Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/10462
Title: Hand gestures classification using electrical impedance tomography images
Authors: Dr. NAWAZ Mehmood 
Chan, Russell W. 
Malik, Anju 
Khan, Tariq 
Cao, Peng 
Issue Date: 2022
Source: IEEE Sensors Journal, 2022, vol. 22(19), pp. 18922-18932.
Journal: IEEE Sensors Journal 
Abstract: Human–computer communication using hand gestures has always been difficult. More than half a century ago, people used differentways of interactionwith computers from the early mediums such as perforated game cards. Nowadays, if a richer lexicon of gestures is given, people can communicate more effectively with computers. Machine learning is now used to recognize and classify hand gestures in amore preciseway. In order to increase the communication between computers and humans, we proposed a technique, which uses a wearable low-cost device to generate the electrical impedance tomography (EIT) images to recover the inner impedance structure of a user’s wrist. This is done by measuring the transverse impedance between all the 16 pairs of electrodesofwrist band that lie on the skin of the user hand. The proposedtechnique is enough to integrate the technology into the prototypewrist band tomonitor and classify gestures in real time. We have conducted a study of 16 gestures with a focus on gross hand and pinch finger gestures. The results evaluation shows that the gross hand gestures achieved 90% accuracy inwrist position, while pinch gestures achieved 93% accuracy.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/10462
ISSN: 1530-437X
1558-1748
DOI: 10.1109/JSEN.2022.3193718
Appears in Collections:Applied Data Science - Publication

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