Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors
Source abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Hand Gesture Recognition Systems · Gaze Tracking and Assistive Technology · Robotics and Automated Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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