Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gestures as Input: Neuroelectric Joysticks and Keyboards
IEEE Pervasive Computing
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
DigitEyes: Vision-Based Human Hand Tracking
DigitEyes: Vision-Based Human Hand Tracking
Hand gestures for HCI using ICA of EMG
VisHCI '06 Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56
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Surface electromyogram (sEMG) based control of prosthesis and computer assisted devices can provide the user with near natural control. Unfortunately there is no suitable technique to classify sEMG when the there are multiple active muscles such as during finger and wrist flexion due to cross-talk. Independent Component Analysis (ICA) to decompose the signal into individual muscle activity has been demonstrated to be useful. However, ICA is an iterative technique that has inherent randomness during initialization. The average improvement in classification of sEMG that was separated using ICA was very small, from 60% to 65%. To overcome this problem associated with randomness of initialization, multi-run ICA (MICA) based sEMG classification system has been proposed and tested. MICA overcame the shortcoming and the results indicate that using MICA, the accuracy of identifying the finger and wrist actions using sEMG was 99%.