  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.
