Weighted-Cumulated S-EMG Muscle Fatigue Estimator.

This paper addresses a new approach to objectively evaluate muscle fatigue in isometric and dynamic physical exertions using surface electromyography (S-EMG). The emphasis of this proposal is to preserve the spectral signature of the muscle fatigue phenomenon while reducing the spatial effects of electrode localization, and decreasing the disparity of results obtained by the same experimental protocol at different times. A cumulated and normalized modeling was sought to make evident the nonstationary characteristics of muscle fatigue that is gradually identified with its inertia and intensity.

Compression of EMG signals with wavelet transform and artificial neural networks.

This paper presents a hybrid adaptive algorithm for the compression of surface electromyographic (S-EMG) signals recorded during isometric and/or isotonic contractions. This technique is useful for minimizing data storage and transmission requirements for applications where multiple channels with high bandwidth data are digitized, such as telemedicine applications. The compression algorithm proposed in this work uses a discrete wavelet transform for spectral decomposition and an intelligent dynamic bit allocation scheme implemented by an approach using the Kohonen layer, which improves the bit allocation for sections of the S-EMG with different characteristics.