Human Movement Modeling and Activity Perception Based on Fiber-Optic Sensing System

Abstract

This paper presents a flexible fiber-optic sensor-based pressure sensing system for human activity analysis and situation perception in indoor environments. In this system, a binary sensing technology is applied to reduce the data workload, and a bipedal movement-based space encoding scheme is designed to capture people's geometric information. We also develop a nonrepetitive encoding scheme to eliminate the ambiguity caused by the two-foot structure of bipedal movements. Furthermore, we propose an invariant activity representation model based on trajectory segments and their statistical distributions. In addition, a mixture model is applied to represent scenarios. The number of subjects is finally determined by Bayesian information criterion. The Bayesian network and region of interests are employed to facilitate the perception of interactions and situations. The results are obtained using distribution divergence estimation, expectation-maximization, and Bayesian network inference methods. In the experiments, we simulated an office environment and tested walk, work, rest, and talk activities for both one and two person cases. The experiment results have demonstrated that the average individual activity recognition is higher than 90%, and the situation perception rate can achieve 80%.