Inferring motion direction using commodity Wi-Fi for interactive exergames

• Main Authors: QIAN, Kun, WU, Chenshu, ZHOU, Zimu, ZHENG, Yue, ZHENG, Yang, LIU, Yunhao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Motion Direction Recognition; Wireless Sensing; Off-the-shelf Wi-Fi; Exergame; Digital Communications and Networking; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4742; https://ink.library.smu.edu.sg/context/sis_research/article/5745/viewcontent/chi17_qian.pdf
• Institution: Singapore Management University

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, pervasive and low-cost user interfaces. 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.