Muscle fatigue detection based on sEMG signal using autocorrelation function and neural networks

Feature extraction from an recorded surface electromyography (sEMG) signal plays an important role in identifying and quantifying the characteristics of muscle activities. These features can be used for various applications like muscle function assessment, muscle fatigue detection, etc. Common features extracted from sEMG signal are time-domain or frequency-domain features. However, features which are sensitive to uncertainties in the signal like noise, movement artifacts, and outliers should be avoided. Autocorrelation function (ACF), which is a measure of similarity between a signal and its time delayed version, is considered in this work as a feature to overcome the impact of noise, artifacts, and outliers. An artificial neural network (ANN) is developed to differentiate between fatigue and non-fatigue conditions using the calculated ACF from sEMG segments. The performance of an ANN model that can be adapted by means of various regularization methods was investigated. The proposed ANN model achieved an accuracy of about 97.62 %, a precision of about 95.50 % and a sensitivity of about 100 % in the classification of fatigue and non-fatigue sEMG segments, outperforming k-means and linear support vector machine approaches that served as references.