019-I2M-5-019"],"award-info":[{"award-number":["2019-I2M-5-019"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["8091A140106"],"award-info":[{"award-number":["8091A140106"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2021SHZDZX"],"award-info":[{"award-number":["2021SHZDZX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. By using cross-session calibration and increasing the amount of training data, it is possible to reduce these variations. The impact of varying the amount of calibration and training data on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of the amount of training data on benchmark performance. Two amputees who had undergone amputations years prior were recruited, and seven sessions of data were collected for analysis from each of them. Ninapro DB6, a publicly available database containing data from ten healthy subjects across ten sessions, was also included in this study. The experimental results show that the calibration data improved the average accuracy by 3.03%, 6.16%, and 9.73% for the two subjects and Ninapro DB6, respectively, compared to the baseline results. Moreover, it was discovered that increasing the number of training sessions was more effective in improving accuracy than increasing the number of trials. Three potential strategies are proposed in light of these findings to enhance cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, while also offering effective strategies to maximize the utilization of the entire dataset.<\/jats:p>","DOI":"10.3390\/s24030920","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T10:44:24Z","timestamp":1706697864000},"page":"920","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Effects of Training and Calibration Data on Surface Electromyogram-Based Recognition for Upper Limb Amputees"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8637-5203","authenticated-orcid":false,"given":"Pan","family":"Yao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China"},{"name":"MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX3 9DU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaifeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Spinal Surgery, Peking University People\u2019s Hospital, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Spinal Surgery, Peking University People\u2019s Hospital, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusen","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3989-1819","authenticated-orcid":false,"given":"Tiezhu","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengdi","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Beijing University, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangyang","family":"Gou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3718-9364","authenticated-orcid":false,"given":"Chunxiu","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Xue","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1038\/s41563-021-00966-9","article-title":"Sensory feedback for limb prostheses in amputees","volume":"20","author":"Raspopovic","year":"2021","journal-title":"Nat. Mater."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4","DOI":"10.4103\/0972-6748.196041","article-title":"Psychological effects of amputation: A review of studies from India","volume":"25","author":"Sahu","year":"2016","journal-title":"Ind. Psychiatry J."},{"key":"ref_3","first-page":"99","article-title":"Lived experience of persons with an amputation of the upper limb","volume":"18","author":"Ligthelm","year":"2014","journal-title":"Int. J. Orthop. Trauma"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1038\/s42256-019-0093-5","article-title":"Shared human-robot proportional control of a dexterous myoelectric prosthesis","volume":"1","author":"Zhuang","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"056051","DOI":"10.1088\/1741-2552\/ac2a8d","article-title":"A portable, self-contained neuroprosthetic hand with deep learning-based finger control","volume":"18","author":"Nguyen","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2426","DOI":"10.1109\/TNSRE.2022.3199809","article-title":"Simultaneous sEMG Recognition of Gestures and Force Levels for Interaction With Prosthetic Hand","volume":"30","author":"Fang","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"066010","DOI":"10.1088\/1741-2552\/ac9e1c","article-title":"Use of regenerative peripheral nerve interfaces and intramuscular electrodes to improve prosthetic grasp selection: A case study","volume":"19","author":"Lee","year":"2022","journal-title":"J. Neural Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1016\/j.jhsa.2005.01.002","article-title":"Refined myoelectric control in below-elbow amputees using artificial neural networks and a data glove","volume":"30","author":"Sebelius","year":"2005","journal-title":"J. Hand Surg."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1109\/TNSRE.2012.2196711","article-title":"Control of upper limb prostheses: Terminology and proportional myoelectric control\u2014A review","volume":"20","author":"Fougner","year":"2012","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5450","DOI":"10.1109\/JBHI.2022.3197831","article-title":"Improving the robustness and adaptability of sEMG-based pattern recognition using deep domain adaptation","volume":"26","author":"Shi","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_11","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_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. Neurorobot."