Preprint Article Version 1 This version is not peer-reviewed

Identification of Hand Movements from Electromyographic Signals Using Machine Learning

Version 1 : Received: 27 February 2020 / Approved: 28 February 2020 / Online: 28 February 2020 (16:06:07 CET)

How to cite: 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. Preprints 2020, 2020020443 (doi: 10.20944/preprints202002.0443.v1). 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. Preprints 2020, 2020020443 (doi: 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%.

Subject Areas

EMG; Machine Learning; Deep Learning; Computational models; Hand and wrist gestures

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.