WiEnhance: Towards Data Augmentation in Human Activity Recognition Using WiFi Signal

Abstract

Recent research have devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual's limb motions in the WiFi spectrum could interfere wireless signal propagation which manifested as unique patterns for activities recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of a major challenge. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carry substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual's activities. To address this challenge, we propose WiEnhance, a WiFi based activity recognition system that synthesize variant activities data and mitigate the impact of activity inconsistency and subject-specific issues. We conduct extensive experiments and show an average 15.6% performance improvement on activity recognition.