Instance-Wise Dynamic Sensor Selection for Human Activity Recognition

Human Activity Recognition (HAR) is an important application of smart wearable/mobile systems for many humancentric problems such as healthcare. The multi-sensor synchronous measurement has shown better performance for HAR than a single sensor. However, the multi-sensor setting increases the costs of data transmission, computation and energy. Therefore, the efficient sensor selection to balance recognition accuracy and sensor cost is the critical challenge. In this paper, we propose an Instance-wise Dynamic Sensor Selection (IDSS) method for HAR. Firstly, we formalize this problem as minimizing both activity classification loss and sensor number by dynamically selecting a sparse subset for each instance. Then, IDSS solves the above minimization problem via Markov Decision Process whose policy for sensor selection is learned by exploiting the instancewise states using Imitation Learning. In order to optimize the parameters of the activity classification model and the sensor selection policy, an algorithm named Mutual DAgger is proposed to alternatively enhance their learning process. To evaluate the performance of IDSS, we conduct experiments on three real-world HAR datasets. The experimental results show that IDSS can effectively reduce the overall sensor number without losing accuracy and outperforms the state-of-theart methods regarding the combined measurement of accuracy and sensor number.

Authors

• Xiaodong Yang
• Yiqiang Chen Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences Peng Cheng Laboratory Beijing Key Laboratory of Mobile Computing and Pervasive Device
• Hanchao Yu
• Yingwei Zhang Beijing Key Lab. of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences University of Chinese Academy of Sciences
• Wang Lu
• Ruizhe Sun

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