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Complex human action recognition on daily living environments using wearable inertial sensors

The aim of this study is to evaluate how similar a set of human actions are when they are performed under controlled conditions versus the same set of actions when they are performed under uncontrolled conditions, namely in daily living environments such as the users' houses. This research is important for automatic recognition of human actions in daily living environments, mainly using wearable sensors, which is still an open research challenge of the field of pervasive computing. Action recognition is important for various applications of this field, such as, for instance, ambient intelligence, smart devices, and healthcare. In this work, we measure and analyze five human complex actions using wearable sensors in both, structured and daily living environments. Three wearable inertial sensor units were used in this study and they were worn by three healthy young subjects on three points of their upper limbs: the scapula, the upper arm and the forearm. The complex actions involved in this study are: grooming, cooking, eating, doing housework, and mouth care. Dynamic Time Warping algorithm was used to measure the intra and inter test variability of actions in both environments. Additionally, the results of the application of three supervised classification techniques, namely C4.5, Naive Bayes and Logistic Regression, are compared in terms of true positive rate TPR, true negative rate TNR and F-measure metrics. The classification models were based on time-domain and frequency-domain features extracted from orientation signals. According to the analysis cooking and eating are the actions with highest and lowest variability, respectively. Concerning the classification results, Naive Bayes and Logistic Regression obtain a TPR of 0.911 using relevant attributes. Our results provide valuable information to measure the similarity of a set of complex actions in daily living environments and to classify them.