Hand gesture recognition based on Wi-Fi chipsets

Tenglong Fan; Dexin Ye; Jiangtao Hangfu; Yongzhi Sun; Changzhi Li; Lixin Ran

doi:10.1109/RWS.2017.7885956

Hand gesture recognition based on Wi-Fi chipsets

Tenglong Fan, Dexin Ye, Jiangtao Hangfu, Yongzhi Sun, Changzhi Li, Lixin Ran

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Hand gesture recognition (HGR) based on radar technology is becoming a hot topic recently. In this paper, we utilize a regular Wi-Fi transceiver chipset to construct a coherent radar sensor. The direct conversion and digital baseband signal processing technologies are used to detect hand motions with specific signatures. Experiments based on a dual-channel receiver aiming to detect two-dimensional motions are conducted, and the results show promising potentials in HGR applications.

Original language	English
Title of host publication	2017 IEEE Radio and Wireless Symposium, RWS 2017
Publisher	IEEE Computer Society
Pages	98-100
Number of pages	3
ISBN (Electronic)	9781509034451
DOIs	https://doi.org/10.1109/RWS.2017.7885956
State	Published - Mar 23 2017
Event	2017 IEEE Radio and Wireless Symposium, RWS 2017 - Phoenix, United States Duration: Jan 15 2017 → Jan 18 2017

Publication series

Name	IEEE Radio and Wireless Symposium, RWS
ISSN (Print)	2164-2958
ISSN (Electronic)	2164-2974

Conference

Conference	2017 IEEE Radio and Wireless Symposium, RWS 2017
Country/Territory	United States
City	Phoenix
Period	01/15/17 → 01/18/17

Keywords

Pattern recognition
Wi-Fi
radar sensor

Access to Document

10.1109/RWS.2017.7885956

Cite this

@inproceedings{b945e6b671a74d4c8ec603201ca6ee40,

title = "Hand gesture recognition based on Wi-Fi chipsets",

abstract = "Hand gesture recognition (HGR) based on radar technology is becoming a hot topic recently. In this paper, we utilize a regular Wi-Fi transceiver chipset to construct a coherent radar sensor. The direct conversion and digital baseband signal processing technologies are used to detect hand motions with specific signatures. Experiments based on a dual-channel receiver aiming to detect two-dimensional motions are conducted, and the results show promising potentials in HGR applications.",

keywords = "Pattern recognition, Wi-Fi, radar sensor",

author = "Tenglong Fan and Dexin Ye and Jiangtao Hangfu and Yongzhi Sun and Changzhi Li and Lixin Ran",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Radio and Wireless Symposium, RWS 2017 ; Conference date: 15-01-2017 Through 18-01-2017",

year = "2017",

month = mar,

day = "23",

doi = "10.1109/RWS.2017.7885956",

language = "English",

series = "IEEE Radio and Wireless Symposium, RWS",

publisher = "IEEE Computer Society",

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TY - GEN

T1 - Hand gesture recognition based on Wi-Fi chipsets

AU - Fan, Tenglong

AU - Ye, Dexin

AU - Hangfu, Jiangtao

AU - Sun, Yongzhi

AU - Li, Changzhi

AU - Ran, Lixin

PY - 2017/3/23

Y1 - 2017/3/23

N2 - Hand gesture recognition (HGR) based on radar technology is becoming a hot topic recently. In this paper, we utilize a regular Wi-Fi transceiver chipset to construct a coherent radar sensor. The direct conversion and digital baseband signal processing technologies are used to detect hand motions with specific signatures. Experiments based on a dual-channel receiver aiming to detect two-dimensional motions are conducted, and the results show promising potentials in HGR applications.

AB - Hand gesture recognition (HGR) based on radar technology is becoming a hot topic recently. In this paper, we utilize a regular Wi-Fi transceiver chipset to construct a coherent radar sensor. The direct conversion and digital baseband signal processing technologies are used to detect hand motions with specific signatures. Experiments based on a dual-channel receiver aiming to detect two-dimensional motions are conducted, and the results show promising potentials in HGR applications.

KW - Pattern recognition

KW - Wi-Fi

KW - radar sensor

UR - http://www.scopus.com/inward/record.url?scp=85018189481&partnerID=8YFLogxK

U2 - 10.1109/RWS.2017.7885956

DO - 10.1109/RWS.2017.7885956

M3 - Conference contribution

AN - SCOPUS:85018189481

T3 - IEEE Radio and Wireless Symposium, RWS

SP - 98

EP - 100

BT - 2017 IEEE Radio and Wireless Symposium, RWS 2017

PB - IEEE Computer Society

T2 - 2017 IEEE Radio and Wireless Symposium, RWS 2017

Y2 - 15 January 2017 through 18 January 2017

ER -

Hand gesture recognition based on Wi-Fi chipsets

Abstract

Publication series

Conference

Keywords

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