Error-Correction Enabled Contactless Sedentary Behavior Detection via WiFi Sensing

Abstract

Sedentary lifestyle has become a major health risk in modern society. Long sitting time can be detected by accurate recognition of sitting and standing (sit-stand) activities. WiFi-based sitting time detection has the remarkable advantage of low cost, noncontact, and privacy-protection. However, accurate recognition of sit-stand activities via WiFi signal is still facing two challenges. The first and also tougher challenge is inevitable mistakes in recognition results of traditional machine learning methods, while the second challenge is the difficulty of accurate activity segmentation before activity recognition. To the best of the authors' knowledge, few work addresses the above challenges, particularly the first challenge. A new contactless sitting time detection system is designed accordingly. The system first accurately segments all activities and removes in-seat activities. Then, the mistakes in sit-stand activity recognition are effectively corrected by a new recognition error correction method. The proposed method first creates and updates a correction benchmark that can satisfy both successive correlation and waveform symmetry between sit-stand activities. The recognition results of traditional machine learning methods are then corrected based on the latest correction benchmark. Extensive experiment results demonstrate that compared to related work, the designed system has much better accuracy on both sit-stand activity recognition and sitting time estimation. The experiment results also demonstrate the robustness of the designed system.