Static Hand Gesture Recognition Using Capacitive Sensing and Machine Learning

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dc.citation.issue7
dc.citation.volume23
dc.contributor.authorNoble F
dc.contributor.authorXu M
dc.contributor.authorAlam F
dc.date.accessioned2023-03-28T02:00:18Z
dc.date.available2023-03-24
dc.date.available2023-03-28T02:00:18Z
dc.date.issued24/03/2023
dc.description.abstractAutomated hand gesture recognition is a key enabler of Human-to-Machine Interfaces (HMIs) and smart living. This paper reports the development and testing of a static hand gesture recognition system using capacitive sensing. Our system consists of a 6×18 array of capacitive sensors that captured five gestures-Palm, Fist, Middle, OK, and Index-of five participants to create a dataset of gesture images. The dataset was used to train Decision Tree, Naïve Bayes, Multi-Layer Perceptron (MLP) neural network, and Convolutional Neural Network (CNN) classifiers. Each classifier was trained five times; each time, the classifier was trained using four different participants' gestures and tested with one different participant's gestures. The MLP classifier performed the best, achieving an average accuracy of 96.87% and an average F1 score of 92.16%. This demonstrates that the proposed system can accurately recognize hand gestures and that capacitive sensing is a viable method for implementing a non-contact, static hand gesture recognition system.
dc.description.confidentialFALSE
dc.identifier.citationSensors, 2023, 23 (7)
dc.identifier.doi10.3390/s23073419
dc.identifier.elements-id460562
dc.identifier.harvestedMassey_Dark
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/10179/18138
dc.publisherMDPI AG
dc.relation.isPartOfSensors
dc.relation.urihttps://www.mdpi.com/1424-8220/23/7/3419
dc.subject.anzsrc0301 Analytical Chemistry
dc.subject.anzsrc0805 Distributed Computing
dc.subject.anzsrc0906 Electrical and Electronic Engineering
dc.subject.anzsrc0502 Environmental Science and Management
dc.subject.anzsrc0602 Ecology
dc.titleStatic Hand Gesture Recognition Using Capacitive Sensing and Machine Learning
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Food and Advanced Technology
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