Burnout: a wearable system for unobtrusive skeletal muscle fatigue estimation

Skeletal muscles are pivotal for sports and exercise. However, overexertion of skeletal muscles causes muscle fatigue which can lead to injury. Consequently, understanding skeletal muscle fatigue is important for injury prevention. Current ways to estimate exhaustion revolve around self-estimation or inference from such sensors as force sensors, electromyography e.t.c. These methods are not always reliable, especially during isotonic exercises. Toward this end, we present Burnout - a wearable system for quantifying skeletal muscle fatigue in an exercise setting. Burnout uses accelerometers to sense skeletal muscle vibrations. From these vibrations, Burnout obtains a region based feature (R- Feature), in the case of this work, the region mean power frequency (R-MPF) gradient to correlate the sensed vibrations to a known ground truth measure of skeletal muscle fatigue, i.e., Dimitrov's spectral fatigue index gradient. We evaluate Burnout on the biceps and quadriceps of 5 healthy participants through four different exercises, collected in a real world environment. Our results show that by using this R-MPF feature on our real world data set, Burnout is able to reduce the error of estimating the ground truth fatigue index gradient by up to 50% on average compared to using the standard MPF feature.