@article{Rustam2023, 
author = {Hammad Rustam and Muhammad Muneeb and Suliman A. Alsuhibany and Yazeed Yasin Ghadi and Tamara Al Shloul and Ahmad Jalal and Jeongmin Park},
title = {Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors},
year = {2023},
journal = {Computers, Materials & Continua},
volume = {75},
number = {1},
pages = {2331-2346},
keywords = {Genetic algorithm, human locomotion activity recognition, human–computer interaction, human gestures recognition principal, hand gestures recognition, inertial sensors, principal component analysis, linear discriminant analysis, stochastic neighbor embedding},
url = {https://www.sciopen.com/article/10.32604/cmc.2023.028712},
doi = {10.32604/cmc.2023.028712},
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.}
}