A Hierarchical Approach towards Activity Recognition
Dario Ortega Anderez, Kofi Appiah, Ahmad Lotfi, Caroline Langesiepen · 2017
Activity recognition with the use of inertial sensors, namely accelerometers and gyroscopes, has gained increasing attention during the last decades. In this work, we propose a novel way of tackling activity classification by developing a multi-step hierarchical classification algorithm. While previous research has looked at the problem as a whole, by adopting one of the two major approaches for activity recognition -- the sliding window approach and primitive-based approach, our system will divide the classification problem into smaller classification problems following a hierarchical approach for improve on accuracy and computational cost. This work aims at detecting self-neglect behaviour in a living environment. As such, the activities chosen to be classified consist of quotidian daily living activities such as walking, brushing teeth, washing hands, typing at the computer, sitting, stand and picking up something from the floor. The experimental work has shown promising results which support the use of the multi-step hierarchical approach proposed in this paper.