Inferring motion direction using commodity Wi-Fi for interactive exergames

Kun Qian; Chenshu Wu; Zimu Zhou; Yue Zheng; Zheng Yang; Yunhao Liu

doi:10.1145/3025453.3025678

Inferring motion direction using commodity Wi-Fi for interactive exergames

Kun Qian, Chenshu Wu, Zimu Zhou, Yue Zheng, Zheng Yang, Yunhao Liu

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

213 Citations (Scopus)

Abstract

In-air interaction acts as a key enabler for ambient intelligence and augmented reality. As an increasing popular example, ex-ergames, 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. © 2017 ACM.

Original language	English
Title of host publication	CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire
Publisher	Association for Computing Machinery
Pages	1961-1972
Volume	2017-May
ISBN (Print)	9781450346559
DOIs	https://doi.org/10.1145/3025453.3025678
Publication status	Published - 2 May 2017
Externally published	Yes
Event	2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 - Colorado Convention Center, Denver, United States Duration: 6 May 2017 → 11 May 2017 http://chi2017.acm.org/

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings
Volume	2017-May

Conference

Conference	2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
Place	United States
City	Denver
Period	6/05/17 → 11/05/17
Internet address	http://chi2017.acm.org/

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Exergame
Motion direction recognition
Off-the-shelf Wi-Fi
Wireless sensing

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10.1145/3025453.3025678

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Cite this

Qian, K., Wu, C., Zhou, Z., Zheng, Y., Yang, Z., & Liu, Y. (2017). Inferring motion direction using commodity Wi-Fi for interactive exergames. In CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire (Vol. 2017-May, pp. 1961-1972). (Conference on Human Factors in Computing Systems - Proceedings; Vol. 2017-May). Association for Computing Machinery. https://doi.org/10.1145/3025453.3025678

Qian, Kun ; Wu, Chenshu ; Zhou, Zimu et al. / Inferring motion direction using commodity Wi-Fi for interactive exergames. CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. Vol. 2017-May Association for Computing Machinery, 2017. pp. 1961-1972 (Conference on Human Factors in Computing Systems - Proceedings).

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abstract = "In-air interaction acts as a key enabler for ambient intelligence and augmented reality. As an increasing popular example, ex-ergames, 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. {\textcopyright} 2017 ACM.",

keywords = "Exergame, Motion direction recognition, Off-the-shelf Wi-Fi, Wireless sensing",

author = "Kun Qian and Chenshu Wu and Zimu Zhou and Yue Zheng and Zheng Yang and Yunhao Liu",

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Qian, K, Wu, C, Zhou, Z, Zheng, Y, Yang, Z & Liu, Y 2017, Inferring motion direction using commodity Wi-Fi for interactive exergames. in CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. vol. 2017-May, Conference on Human Factors in Computing Systems - Proceedings, vol. 2017-May, Association for Computing Machinery, pp. 1961-1972, 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017, Denver, United States, 6/05/17. https://doi.org/10.1145/3025453.3025678

Inferring motion direction using commodity Wi-Fi for interactive exergames. / Qian, Kun; Wu, Chenshu; Zhou, Zimu et al.
CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. Vol. 2017-May Association for Computing Machinery, 2017. p. 1961-1972 (Conference on Human Factors in Computing Systems - Proceedings; Vol. 2017-May).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Inferring motion direction using commodity Wi-Fi for interactive exergames

AU - Qian, Kun

AU - Wu, Chenshu

AU - Zhou, Zimu

AU - Zheng, Yue

AU - Yang, Zheng

AU - Liu, Yunhao

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2017/5/2

Y1 - 2017/5/2

N2 - In-air interaction acts as a key enabler for ambient intelligence and augmented reality. As an increasing popular example, ex-ergames, 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. © 2017 ACM.

AB - In-air interaction acts as a key enabler for ambient intelligence and augmented reality. As an increasing popular example, ex-ergames, 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. © 2017 ACM.

KW - Exergame

KW - Motion direction recognition

KW - Off-the-shelf Wi-Fi

KW - Wireless sensing

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DO - 10.1145/3025453.3025678

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781450346559

VL - 2017-May

T3 - Conference on Human Factors in Computing Systems - Proceedings

SP - 1961

EP - 1972

BT - CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire

PB - Association for Computing Machinery

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Y2 - 6 May 2017 through 11 May 2017

ER -

Qian K, Wu C, Zhou Z, Zheng Y, Yang Z, Liu Y. Inferring motion direction using commodity Wi-Fi for interactive exergames. In CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. Vol. 2017-May. Association for Computing Machinery. 2017. p. 1961-1972. (Conference on Human Factors in Computing Systems - Proceedings). doi: 10.1145/3025453.3025678

Inferring motion direction using commodity Wi-Fi for interactive exergames

Abstract

Publication series

Conference

Bibliographical note

Research Keywords

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