@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} }