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In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID Tags
College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China.
College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China; Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China.
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).ORCID-id: 0000-0002-2763-8085
College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China; Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China; Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Post and Telecommunications, Nanjing, 210003, China.
2019 (engelsk)Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 94460-94472Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In-air gesture interaction enables a natural communication between a man and a machine with its clear semantics and humane mode of operation. In this paper, we propose a real-time recognition system on multiple gestures in the air. It uses the commodity off-the-shelf (COTS) reader with three antennas to detect the radio frequency (RF) signals of the passive radio frequency identification (RFID) Tags attached to the fingers. The idea derives from the crucial insight that the sequential phase profile of the backscatter RF signals is a reliable and well-regulated indicator insinuating space-time situation of the tagged object, which presents a close interdependency with tag's movements and positions. The KL divergence is utilized to extract the dynamic gesture segment by confirming the endpoints of the data flow. To achieve the template matching and classification, we bring in the dynamic time warping (DTW) and k-nearest neighbors (KNN) for similarity scores calculation and appropriate gesture recognition. The experiment results show that the recognition rates for static and dynamic gestures can reach 85% and 90%, respectively. Moreover, it can maintain satisfying performance under different situations, such as diverse antenna-to-user distances and being hidden from view by nonconducting obstacles.

sted, utgiver, år, opplag, sider
IEEE, 2019. Vol. 7, s. 94460-94472
Emneord [en]
Gesture recognition, radio frequency identification (RFID), phase
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Identifikatorer
URN: urn:nbn:se:mau:diva-2419DOI: 10.1109/ACCESS.2019.2928318ISI: 000478676600025Scopus ID: 2-s2.0-85073915435Lokal ID: 30221OAI: oai:DiVA.org:mau-2419DiVA, id: diva2:1399172
Tilgjengelig fra: 2020-02-27 Laget: 2020-02-27 Sist oppdatert: 2024-06-17bibliografisk kontrollert

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