Towards fatigue and intensity measurement framework during continuous repetitive activities

With the recent advancement in the wearable sensor technology there has been many studies about recognizing user's activities, location or environment, but they did not recognize the effect of these activities on the physiological state of the person. The two major physiological aspects associated with any activity are intensity of activity and associated fatigue. Fatigue is an universal human experience that can negatively affect daily life activities. In this paper, we present a framework to measure the level of fatigue and intensity of activity during repetitive daily life activities. The proposed framework acquires and processes time series data from a surface Electromyogram (sEMG) sensor and employs state of art machine learning and data mining techniques to measure the physiological status. We tested this framework using the raw sEMG signals from the hand muscles of 10 subjects, including male and female, of age group around 25 to 45 years, collected during the continuous monitoring of repetitive palm movements at different repetition speeds. The framework graded the levels of fatigue and intensity of activity in a scale of 0 to 1 with an accuracy of 88% with AdaBoost, 94% with SVM, 96% with both HMM and KNN based machine learning techniques.