RoSeFi: A Robust Sedentary Behavior Monitoring System With Commodity WiFi Devices

Abstract

Sedentary behaviors are shown to be hazardous to human health. Detecting sedentary behaviors in a ubiquitous way can be realized by the promising WiFi sensing technique. The accurate detection of sedentary behaviors is determined by the accurate recognition of sit-stand postural transition (SPT). However, according to our findings, SPT recognition errors are inevitable even with advanced machine-learning methods, because different SPTs may result in a similar change in WiFi channel state information (CSI). To effectively reduce SPT recognition errors, in this paper we propose RoSeFi, a robust sedentary behavior monitoring system. We first classify the errors in SPT recognition results into two categories: the errors violating SPT's consistency and the errors violating SPTs' symmetry. To correct the above errors, we reveal two inherent features in the CSI data of SPTs, i.e., contextual association and waveform mirror symmetry. Then a novel metric named WMSF is defined to quantify the degree of waveform mirror symmetry between two SPTs' CSI data. Integrating the above features, the problem of recognition error correction can be modeled as a constrained nonlinear optimization problem (CNOP). To solve the problem, we design a unified error detection/correction scheme, named UEDC, which converts the CNOP into a sequence decoding problem in Hidden Markov Model (HMM). A tailored Viterbi algorithm combined with WMSF is proposed to detect and correct the errors simultaneously. The experimental results show that RoseFi reduces 60-82% SPT recognition errors, gains 15-20% relative improvement in the accuracy of SPT recognition, and eventually reduces the sedentary time estimation errors by 10%-20%, compared with typical existing systems. In addition, our error correction method can be adapted to most existing machine learning based human action recognition methods, effectively improving their performance.