Electromyography-Based Hand Pose Estimation U sing Machine Learning

• Published in: 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE) pp. 1 - 5
• Main Authors: Wichai, Teerasak, Chukamlang, Ratthathammanoon, Massagram, Wansuree
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19-06-2024
• Subjects: Accuracy; Convolutional Neural Networks(CNNs); Electromyography; Eletromyography(EMG); Hand pose esti-mation; Human computer interaction; Long short-term memory(LSTM); Machine learning; Neural networks; Pose estimation; Solid modeling; Temporal Convolutional Neural Networks(TCNNs)
• ISSN: 2642-6579
• DOI: 10.1109/JCSSE61278.2024.10613637

This paper presents an approach to hand pose estimation using Electromyography (EMG) signals with machine learning techniques. Long Short-Term Memory (LSTM) was shown to be the most accurate neural network architecture for identifying hand gestures from EMG signals. The EMG data were gathered from 4 classes of 15 participants, each of whom made a gesture five times for every class. T he model's output indicates an accuracy of roughly 93 %, which makes it feasible for hand posture estimation. Such technology plays a pivotal role in various applications, including human-computer interaction, re-habilitation, prosthetics, virtual reality, and gaming. Leveraging EM G signals offers a direct and intuitive means of inferring hand gestures and movements and better protects the user's identity than other motion capture techniques such as video images. This research has the potential to contribute to the growing field of human-machine interaction and assistive technology by offering a reliable and efficient means of estimating hand poses.