TY - EJOU AU - Rustam, Hammad AU - Muneeb, Muhammad AU - Alsuhibany, Suliman A. AU - Ghadi, Yazeed Yasin AU - Shloul, Tamara Al AU - Jalal, Ahmad AU - Park, Jeongmin TI - Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors T2 - Computers, Materials \& Continua PY - 2023 VL - 75 IS - 1 SN - 1546-2226 AB - 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. KW - 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 DO - 10.32604/cmc.2023.028712