Indoor Person Position Estimation with Channel State Information with Privacy Consideration

This paper presents a CSI-based human detection system with commodity sensors: M5Stack. Especially, we remedy too sensitivity to individual with respect to measured CSI. Monitoring systems considering privacy proof is important for monitoring the elderly and children. In this paper, we carry out experiments on indoor human location estimation using Channel State Information (CSI). Specifically, to improve recognition accuracy in cases where training data and test data are generated from different persons, we proposed a method that includes dimensionality reduction using Principal Component Analysis (PAC) as a preprocessing. In our experiments, we clarify problems of CSI-based method and solve the problems using the proposed method. In the evaluation experiments, we achieved an improvement of 10% to 30% depending on individuals.