Mora Rubio, A.; Alzate Grisales, J.A.; Tabares-Soto, R.; Orozco-Arias, S.; Jiménez Varón, C.F.; Padilla Buriticá, J.I. Identification of Hand Movements from Electromyographic Signals Using Machine Learning. Preprints2020, 2020020443. https://doi.org/10.20944/preprints202002.0443.v1
APA Style
Mora Rubio, A., Alzate Grisales, J.A., Tabares-Soto, R., Orozco-Arias, S., Jiménez Varón, C.F., & Padilla Buriticá, J.I. (2020). Identification of Hand Movements from Electromyographic Signals Using Machine Learning. Preprints. https://doi.org/10.20944/preprints202002.0443.v1
Chicago/Turabian Style
Mora Rubio, A., Cristian Felipe Jiménez Varón and Jorge Iván Padilla Buriticá. 2020 "Identification of Hand Movements from Electromyographic Signals Using Machine Learning" Preprints. https://doi.org/10.20944/preprints202002.0443.v1
Abstract
Electromyographic (EMG) signals provide information about a person's muscle activity. For hand movements, in particular, the execution of each gesture involves the activation of different combinations of the forearm muscles, which generate distinct electrical patterns. Conversely, the analysis of these muscle activation patterns, represented by EMG signals, allows recognizing which gesture is being performed. In this study, we aimed to implement an automatic identification system of hand or wrist gestures based on supervised Machine Learning (ML) techniques. We trained different computational models and determined which of these showed the best capacity to identify six hand or wrist gestures and generalize between different subjects. We used an open access database containing recordings of EMG signals from 36 subjects. Among the results obtained, we highlight the performance of the Random Forest model, with an accuracy of 95.39%, and the performance of a convolutional neural network with an accuracy of 94.77%.
Keywords
EMG; Machine Learning; Deep Learning; Computational models; Hand and wrist gestures
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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