Hand Gesture Recognition Using Continuous Wave (CW) Radar based on Hybrid PCA-KNN

Development in human-computer interactions (HCI) has increased over the last decades due to the widespread use of computing devices such as portable computers and smartphones. While the conventional keyboard and mouse are effective, their use can be limited, therefore the hand gesture is an alternat...

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Published in:IEEE Symposium on Wireless Technology and Applications, ISWTA
Main Author: Rashid N.E.A.; Nor Y.A.I.M.; Sharif K.K.M.; Khan Z.I.; Zakaria N.A.
Format: Conference paper
Language:English
Published: IEEE Computer Society 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125754345&doi=10.1109%2fISWTA52208.2021.9587404&partnerID=40&md5=de61586f863d4e9c3197930108f6f7d3
id 2-s2.0-85125754345
spelling 2-s2.0-85125754345
Rashid N.E.A.; Nor Y.A.I.M.; Sharif K.K.M.; Khan Z.I.; Zakaria N.A.
Hand Gesture Recognition Using Continuous Wave (CW) Radar based on Hybrid PCA-KNN
2021
IEEE Symposium on Wireless Technology and Applications, ISWTA
2021-August

10.1109/ISWTA52208.2021.9587404
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125754345&doi=10.1109%2fISWTA52208.2021.9587404&partnerID=40&md5=de61586f863d4e9c3197930108f6f7d3
Development in human-computer interactions (HCI) has increased over the last decades due to the widespread use of computing devices such as portable computers and smartphones. While the conventional keyboard and mouse are effective, their use can be limited, therefore the hand gesture is an alternative interaction method. The development of a hand gesture recognition system will certainly improve the existing HCI systems. In this study, a hand gesture recognition system was presented using commercially available continuous-wave (CW) radar combined with a classification algorithm based on k-nearest neighbours. The principal component analysis (PCA) is proposed to extract features and reduce dimensionality of hand gesture spectra. Performance validation based on the leave-one-out procedure shows that the proposed system has almost 100% classification accuracy. © 2021 IEEE
IEEE Computer Society
23247843
English
Conference paper

author Rashid N.E.A.; Nor Y.A.I.M.; Sharif K.K.M.; Khan Z.I.; Zakaria N.A.
spellingShingle Rashid N.E.A.; Nor Y.A.I.M.; Sharif K.K.M.; Khan Z.I.; Zakaria N.A.
Hand Gesture Recognition Using Continuous Wave (CW) Radar based on Hybrid PCA-KNN
author_facet Rashid N.E.A.; Nor Y.A.I.M.; Sharif K.K.M.; Khan Z.I.; Zakaria N.A.
author_sort Rashid N.E.A.; Nor Y.A.I.M.; Sharif K.K.M.; Khan Z.I.; Zakaria N.A.
title Hand Gesture Recognition Using Continuous Wave (CW) Radar based on Hybrid PCA-KNN
title_short Hand Gesture Recognition Using Continuous Wave (CW) Radar based on Hybrid PCA-KNN
title_full Hand Gesture Recognition Using Continuous Wave (CW) Radar based on Hybrid PCA-KNN
title_fullStr Hand Gesture Recognition Using Continuous Wave (CW) Radar based on Hybrid PCA-KNN
title_full_unstemmed Hand Gesture Recognition Using Continuous Wave (CW) Radar based on Hybrid PCA-KNN
title_sort Hand Gesture Recognition Using Continuous Wave (CW) Radar based on Hybrid PCA-KNN
publishDate 2021
container_title IEEE Symposium on Wireless Technology and Applications, ISWTA
container_volume 2021-August
container_issue
doi_str_mv 10.1109/ISWTA52208.2021.9587404
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125754345&doi=10.1109%2fISWTA52208.2021.9587404&partnerID=40&md5=de61586f863d4e9c3197930108f6f7d3
description Development in human-computer interactions (HCI) has increased over the last decades due to the widespread use of computing devices such as portable computers and smartphones. While the conventional keyboard and mouse are effective, their use can be limited, therefore the hand gesture is an alternative interaction method. The development of a hand gesture recognition system will certainly improve the existing HCI systems. In this study, a hand gesture recognition system was presented using commercially available continuous-wave (CW) radar combined with a classification algorithm based on k-nearest neighbours. The principal component analysis (PCA) is proposed to extract features and reduce dimensionality of hand gesture spectra. Performance validation based on the leave-one-out procedure shows that the proposed system has almost 100% classification accuracy. © 2021 IEEE
publisher IEEE Computer Society
issn 23247843
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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