Multi-Criteria Analysis of Sensor Reliability for Wearable Human Activity Recognition

In Body Sensor Networks (BSNs), evaluating reliability of sensors is an important research topic which aims to optimize the overall performance of BSNs. Previous studies have often addressed this problem based only on a single criterion. However, it is often unreliable to rely on a single criterion to assess sensors in real situations. Accordingly, in this paper, we propose a novel multi-criteria approach for evaluating sensor reliability in activity recognition problem based on belief function theory. Specifically, in the theoretical part, we first describe the Multi-Criteria Analysis of Sensor Reliability (MCASR) using Belief Function based the Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS). And in our proposed MCARS, two criteria are chosen in this work: 1) the conflict between sensor readings and, 2) the imprecision of sensor readings. In the application part, in order to prove the efficiency of MCASR, we propose a novel fused Long-Short Term Memory (LSTM) with MCASR to solve the problem of activity recognition. By using our proposed strategy, the final recognition accuracy has been significantly improved as compared with classical methods.