LAUSR

Electromyography (EMG) signals are widely used for predicting human movement intention in the operation of robotic assistive devices that improve the quality of people’s lives with motor problems. One of the current challenges controlling such devices is achieving a natural interaction between the device and the user. However, the most common algorithms applied in motion detection exhibit a slow time response. In this work, we propose the use of robust differentiator algorithms to extract features from EMG signals that allow a fast detection of movement intention. Experimental results show that by using robust differentiator algorithms, we can significantly reduce the latency between the detection movement intention and the real movement, without losing accuracy.