Machine learning-based novel approach to classify the shoulder motion of upper limb amputees

Kim, 1996, Statistical analysis of amputations and trends in Korea, Prosthet Orthot Int, 20, 88, 10.3109/03093649609164424 Geng, 2012, Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees, J Neuroeng Rehabil, 9, 74, 10.1186/1743-0003-9-74 DaSalla, 2015, Robot Control Using Electromyography (EMG) Signals of the Wrist, Appl Bionics Biomech, 2, 97, 10.1155/2005/952754 You, 2011, Finger motion decoding using EMG signals corresponding various arm postures, Exp Neurobiol, 19, 54, 10.5607/en.2010.19.1.54 Shi, 2018, A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study, Biocybern Biomed Eng, 38, 126, 10.1016/j.bbe.2017.11.001 Leija-Salas, 2018, sEMG Signal Acquisition Strategy towards Hand FES Control, J Healthc Eng, 2018, 1 Guglielmelli, 2016, Control of Prosthetic Hands via the Peripheral Nervous System, Funct Neurol, 10, 1 Englehart, 1999, Classification of the Myoeletric Signal using Time-Frequency Based Representations, Med Eng Phys, 21, 431, 10.1016/S1350-4533(99)00066-1 Rojas-Martínez, 2012, High-density surface EMG maps from upper-arm and forearm muscles, J Neuroeng Rehabil, 9, 1, 10.1186/1743-0003-9-85 Phinyomark, 2013, EMG feature evaluation for improving myoelectric pattern recognition robustness, Expert Syst Appl, 40, 4832, 10.1016/j.eswa.2013.02.023 Guo, 2015, Comparison of sEMG-based feature extraction and motion classification methods for upper-limb movement, Sensors (Switzerland), 15, 9022, 10.3390/s150409022 Karabulut, 2017, Comparative evaluation of EMG signal features for myoelectric controlled human arm prosthetics, Biocybern Biomed Eng, 37, 326, 10.1016/j.bbe.2017.03.001 Hussain, 2009, Electromyography Signal Analysis Using Wavelet Transform and Higher Order Statistics to Determine Muscle Contraction, Expert Systems, 26, 35, 10.1111/j.1468-0394.2008.00483.x Hariharan, 2012, A comparative study of wavelet families for classification of wrist motions, Comp Electr Eng Arch, 38, 1798, 10.1016/j.compeleceng.2012.08.009 Narayan, 2017, sEMG signal classification using Discrete Wavelet Transform and Decision Tree classifier, Int J Control Theory Appl, 10, 511 Ngui, 2013, Wavelet Analysis: Mother Wavelet Selection Methods, Appl Mech Mater, 393, 953, 10.4028/www.scientific.net/AMM.393.953 Uyar, 2008, An effective wavelet-based feature extraction method for classification of power quality disturbance signals, Electr Pow Syst Res, 78, 1747, 10.1016/j.epsr.2008.03.002 Zhang, 2010, Wavelet Transform Theory and its Application in EMG Signal Processing, 2234 Myoelectric, 2009, Principal Components Analysis Preprocessing for Improved Classification Accuracies in pattern-recognition-based myoelectric control, EEE Trans Biomed Eng, 56, 1407, 10.1109/TBME.2008.2008171 Veer, 2015, A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier, Measurement, 60, 283, 10.1016/j.measurement.2014.10.023 Reaz, 2006, Techniques of EMG Signal Analysis: Detection, Processing, Classification and Applications, Biol Proced Online, 8, 11, 10.1251/bpo115 Lolure, 2015, 1273 Subasi, 2012, Classification of EMG signals using combined features and soft computing techniques, Appl Soft Comput J, 12, 2188, 10.1016/j.asoc.2012.03.035 Andrade, 2011, Classification of EMG signals using artificial neural networks for virtual hand prosthesis control, 7354 Blanaa, 2016, Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment, J Electromyogr Kinesiol., 21, 10.1016/j.jelekin.2015.06.010 Elektrik, 2018, Application of Spectrogram and Discrete Wavelet Transform for Emg Pattern, Journal of Theoretical and Applied Information Technology, 