@Article{cmc.2023.028712,
AUTHOR = {Hammad Rustam, Muhammad Muneeb, Suliman A. Alsuhibany, Yazeed Yasin Ghadi, Tamara Al Shloul, Ahmad Jalal, Jeongmin Park},
TITLE = {Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {75},
YEAR = {2023},
NUMBER = {1},
PAGES = {2331--2346},
URL = {http://www.techscience.com/cmc/v75n1/51420},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2023.028712}
}