Paper detail

Analysis of LGM Model for sEMG Signals related to Weight Training

Statistical models of Surface electromyography (sEMG) signals have several applications such as better understanding of sEMG signal generation, improved pattern recognition based control of wearable exoskeletons and prostheses, improving training strategies in sports activities, and EMG simulation studies. Most of the existing studies analysed the statistical model of sEMG signals acquired under isometric contractions. However, there is no study that addresses the statistical model under isotonic contractions. In this work, a new dataset, electromyography analysis of human activities - database 2 (EMAHA-DB2) is developed. It consists of two experiments based on both isometric and isotonic activities during weight training. Previously, a novel Laplacian-Gaussian Mixture (LGM) model was demonstrated for a few benchmark datasets consisting of basic movements and gestures. In this work, the model suitability analysis is extended to the EMAHA-DB2 dataset. Further, the LGM model is compared with three existing statistical models including the recent scale-mixture model. According to qualitative and quantitative analyses, the LGM model has a better fit to the empirical pdf of the recorded sEMG signals compared with the scale mixture model and the other standard models. The variance and mixing weight of the Laplacian component of the signal are analyzed with respect to the type of muscle, type of muscle contraction, dumb-bell weight and training experience of the subjects. The sEMG variance (the Laplacian component) increases with respect to the weights, is greater for isotonic activity especially for the biceps. For isotonic activity, the signal variance increases with training experience. Importantly, the ratio of the variances from the two muscle sites is observed to be nearly independent of the lifted weight and consistently increases with the training experience.