In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID Tags
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 antenna...
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doaj-53530566d08a4c9b8b43fe7c39637f432021-03-29T23:58:57ZengIEEEIEEE Access2169-35362019-01-017944609447210.1109/ACCESS.2019.29283188760239In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID TagsKang Cheng0Ning Ye1Reza Malekian2https://orcid.org/0000-0002-4001-2917Ruchuan Wang3College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing, ChinaDepartment of Computer Science and Media Technology, Malmö University, Malmö, SwedenCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing, ChinaIn-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.https://ieeexplore.ieee.org/document/8760239/Gesture recognitionradio frequency identification (RFID)phase |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kang Cheng Ning Ye Reza Malekian Ruchuan Wang |
spellingShingle |
Kang Cheng Ning Ye Reza Malekian Ruchuan Wang In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID Tags IEEE Access Gesture recognition radio frequency identification (RFID) phase |
author_facet |
Kang Cheng Ning Ye Reza Malekian Ruchuan Wang |
author_sort |
Kang Cheng |
title |
In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID Tags |
title_short |
In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID Tags |
title_full |
In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID Tags |
title_fullStr |
In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID Tags |
title_full_unstemmed |
In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID Tags |
title_sort |
in-air gesture interaction: real time hand posture recognition using passive rfid tags |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
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. |
topic |
Gesture recognition radio frequency identification (RFID) phase |
url |
https://ieeexplore.ieee.org/document/8760239/ |
work_keys_str_mv |
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