{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:10:36Z","timestamp":1776078636069,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hangzhou Key Scientific Research Program","award":["2025SZD1B26"],"award-info":[{"award-number":["2025SZD1B26"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface electromyography (sEMG) is widely used for gesture recognition, yet the way classic feature\u2013classifier pipelines fail under realistic signal degradations is still poorly quantified. Existing studies typically report accuracy on clean laboratory data, leaving open how amplitude saturation and channel dropout jointly affect different feature combinations, classifiers, and subjects. In this work, we provide, to our knowledge, the first systematic robustness map of a conventional sEMG pipeline under controlledclipping and single-sensor failure. sEMG from nine subjects performing a multi-session, multi-gesture protocol is windowed (250 ms, 50 ms hop) and represented using four common time-domain features (Root Mean Square, Variance, Zero Crossing, and Waveform Length). We exhaustively evaluated single features and all pairwise fusions with three standard classifiers (Support Vector Machine (RBF kernel), Linear Discriminant Analysis, and Random Forest) over (i) a sweep of symmetric saturation thresholds (10\u22126\u201310\u22121) and (ii) five single-channel dropout scenarios, reporting subject-wise dispersion rather than aggregate scores alone. This design enables explicit characterization of the following: (1) accuracy recovery as clipping weakens for each feature pair; (2) dependency of robustness on which channel fails; and (3) differences among Support Vector Machine, Linear Discriminant Analysis, and Random Forest under identical degradations. The results show that lightweight feature pairs (Root Mean Square + Waveform Length, Variance + Zero Crossing, and Waveform Length + Zero Crossing) coupled with Random Forest form a consistently robust operating point, with performance recovering as clipping weakens and remaining resilient under single-channel dropout. Beyond robustness, the conventional pipeline trains substantially faster than representative deep learning baselines under a unified end-to-end timing definition, supporting real-time recalibration and repeated robustness sweeps in wearable deployments.<\/jats:p>","DOI":"10.3390\/s26082386","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:19:14Z","timestamp":1776075554000},"page":"2386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the Resilience of sEMG Classifiers to Sensor Malfunction and Signal Saturation"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8617-1286","authenticated-orcid":false,"given":"Congyi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computing, Mathematics and Physics, University of Portsmouth, Portsmouth PO1 3HE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2363-9125","authenticated-orcid":false,"given":"Dalin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computing, Mathematics and Physics, University of Portsmouth, Portsmouth PO1 3HE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5794-8925","authenticated-orcid":false,"given":"Yinfeng","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxu","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computing, Mathematics and Physics, University of Portsmouth, Portsmouth PO1 3HE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaojie","family":"Ju","sequence":"additional","affiliation":[{"name":"School of Computing, Mathematics and Physics, University of Portsmouth, Portsmouth PO1 3HE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"ref_1","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_2","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_3","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_4","doi-asserted-by":"crossref","first-page":"102440","DOI":"10.1016\/j.jelekin.2020.102440","article-title":"Tutorial. Surface EMG Detection, Conditioning and Pre-Processing: Best Practices","volume":"54","author":"Merletti","year":"2020","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.bspc.2007.11.005","article-title":"A Training Strategy to Reduce Classification Degradation due to Electrode Displacements in Pattern Recognition Based Myoelectric Control","volume":"3","author":"Hargrove","year":"2008","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ketyk\u00f3, I., Kov\u00e1cs, F., and Varga, Z. (2019). Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks. arXiv.","DOI":"10.1109\/IJCNN.2019.8852018"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"100126","DOI":"10.1016\/j.health.2022.100126","article-title":"A Systematic Review on Surface Electromyography-Based Classification System for Identifying Hand and Finger Movements","volume":"3","author":"Sultana","year":"2023","journal-title":"Healthc. Anal."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pizzolato, S., Tagliapietra, L., Cognolato, M., Reggiani, M., M\u00fcller, H., and Atzori, M. (2017). Comparison of Six Electromyography Acquisition Setups on Hand Movement Classification Tasks. