Cost Efficient Sensor Positions Determination For Human Activity Recognition

Abstract

Human activity recognition(HAR) is one of the most active topics in the field of ubiquitous computing. Multi-sensor based HAR has attracted extensive interest because of its high recognition accuracy. Correspondingly, the number of body sensors in terms of hardware cost, the overload on communications, the storage and the computational complexity will be very high. In this paper, we propose a novel approach which can optimize cost-efficient sensors positions to save all the costs while maintaining high recognition performance. The tradeoff among the sensor positions, the target category, and the redundancy is considered. We also propose a data set D that contains acceleration sensor data for seventeen positions of the human body by simulating the worker actions in a factory assembly lines. The experimental results show that only six sensors can maintain high activity recognition accuracy out of seventeen sensors by using the proposed method.