EXPLOITING EMG SIGNALS FOR THE RECOGNITION OF FINGER FLEXIONS USING WAVELET TRANSFORM AND MACHINE LEARNING
Keywords:
electromyographic signals, wavelet transform, feature extraction, machine learningAbstract
Electromyography (EMG) is a technique that measures and records electrical activity in response to a nerve’s stimulation of the muscle. EMG signals are biomedical signals that represent electrical currents generated in muscles during their contraction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing and classification. Various mathematical techniques have received extensive attention and one of the most popular is Wavelet transform. Wavelet transform is a mathematical tool for analyzing data where the signal values vary at different scales, such as in EMG signals, so it is widely used in EMG signal processing systems. This study explored the potential of applying wavelet transform to EMG signals, which were collected using two sensors placed on the forearms of eight subjects performing individual finger flexions. We experimented with various mother wavelets and decomposition levels to determine the most effective combination. After evaluating the results obtained from training models, we selected the Daubechies wavelet (db1) with a second level of decomposition as the optimal solution. To generate meaningful features from the wavelet coefficients, we extracted time-frequency domain features, which were then used as inputs for training and testing machine learning models. We employed five classification algorithms: K-nearest neighbors, Support Vector Machine, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost). By evaluating and comparing the performance of these algorithms, we demonstrated enhanced accuracy and robustness achieved by the combination of wavelet transform and feature extraction in EMG signal analysis.
References
LeBlanc M, 2008. Taken from: https://web.stanford.edu/class/engr110/2011/LeBlanc-03a.pdf.
Premier Surgical Prosthetic Center, The Evolution of Prosthetic Limbs: Current Technological Advancements, 2023.
Avelable from: https://www.premierprosthetic.com/09/the-evolution-of-prosthetic-limbs-current-technological-advancements/
Hristov B, Nadzinski G, Ojleska Latkoska V, Zlatinov S. Classification of Individual and Combined Finger Flexions Using Machine Learning Approaches. IEEE ICCA 2022; 986-991. doi:10.1109/ICCA54724.2022.9831952.
Reza Bagherian Azhiri, Mohammad Esmaeili, Mehrdad Nourani. EMG- Based Feature Extraction and Classification for Prosthetic Hand Control, 2021; doi:10.48550/arXiv.2107.00733.
Phinyomark A, Limsakul C, Phukpattaranont P. Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification. Measurement Science Review 2011; 11(2): 45-52. doi: 10.2478/v10048-011-0009-y.
Raez MB, Hussain MS, Mohd-Yasin F. Techniques of EMG signal analysis: detection, processing, classification and applications. Biol Proced Online 2006; 8: 11-35. doi: 10.1251/bpo115.
Khushaba RN, Kodagoda S, Takruri M, Dissanayake G. Toward Improved Control of Prosthetic Fingers Using Surface Electromyogram (EMG) Signals. Expert Systems with Applications 2012; 39(12): 10731-10738, 2012; doi:10.1016/j.eswa.2012.02.192.
Rakesh R, Bikash CS, Ashish KB. Ocular artifact elimination from electroencephalography signals: A systematic review. Journal of Applied Biomedicine 2021; 41(3): 960-996. doi: 10.1016/j.bbe.2021.06.007.
Azmoudeh B, Cvetkovic D. Wavelets in Biomedical Signal Processing and Analysis. In book: Reference Module in Biomedical Sciences, 2019; doi: 10.1016/B978-0-12-801238-3.99972-0.
Amer FAHAl, Tasnim HKA. An overview of machine learning classification techniques. BIO Web of Conferences 2024; 97(4): 00133. doi: 10.1051/bioconf/20249700133.