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

Hammad Rustam1, Muhammad Muneeb1, Suliman A. Alsuhibany2, Yazeed Yasin Ghadi3, Tamara Al Shloul4, Ahmad Jalal1, Jeongmin Park5,*

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.


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.


Cite This Article

H. Rustam, M. Muneeb, S. A. Alsuhibany, Y. Y. Ghadi, T. A. Shloul et al., "Home automation-based health assessment along gesture recognition via inertial sensors," Computers, Materials & Continua, vol. 75, no.1, pp. 2331–2346, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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