A package designed to predict static pose and detect falls with 2D RGB Camera in well lit indoor environments.
This project introduces an innovative, cost-effective solution for real-time activity monitoring of elderly individuals. By leveraging the MediaPipe pose estimation model, fuzzy logic, and finite state machines, the system can reliably track individuals, recognize static postures (standing, sitting, lying), and detect transitions, particularly focusing on falls.
FAQs
A package designed to predict static pose and detect falls with 2D RGB Camera in well lit indoor environments.
We found that ai-posture-monitor demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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