TY  - JOUR
AU  - Rustam, Hammad
AU  - Muneeb, Muhammad
AU  - Alsuhibany, Suliman A.
AU  - Ghadi, Yazeed Yasin
AU  - Shloul, Tamara Al
AU  - Jalal, Ahmad
AU  - Park, Jeongmin
PY  - 2023
TI  - Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors
JO  - Computers, Materials & Continua
SN  - 1546-2218
SP  - 2331
EP  - 2346
VL  - 75
IS  - 1
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.
UR  - https://doi.org/10.32604/cmc.2023.028712
DO  - 10.32604/cmc.2023.028712