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Research Article | Open Access

Hand Gestures for Elderly Care Using a Microsoft Kinect

Munir Oudah1, Ali Al-Naji1,2( ), Javaan Chahl2,3
Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq
School of Engineering, University of South Australia, Mawson Lakes SA 5095, Australia
Joint and Operations Analysis Division, Defence Science and Technology Group, Melbourne VIC 3207, Australia
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Abstract

The link between humans and computers, called human-computer interaction (HCI) techniques, has the potential to improve quality of life, where analysis of the information collected from humans through computers allows personal patient requirements to be achieved. Among them, a computer vision system for helping elderly patients currently attracts a large amount of research interest to avail of personal requirements. This paper proposes a real-time computer vision system to recognize hand gestures for elderly patients who are disabled or unable to translate their orders or feelings into words. The proposed system uses a Microsoft Kinect v2 Sensor, installed in front of the elderly patient, to recognize hand signs that correspond to a specific request and sends their meanings to the care provider or family member through a microcontroller and global system for mobile communications (GSM). The experimental results illustrated the effectiveness of the proposed system, which showed promising results with several hand signs, whilst being reliable, safe, comfortable, and cost-effective.

Keywords

Real-timeNon-contact computer vision systemMicrosoft kinect sensorArduino nano microcontrollerGlobal system for mobile communication (GSM)Elderly care

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Nano Biomedicine and Engineering
Volume 12 Issue 3,
September 2020
Pages 197-204
DOI: 10.5101/nbe.v12i3.p197-204
Cite this article:
Oudah M, Al-Naji A, Chahl J. Hand Gestures for Elderly Care Using a Microsoft Kinect. Nano Biomedicine and Engineering, 2020, 12(3): 197-204. https://doi.org/10.5101/nbe.v12i3.p197-204

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Received: 26 March 2020
Accepted: 20 June 2020
Published: 27 July 2020
© Munir Oudah, Ali Al-Naji, and Javaan Chahl.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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