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2570","DOI":"10.1109\/TNSRE.2023.3281455","article-title":"LSTM-AE for Domain Shift Quantification in Cross-day Upper-limb Motion Estimation Using Surface Electromyography","volume":"31","author":"Bao","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Palermo, F., Cognolato, M., Gijsberts, A., M\u00fcller, H., Caputo, B., and Atzori, M. (2017, January 17\u201320). Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data. Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR), London, UK.","DOI":"10.1109\/ICORR.2017.8009405"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Furman, S., and Imtiaz, M.H. (2023, January 8\u201310). A Subject-Independent Machine Learning Model to Recognize Hand Gestures from Surface Electromyography Signals. Proceedings of the 2023 IEEE 32nd Microelectronics Design & Test Symposium (MDTS), Albany, NY, USA.","DOI":"10.1109\/MDTS58049.2023.10168147"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kanoga, S., Kanemura, A., and Asoh, H. (2020). Are armband sEMG devices dense enough for long-term use?\u2014Sensor placement shifts cause significant reduction in recognition accuracy. Biomed. Signal Process. Control, 60.","DOI":"10.1016\/j.bspc.2020.101981"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kanoga, S., Matsuoka, M., and Kanemura, A. (2018, January 18\u201321). Transfer learning over time and position in wearable myoelectric control systems. 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.2018.8512872"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1109\/TNSRE.2019.2907200","article-title":"Counteracting electrode shifts in upper-limb prosthesis control via transfer learning","volume":"27","author":"Prahm","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_19","unstructured":"Prahm, C., Paassen, B., Schulz, A., Hammer, B., and Aszmann, O. (2016). Converging Clinical and Engineering Research on Neurorehabilitation II, Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016), Segovia, Spain, 18\u201321 October 2016, Springer."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nazmi, N., Rahman, M.A.A., Yamamoto, S.I., Ahmad, S.A., Zamzuri, H., and Mazlan, S.A. (2016). A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions. Sensors, 16.","DOI":"10.3390\/s16081304"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.neucom.2022.04.084","article-title":"A comprehensive empirical review of modern voice activity detection approaches for movies and TV shows","volume":"494","author":"Sharma","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107381","DOI":"10.1016\/j.patcog.2020.107381","article-title":"BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs","volume":"105","author":"Wang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/0141-5425(82)90021-8","article-title":"Multifunctional prosthesis and orthosis control via microcomputer identification of temporal pattern differences in single-site myoelectric signals","volume":"4","author":"Graupe","year":"1982","journal-title":"J. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tolooshams, B., and Jiang, N. (2017). Robustness of Frequency Division Technique for Online Myoelectric Pattern Recognition against Contraction-Level Variation. Front. Bioeng. Biotechnol., 5.","DOI":"10.3389\/fbioe.2017.00003"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, B., and Kamavuako, E.N. (2021, January 8\u201310). Correlation between the stability of feature distribution and classification performance in sEMG signals. Proceedings of the 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART), Paris, France.","DOI":"10.1109\/BioSMART54244.2021.9677831"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, J., Jiang, X., Liu, X., Jia, F., and Dai, C. (2024). Optimizing the feature set and electrode configuration of high-density electromyogram via interpretable deep forest. Biomed. Signal Process. Control, 87.","DOI":"10.1016\/j.bspc.2023.105445"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"20676","DOI":"10.1109\/JSEN.2022.3204121","article-title":"Spectral image-based multiday surface electromyography classification of hand motions using CNN for human\u2013computer interaction","volume":"22","author":"Qureshi","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8989","DOI":"10.1109\/JSEN.2023.3255408","article-title":"E2CNN: An Efficient Concatenated CNN for Classification of Surface EMG Extracted From Upper Limb","volume":"23","author":"Qureshi","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zia ur Rehman, M., Gilani, S.O., Waris, A., Niazi, I.K., Slabaugh, G., Farina, D., and Kamavuako, E.N. (2018). Stacked sparse autoencoders for EMG-based classification of hand motions: A comparative multi day analyses between surface and intramuscular EMG. Appl. Sci., 8.","DOI":"10.3390\/app8071126"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Karrenbach, M., Preechayasomboon, P., Sauer, P., Boe, D., and Rombokas, E. (2022). Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMG. Front. Bioeng. Biotechnol., 10.","DOI":"10.3389\/fbioe.2022.1034672"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"379","DOI":"10.3389\/fnins.2017.00379","article-title":"Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network","volume":"11","author":"Zhai","year":"2017","journal-title":"Front. Neurosci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/920\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:52:18Z","timestamp":1760104338000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/920"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,31]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24030920"],"URL":"https:\/\/doi.org\/10.3390\/s24030920","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,31]]}}}