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%.
- Bio-signal analysis
- Blind source separation (BSS)
- Hand gesture sensing
- Independent component analysis (ICA)
- Multi run ICA
- Surface electromyography (sEMG)