Inferring Motion Direction using Commodity Wi-Fi for Interactive Exergames

論文URL:http://dl.acm.org/citation.cfm?doid=3025453.3025678

論文アブストラクト: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.

日本語のまとめ:

既製のWi-Fiデバイス上の複数のアンテナを利用することで、Wi-Fi信号からドップラー効果を正確に導出し、エクササイズ系のゲームで使える、人のモーション方向を抽出するシステムWiDanceを提案し実験と評価を行った。

(109文字)

発表スライド: