{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T00:36:51Z","timestamp":1769215011259,"version":"3.49.0"},"reference-count":74,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,1]],"date-time":"2018-05-01T00:00:00Z","timestamp":1525132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior.<\/jats:p>","DOI":"10.3390\/s18051388","type":"journal-article","created":{"date-parts":[[2018,5,3]],"date-time":"2018-05-03T03:20:27Z","timestamp":1525317627000},"page":"1388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0477-2465","authenticated-orcid":false,"given":"Karina de O. A.","family":"De Moura","sequence":"first","affiliation":[{"name":"Electrical Engineering, Instrumentation Laboratory, Federal University of Rio Grande do Sul (UFRGS), Avenue Osvaldo Aranha 103, Porto Alegre, RS 90035-190, Brazil"}]},{"given":"Alexandre","family":"Balbinot","sequence":"additional","affiliation":[{"name":"Electrical Engineering, Instrumentation Laboratory, Federal University of Rio Grande do Sul (UFRGS), Avenue Osvaldo Aranha 103, Porto Alegre, RS 90035-190, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s12984-016-0130-0","article-title":"Flexible and static wrist units in upper limb prosthesis users: Functionality scores, user satisfaction and compensatory movements","volume":"13","author":"Deijs","year":"2016","journal-title":"J. Neuroeng. Rehabilt."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s12984-015-0044-2","article-title":"Surveying the interest of individuals with upper limb loss in novel prosthetic control techniques","volume":"12","author":"Engdahl","year":"2015","journal-title":"J. Neuroeng. Rehabilt."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhao, X., Han, J., and Zhao, Y. (June, January 31). A comparative study on PCA and LDA based EMG pattern recognition for anthropomorphic robotic hand. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907569"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.jelekin.2015.06.010","article-title":"Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment","volume":"29","author":"Blana","year":"2016","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_5","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. Rehabilt. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.medengphy.2015.02.005","article-title":"Adaptive myoelectric pattern recognition toward improved multifunctional prosthesis control","volume":"37","author":"Liu","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s12984-015-0011-y","article-title":"A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition","volume":"12","author":"Zhang","year":"2015","journal-title":"J. Neuroeng. Rehabilt."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1186\/1743-0003-11-110","article-title":"A state-based, proportional myoelectric control method: Online validation and comparison with the clinical state-of-the-art","volume":"11","author":"Jiang","year":"2014","journal-title":"J. Neuroeng. Rehabilt."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"162","DOI":"10.3389\/fnsys.2015.00162","article-title":"Control Capabilities of Myoelectric Robotic Prostheses by Hand Amputees: A Scientific Research and Market Overview","volume":"9","author":"Atzori","year":"2015","journal-title":"Front. Syst. Neurosci."},{"key":"ref_10","first-page":"226","article-title":"Detection of and Compensation for EMG Disturbances for Powered Lower Limb Prosthesis Control","volume":"4320","author":"Spanias","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabilt. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1109\/TIM.2014.2317296","article-title":"Automated Biosignal Quality Analysis for Electromyography Using a One-Class Support Vector Machine","volume":"63","author":"Fraser","year":"2014","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1109\/LSP.2014.2313880","article-title":"Time-Varying Multicomponent Signal Modeling for Analysis of Surface EMG Data","volume":"21","author":"Zivanovic","year":"2014","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3887","DOI":"10.1109\/JSEN.2016.2536363","article-title":"Power Line Interference Removal for High-Quality Continuous Biosignal Monitoring with Low-Power Wearable Devices","volume":"16","author":"Tomasini","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mastinu, E., Ahlberg, J., Lendaro, E., Hermansson, L., Hakansson, B., and Ortiz-Catalan, M. (2018). An alternative myoelectric pattern recognition approach for the control of hand prostheses: A case study of use in daily life by a dysmelia subject. IEEE J. Transl. Eng. Health Med., 6.","DOI":"10.1109\/JTEHM.2018.2811458"},{"key":"ref_15","unstructured":"Zhang, X., Huang, H., and Yang, Q. (2013, January 3\u20137). Real-time implementation of a self-recovery EMG pattern recognition interface for artificial arms. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan."},{"key":"ref_16","first-page":"21","article-title":"Self-adaptive method for sEMG movement classification based on continuous optimal electrode assortment","volume":"4","author":"Favieiro","year":"2016","journal-title":"Braz. J. Instrum. Control"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Soedirdjo, S.D.H., Ullah, K., and Merletti, R. (2015, January 25\u201329). Power line interference attenuation in multi-channel sEMG signals: Algorithms and analysis. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319227"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fraser, G.D., Chan, A.D.C., Green, J.R., Abser, N., and MacIsaac, D. (September, January 30). CleanEMG\u2014Power line interference estimation in sEMG using an adaptive least squares algorithm. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091958"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Su, S., Xu, P., and Yao, D. (2017, January 11\u201315). Performance evaluation of Noise-Assisted Multivariate Empirical Mode Decomposition and its application to multichannel EMG signals. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, Korea.","DOI":"10.1109\/EMBC.2017.8037600"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nazmi, N., Abdul Rahman, M., Yamamoto, S., Ahmad, S., Zamzuri, H., and Mazlan, S. (2016). A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions. Sensors, 16.","DOI":"10.3390\/s16081304"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s12938-016-0196-8","article-title":"FastICA peel-off for ECG interference removal from surface EMG","volume":"15","author":"Chen","year":"2016","journal-title":"Biomed. Eng. Online"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.agwat.2014.09.013","article-title":"Water content virtual sensor for tomatoes in coconut coir substrate for irrigation control design","volume":"151","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1016\/j.conengprac.2010.05.006","article-title":"Virtual sensors design for active fault tolerant control system applied to a winding machine","volume":"18","author":"Ponsart","year":"2010","journal-title":"Control Eng. Pract."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.aei.2011.07.004","article-title":"Virtual sensors for estimation of energy consumption and thermal comfort in buildings with underfloor heating","volume":"25","author":"Ploennigs","year":"2011","journal-title":"Adv. Eng. Inform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.automatica.2012.09.009","article-title":"A combined Moving Horizon and Direct Virtual Sensor approach for constrained nonlinear estimation","volume":"49","author":"Fagiano","year":"2013","journal-title":"Automatica"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1299\/jbse.5.472","article-title":"Physical-Sensor and Virtual-Sensor Based Method for Estimation of Lower Limb Gait Posture Using Accelerometers and Gyroscopes","volume":"5","author":"Liu","year":"2010","journal-title":"J. Biomech. Sci. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1016\/j.engappai.2013.05.004","article-title":"Viscosity virtual sensor to control combustion in fossil fuel power plants","volume":"26","author":"Delgadillo","year":"2013","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.arcontrol.2013.04.004","article-title":"Fault-tolerant control of systems with convex polytopic linear parameter varying model uncertainty using virtual-sensor-based controller reconfiguration","volume":"37","author":"Nazari","year":"2013","journal-title":"Annu. Rev. Control"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1049\/iet-cta.2010.0089","article-title":"Invariant-set-based fault tolerant control using virtual sensors","volume":"5","author":"Nazari","year":"2011","journal-title":"IET Control Theory Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1109\/TVT.2012.2230200","article-title":"Virtual-Sensor-Based Maximum-Likelihood Voting Approach for Fault-Tolerant Control of Electric Vehicle Powertrains","volume":"62","author":"Tabbache","year":"2013","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ho, L.M., Satzger, C., and de Castro, R. (2017, January 26\u201329). Fault-tolerant control of an electrohydraulic brake using virtual pressure sensor. Proceedings of the 2017 International Conference on Robotics and Automation Sciences (ICRAS), Hong Kong, China.","DOI":"10.1109\/ICRAS.2017.8071920"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/JSEN.2011.2121059","article-title":"From Modeling to Implementation of Virtual Sensors in Body Sensor Networks","volume":"12","author":"Raveendranathan","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2456","DOI":"10.1109\/TIM.2017.2707838","article-title":"Virtual Respiratory Rate Sensors: An Example of A Smartphone-Based Integrated and Multiparametric mHealth Gateway","volume":"66","author":"Crema","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, Y., Pandis, I., and Guo, Y. (2016). Enabling Virtual Sensing as a Service. Informatics, 3.","DOI":"10.3390\/informatics3020003"},{"key":"ref_36","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. Rehabilt. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1109\/TBME.2010.2088396","article-title":"Spectral estimation of nonstationary EEG using particle filtering with application to event-related desynchronization (ERD)","volume":"58","author":"Ting","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4975","DOI":"10.3390\/s150304975","article-title":"A Robust Kalman Framework with Resampling and Optimal Smoothing","volume":"15","author":"Kautz","year":"2015","journal-title":"Sensors"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ymssp.2017.08.032","article-title":"Virtual microphone sensing through vibro-acoustic modelling and Kalman filtering","volume":"104","author":"Naets","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1080\/17415977.2012.667090","article-title":"Control of the ledge thickness in high-temperature metallurgical reactors using a virtual sensor","volume":"20","author":"LeBreux","year":"2012","journal-title":"Inverse Probl. Sci. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.1121\/1.3531941","article-title":"Virtual sensors for active noise control in acoustic-structural coupled enclosures using structural sensing: Robust virtual sensor design","volume":"129","author":"Halim","year":"2011","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.renene.2017.12.102","article-title":"Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines","volume":"120","author":"Cho","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ortiz-Catalan, M., Rouhani, F., Branemark, R., and Hakansson, B. (2015, January 25\u201329). Offline accuracy: A potentially misleading metric in myoelectric pattern recognition for prosthetic control. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7318567"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s12984-018-0363-1","article-title":"Online mapping of EMG signals into kinematics by autoencoding","volume":"15","author":"Vujaklija","year":"2018","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3389\/fnbot.2017.00007","article-title":"Translating Research on Myoelectric Control into Clinics\u2014Are the Performance Assessment Methods Adequate?","volume":"11","author":"Vujaklija","year":"2017","journal-title":"Front. Neurorobot."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Moura, K.O.A., Favieiro, G.W., and Balbinot, A. (2016, January 16\u201320). Support vectors machine classification of surface electromyography for non-invasive naturally controlled hand prostheses. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590819"},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1109\/TNSRE.2014.2303394","article-title":"Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification","volume":"22","author":"Gijsberts","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabilt. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"140053","DOI":"10.1038\/sdata.2014.53","article-title":"Electromyography data for non-invasive naturally-controlled robotic hand prostheses","volume":"1","author":"Atzori","year":"2014","journal-title":"Sci. Data"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TNSRE.2014.2328495","article-title":"Characterization of a Benchmark Database for Myoelectric Movement Classification","volume":"23","author":"Atzori","year":"2015","journal-title":"IEEE Trans. Neural Syst. Rehabilt. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.bspc.2014.07.007","article-title":"Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees","volume":"14","author":"Riillo","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Farrell, T.R. (2011). Determining delay created by multifunctional prosthesis controllers. J. Rehabilt. Res. Dev., 48.","DOI":"10.1682\/JRRD.2011.03.0055"},{"key":"ref_53","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_54","doi-asserted-by":"crossref","unstructured":"Winkler, G., and Balbinot, A. (2012). Proposal of a Neuro Fuzzy System for Myoelectric Signal Analysis from Hand-Arm Segment. Computational Intelligence in Electromyography Analysis\u2014A Perspective on Current Applications and Future Challenges, IntechOpen.","DOI":"10.5772\/48793"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Balbinot, A., J\u00fanior, A., and Favieiro, G.W. (2013). Decoding Arm Movements by Myoelectric Signal and Artificial Neural Networks. Intell. Control Autom., 87\u201393.","DOI":"10.4236\/ica.2013.41012"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"11100","DOI":"10.3390\/s101211100","article-title":"Man-machine interface system for neuromuscular training and evaluation based on EMG and MMG signals","volume":"10","author":"Alonso","year":"2010","journal-title":"Sensors"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Cene, V.H., Favieiro, G., and Balbinot, A. (2015, January 25\u201329). Upper-limb movement classification based on sEMG signal validation with continuous channel selection. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7318405"},{"key":"ref_58","first-page":"14","article-title":"Optimization of Features to Classify Upper\u2014Limb Movements Through sEMG Signal Processing","volume":"4","author":"Cene","year":"2016","journal-title":"Braz. J. Instrum. Control"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/S1050-6411(02)00071-8","article-title":"Crosstalk in surface electromyography of the proximal forearm during gripping tasks","volume":"13","author":"Mogk","year":"2003","journal-title":"J. Electromyogr. Kinesiol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1152\/japplphysiol.01070.2003","article-title":"The extraction of neural strategies from the surface EMG","volume":"96","author":"Farina","year":"2004","journal-title":"J. Appl. Physiol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.measurement.2016.04.074","article-title":"Correlation of sensory analysis with a virtual sensor array data for odour diagnosis of fragrant fabrics","volume":"90","author":"Shakoorjavan","year":"2016","journal-title":"Measurement"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/TBME.2003.820381","article-title":"Robust algorithm for estimation of time-varying transfer functions","volume":"51","author":"Zou","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4366","DOI":"10.1109\/TSP.2007.896265","article-title":"Time-frequency ARMA models and parameter estimators for underspread nonstationary random processes","volume":"55","author":"Jachan","year":"2007","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1016\/j.eswa.2007.08.088","article-title":"Particle swarm optimization for parameter determination and feature selection of support vector machines","volume":"35","author":"Lin","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Tosin, M., Majolo, M., Chedid, R., Cene, V.H., and Balbinot, A. (2017, January 11\u201315). SEMG feature selection and classification using SVM-RFE. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, Korea.","DOI":"10.1109\/EMBC.2017.8036844"},{"key":"ref_67","unstructured":"Montgomery, D.C. (2001). Design and Analysis of Experiments, John Wiley & Sons, Inc.. [5th ed.]."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1186\/s12984-017-0290-6","article-title":"NLR, MLP, SVM, and LDA: A comparative analysis on EMG data from people with trans-radial amputation","volume":"14","author":"Gruppioni","year":"2017","journal-title":"J. Neuroeng. Rehabilt."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wei, Y., Geng, Y., Yu, W., Samuel, O.W., Jiang, N., Zhou, H., Guo, X., Lu, X., and Li, G. (2017, January 14\u201318). Real-time Classification of Forearm Movements Based on High Density Surface Electromyography. Proceedings of the 2017 IEEE International Conference on Real-Time Computing and Robotics (RCAR), Okinawa, Japan.","DOI":"10.1109\/RCAR.2017.8311868"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Benatti, S., Milosevic, B., Farella, E., Gruppioni, E., and Benini, L. (2017). A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies. Sensors, 17.","DOI":"10.3390\/s17040869"},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1186\/s12984-017-0283-5","article-title":"Classification complexity in myoelectric pattern recognition","volume":"14","author":"Nilsson","year":"2017","journal-title":"J. Neuroeng. Rehabilt."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Favieiro, G.W., Moura, K.O.A., and Balbinot, A. (2016, January 16\u201320). Novel method to characterize upper-limb movements based on paraconsistent logic and myoelectric signals. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590723"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4437","DOI":"10.1038\/s41598-017-04255-x","article-title":"User Adaptation in Myoelectric Man-Machine Interfaces","volume":"7","author":"Hahne","year":"2017","journal-title":"Sci. Rep."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1388\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:02:49Z","timestamp":1760194969000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1388"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,1]]},"references-count":74,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["s18051388"],"URL":"https:\/\/doi.org\/10.3390\/s18051388","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,1]]}}}