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0186132"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Hellara, H., Barioul, R., Sahnoun, S., Fakhfakh, A., and Kanoun, O. (2024). Comparative Study of sEMG Feature Evaluation Methods and Their Impact on Gesture Recognition Accuracy. Sensors, 24.","DOI":"10.3390\/s24113638"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jiang, X., Deng, Y., Zhang, B., and Chen, S. (2024). Posture-Invariant Myoelectric Control with Self-Calibrating Models. Front. Neurorobot., 18.","DOI":"10.3389\/fnbot.2024.1462023"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/TNSRE.2014.2305111","article-title":"The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges","volume":"22","author":"Farina","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Merletti, R., and Farina, D. (2016). Surface Electromyography: Physiology, Engineering, and Applications, Wiley-IEEE Press.","DOI":"10.1002\/9781119082934"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Arpaia, P., De Luca, M., Di Marino, L., Duran, D., Gargiulo, L., Lanteri, P., Moccaldi, N., Nalin, M., Picciafuoco, M., and Robbio, R. (2025). A Systematic Review of Techniques for Artifact Detection and Artifact Category Identification in Electroencephalography from Wearable Devices. Sensors, 25.","DOI":"10.3390\/s25185770"},{"key":"ref_16","first-page":"3","article-title":"Beyond the lab: Real-world benchmarking of wearable EEGs for passive BCIs","volume":"13","author":"Ronca","year":"2025","journal-title":"Brain Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8408","DOI":"10.1038\/s41598-026-39056-8","article-title":"A comprehensive evaluation framework for consumer-grade EEG devices: Signal quality, robustness, and usability","volume":"16","author":"Lee","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, X., and Li, M. (2025). Ultrasound and Unsupervised Segmentation-Based Gesture Recognition for Smart Device Unlocking. Sensors, 25.","DOI":"10.3390\/s25206408"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s12984-025-01784-9","article-title":"Sonomyography accurately captures joint kinematics during volitional and electrically stimulated motion in healthy adults and an individual with cerebral palsy","volume":"23","author":"Patwardhan","year":"2025","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rehman, M.U., Shah, K., Haq, I.U., Iqbal, S., and Ismail, M.A. (2023). A Wearable Force Myography-Based Armband for Recognition of Upper Limb Gestures. Sensors, 23.","DOI":"10.3390\/s23239357"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1007\/s11517-024-03019-w","article-title":"A survey on the state of the art of force myography (FMG): Analysis and assessment","volume":"62","author":"Sherif","year":"2024","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Scano, A., Zanoletti, M., Pirovano, I., Spinelli, L., Contini, D., Torricelli, A., and Re, R. (2019). NIRS-EMG for Clinical Applications: A Systematic Review. Appl. Sci., 9.","DOI":"10.3390\/app9152952"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"V\u00e1sconez, J.P., Barona L\u00f3pez, L.I., Valdivieso Caraguay, \u00c1.L., and Benalc\u00e1zar, M.E. (2022). Hand Gesture Recognition Using EMG-IMU Signals and Deep Q-Networks. Sensors, 22.","DOI":"10.3390\/s22249613"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fratti, R., Marini, N., Atzori, M., M\u00fcller, H., Tiengo, C., and Bassetto, F. (2024). A Multi-Scale CNN for Transfer Learning in sEMG-Based Hand Gesture Recognition for Prosthetic Devices. Sensors, 24.","DOI":"10.3390\/s24227147"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105600","DOI":"10.1016\/j.bspc.2023.105600","article-title":"EMG-based Hand Gesture Classifier Robust to Daily Variation: Recursive Domain Adversarial Neural Network with Data Synthesis","volume":"89","author":"Lee","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"44850","DOI":"10.1038\/s41598-025-28302-0","article-title":"Calibration-free sEMG Intention Recognition via Self-supervised Pretraining and Adversarial Domain Alignment for Upper-limb Rehabilitation","volume":"15","author":"Yang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e18","DOI":"10.1017\/wtc.2025.7","article-title":"Functional evaluation of a real-time EMG controlled prosthetic hand","volume":"6","author":"Kalita","year":"2025","journal-title":"Wearable Technol."},{"key":"ref_28","first-page":"291","article-title":"Low-Price Prosthetic Hand Controlled by EMG Signals","volume":"184","author":"Unanyan","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"George, J.A., Davis, T.S., Brinton, M.R., and Clark, G.A. (2019). Intuitive Neuromyoelectric Control of a Dexterous Bionic Arm Using a Modified Kalman Filter. arXiv.","DOI":"10.1016\/j.jneumeth.2019.108462"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s12984-021-00815-5","article-title":"Systematic review on wearable lower-limb exoskeletons for gait training in neuromuscular impairments","volume":"18","year":"2021","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Fares, H., Ghayvat, H., Brunner, I.C., Puthusserypady, S., Razavi, B., Lansberg, M., Poon, A., and Meador, K.J. (2023). A systematic review on functional electrical stimulation systems for upper limb stroke rehabilitation. Front. Neurol., 14.","DOI":"10.3389\/fneur.2023.1272992"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mashud, G., Hasan, S.K., and Alam, N. (2025). Advances in Control Techniques for Rehabilitation Exoskeleton Robots: A Systematic Review. Actuators, 14.","DOI":"10.3390\/act14030108"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1111\/aor.14925","article-title":"Consumer views of functional electrical stimulation and robotic exoskeleton technologies","volume":"49","author":"Pei","year":"2025","journal-title":"Artif. 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