Open Access

ARTICLE

Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors

by Hammad Rustam¹, Muhammad Muneeb¹, Suliman A. Alsuhibany², Yazeed Yasin Ghadi³, Tamara Al Shloul⁴, Ahmad Jalal¹, Jeongmin Park^5,*

1 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
2 Department of Computer Science, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia
3 Department of Computer Science and Software Engineering, Al Ain University, Al Ain, 15551, UAE
4 Department of Humanities and Social Science, Al Ain University, Al Ain, 15551, UAE
5 Department of Computer Engineering, Korea Polytechnic University, 237 Sangidaehak-ro Siheung-si, Gyeonggi-do, 15073, Korea

* Corresponding Author: Jeongmin Park. Email:

Computers, Materials & Continua 2023, 75(1), 2331-2346. https://doi.org/10.32604/cmc.2023.028712

Received 16 February 2022; Accepted 19 April 2022; Issue published 06 February 2023

Abstract

Hand gesture recognition (HGR) is used in a numerous applications, including medical health-care, industrial purpose and sports detection. We have developed a real-time hand gesture recognition system using inertial sensors for the smart home application. Developing such a model facilitates the medical health field (elders or disabled ones). Home automation has also been proven to be a tremendous benefit for the elderly and disabled. Residents are admitted to smart homes for comfort, luxury, improved quality of life, and protection against intrusion and burglars. This paper proposes a novel system that uses principal component analysis, linear discrimination analysis feature extraction, and random forest as a classifier to improve HGR accuracy. We have achieved an accuracy of 94% over the publicly benchmarked HGR dataset. The proposed system can be used to detect hand gestures in the healthcare industry as well as in the industrial and educational sectors.

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Keywords

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