Open AccessComputer Vision for Elderly Care Based on Deep Learning CNN and SVM
Author(s) -
Maitham Oudah,
Ali Al-Naji,
Javaan Chahl
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1105/1/012070
Subject(s) - gesture , computer science , convolutional neural network , gsm , support vector machine , interface (matter) , mobile phone , gesture recognition , human–computer interaction , multimedia , health care , artificial intelligence , mobile device , computer network , world wide web , operating system , bubble , maximum bubble pressure method , economics , economic growth
Computer vision has wide application in medical sciences such as health care and home automation. This study on computer vision for elderly care is based on a Microsoft Kinect sensor considers an inexpensive, three dimensional, non-contact technique, that is comfortable for patients while being highly reliable and suitable for long term monitoring. This paper proposes a hand gesture system for elderly health care based on deep learning convolutional neural network (CNN) that is used to extract features and to classify five gestures according to five categories using a support vector machine (SVM). The proposed system is beneficial for elderly patients who are voiceless or deaf-mute and unable to communicate with others. Each gesture indicates a specific request such as “Water”, “Meal”, “Toilet”, “Help” and “Medicine” and translates as a command sending to a Microcontroller circuit that sends the request to the caregiver’s mobile phone via the global system for mobile communication (GSM). The system was tested in an indoor environment and provides reliable outcomes and a useful interface for older people with disabilities in their limbs to communicate with their families and caregivers.