About this document
Real-Time Hand Gesture Recognition Model by Zineb Bendahgane is a document available to read on EtoBox.
The paper presents a real-time hand gesture recognition model that utilizes electromyographic (EMG) signals from the Myo Armband and employs deep learning techniques, specifically an autoencoder for feature extraction and a feed-forward neural network for classification. The model achieves an average recognition accuracy of 85.08% ± 15.21% with a response time of 3 ± 1 ms, and it is designed to work universally without requiring user-specific training. The authors provide publicly available code for the imp
- Author
- Zineb Bendahgane
- Language
- EN