Training a multivariable myoelectric mapping function to estimate fatigue

The mapping index (MI) is a fatigue assessment index that uses multiple time-domain myoelectric features to train an artificial neural network (ANN) to track the progression of fatigue. This work showed that mapping functions trained using data from independent subjects and contraction conditions to yield a generalized mapping index (GMI) can assess fatigue as well as functions trained with subject and contraction-specific data to yield MI. Surface myoelectric signals were collected from nine healthy participants during isometric, cyclic and random fatiguing contractions. Two datasets were collected: one for tuning the functions and the other for testing. The performance of fatigue indices was evaluated using a newly proposed piece-wise linear signal to noise ratio. ANN based indices were compared to normalized spectral moments (NSM) and mean frequency (MF). GMI performed as well as MI and outperformed NSM and MF demonstrating that subject and contraction-specific baseline data is not needed in order to train a mapping function which can effectively assess fatigue.