96, 3036 Wu, 2008, Top 10 algorithms in data mining, Knowledge and Information Systems, 14, 10.1007/s10115-007-0114-2 Gokgoz, 2015, Comparison of decision tree algorithms for EMG signal classification using DWT, Biomedical Signal Processing and Control, 18, 138, 10.1016/j.bspc.2014.12.005 Abdullah, 2017, 475 Gaudet, 2018, Classification of Upper limb phantom movements in transhumeral amputees using electromyographic and kinematic features, Eng Appl Artif Intel, 68, 153, 10.1016/j.engappai.2017.10.017 Gini, 2018, Analysis and comparison of features and algorithms to classify shoulder movements from sEMG signals, IEEE Sens J, 1 Li, 2017, A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees, J Neuroeng Rehabil, 14, 2, 10.1186/s12984-016-0212-z Cook, 2019, Braz. J. Phys. Ther, 22 Minning, 2007, EMG analysis of shoulder muscle fatigue during resisted isometric shoulder elevation, J Electromyog. Kinesiol, 17, 153, 10.1016/j.jelekin.2006.01.008 Jochumsen, 2018, The effect of arm position on classification of hand gestures with intramuscular EMG, Biomed Signal Process Control, 43, 1, 10.1016/j.bspc.2018.02.013 Kaur, 2017, Adaptive threshold method for peak detection of surface electromyography signal from around shoulder muscles, J Appl Stat, 44, 1 Phinyomark, 2011, Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification, Meas Sci Rev, 11, 45, 10.2478/v10048-011-0009-y Kaur, 2016, Comparison of muscles activity of abled bodied and amputee subjects for around shoulder movement, Bio-Medical Materials and Engineering, 27, 29, 10.3233/BME-161565 Stegeman, 2007 Williams, 2014, Surface Electromyography with in Equine Performance Analysis Hosseini, 2004, Wavelet analysis of surface electromyography signals, 3832 Moshou, 2000, Wavelets and Self-organising Maps in Electromyogram (EMG) Analysis, 186 Zhang, 2006, Wavelet De-noising of Electromyography, 1553 Phinyomark, 2008, EMG Feature Extraction for Tolerance of White Gaussian Noise, 178 Megahed, 2008, Selection of a suitable mother wavelet for analyzing power system fault transients, 1 Rekhi, 2009, Multi-Class SVM Classification of Surface EMG Signal for Upper Limb Function, 1 Meena, 2016, Classification of EMG Signals using SVM-kNN, Ijarece, 5, 1718 Camelia, 2010 Kumar, 2009, A radial basis function neural network classifier for the discrimination of individual odor using responses of thick-film tin-oxide sensors, IEEE Sens J, 9, 1254, 10.1109/JSEN.2009.2030072 Nayak, 1993, Transformed cluster analysis: An approach to the identification of gases/odors using integrated gas sensor array, Sens Actuators, 12, 103, 10.1016/0925-4005(93)80005-V AlOmari, 2014, Analysis of Extracted Forearm sEMG Signal Using LDA, QDA, K-NN Classification Algorithms, Open Autom Contr Syst J, 6, 108, 10.2174/1874444301406010108 Côté-Allard, 2018, 1 Dwi, 2017, Classification of EMG Signals from forearm Muscles As Automatic Control Using Naive Bayes, Int. Semin. Intell. Technol. Its Appl. Classif., 346 Foster, 2014, Machine learning, medical diagnosis, and biomedical engineering research - commentary, Biomed Eng Online, 13, 94, 10.1186/1475-925X-13-94 Keller, F. (n.d.). Naive Bayes Classifiers, 1–22. Moin, A., Zhou, A., Benatti, S., Rahimi, A., Alexandrov, G., Menon, A., Rabaey, J. M. (n.d.). Adaptive EMG-based hand gesture recognition using hyperdimensional computing. Moise, I., Pournaras, E., Helbing, D., Moise, I., Pournaras, E., & Helbing, D. (n.d.). K-Nearest Neighbour Classifier Supervised data mining Classification Decision Trees. Sutton, 2012, 1 Universit, 2012, 13, 1063 Phinyomark, 2012, The Usefulness of Wavelet Transform to Reduce Noise in the SEMG Signal, 107