Surface electromyography analysis in long-term recordings: application to head rest comfort in cars

Analysis of long-term surface electromyographic (SEMG) signals has many applications in ergonomics when related to muscle fatigue. The present work proposes a set of processing methods reporting SEMG modifications during long-term driving tests in various situations (with or without head rest). A segmentation/classification algorithm allows signal splitting into homogeneous parts (postural activity and EMG bursts) and an efficient artefact suppression. Postural activity modifications are evaluated from time-varying amplitude probability density function (TAPDF) parameters. EMG burst analysis is achieved taking into account the relationships of these bursts with accelerometric events. This segmentation/classification procedure improves repeatability but does not significantly modify the overall results obtained before segmentation, as far as the analysis of head rest influence is concerned.