1. Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks
- Author
-
Panagiotis Tsinganos, Bart Jansen, Jan Cornelis, and Athanassios Skodras
- Subjects
sEMG ,hand gesture recognition ,deep learning ,CNN ,TCN ,real time ,Chemical technology ,TP1-1185 - Abstract
In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. Recently, a specific class of these models called Temporal Convolutional Networks (TCNs) has been successfully applied to this task. (2) Methods: In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using TCNs. Specifically, we investigate the real-time behavior of our previous TCN model by performing a simulation experiment on a recorded sEMG dataset. (3) Results: The proposed network trained with data augmentation yields a small improvement in accuracy compared to our existing model. However, the classification accuracy is decreased in the real-time evaluation, showing that the proposed TCN architecture is not suitable for such applications. (4) Conclusions: The real-time analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.
- Published
- 2022
- Full Text
- View/download PDF