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Towards ubiquitous human gestures recognition using wireless networks

Towards ubiquitous human gestures recognition using wireless networks PurposeRecently, many researches have been devoted to studying the possibility of using wireless signals of the Wi-Fi networks in human-gesture recognition. They focus on classifying gestures despite who is performing them, and only a few of the previous work make use of the wireless channel state information in identifying humans. This paper aims to recognize different humans and their multiple gestures in an indoor environment.Design/methodology/approachThe authors designed a gesture recognition system that consists of channel state information data collection, preprocessing, features extraction and classification to guess the human and the gesture in the vicinity of a Wi-Fi-enabled device with modified Wi-Fi-device driver to collect the channel state information, and process it in real time.FindingsThe proposed system proved to work well for different humans and different gestures with an accuracy that ranges from 87 per cent for multiple humans and multiple gestures to 98 per cent for individual humans’ gesture recognition.Originality/valueThis paper used new preprocessing and filtering techniques, proposed new features to be extracted from the data and new classification method that have not been used in this field before. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Pervasive Computing and Communications Emerald Publishing

Towards ubiquitous human gestures recognition using wireless networks

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1742-7371
DOI
10.1108/IJPCC-D-17-00005
Publisher site
See Article on Publisher Site

Abstract

PurposeRecently, many researches have been devoted to studying the possibility of using wireless signals of the Wi-Fi networks in human-gesture recognition. They focus on classifying gestures despite who is performing them, and only a few of the previous work make use of the wireless channel state information in identifying humans. This paper aims to recognize different humans and their multiple gestures in an indoor environment.Design/methodology/approachThe authors designed a gesture recognition system that consists of channel state information data collection, preprocessing, features extraction and classification to guess the human and the gesture in the vicinity of a Wi-Fi-enabled device with modified Wi-Fi-device driver to collect the channel state information, and process it in real time.FindingsThe proposed system proved to work well for different humans and different gestures with an accuracy that ranges from 87 per cent for multiple humans and multiple gestures to 98 per cent for individual humans’ gesture recognition.Originality/valueThis paper used new preprocessing and filtering techniques, proposed new features to be extracted from the data and new classification method that have not been used in this field before.

Journal

International Journal of Pervasive Computing and CommunicationsEmerald Publishing

Published: Nov 6, 2017

References