Inferring Motion Direction with Wi-Fi

In-air interaction acts as a key enabler for ambient intelligence and augmented reality. As an increasing popular example, exergames, and the alike gesture recognition applications, have attracted extensive research in designing accurate and low-cost user interfaces applicable in whole-home environments. Recent advances in wireless sensing show promise for a ubiquitous gesture-based interaction interface with Wi-Fi. In this work, we extract complete information of motion-induced Doppler shifts with only commodity Wi-Fi. The key insight is to harness antenna diversity to carefully eliminate random phase shifts while retaining relevant Doppler shifts. We further correlate Doppler shifts with motion directions and propose a light-weight pipeline to detect, segment, and recognize motions without training. On this basis, we present WiDance, a Wi-Fi-based user interface, which we utilize to design and prototype a contactless dance-pad exergame. Experimental results in typical indoor environment demonstrate a superior performance with an accuracy of 92%, remarkably outperforming prior approaches. (This chapter is based on our previously published paper. Inferring Motion Direction using Commodity Wi-Fi for Interactive Exergames. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ‘17), Pages 1961–1972, Ⓒ2017 ACM, Inc. https://doi.org/10.1145/3025453.3025678.)