A package designed to predict static pose and detect falls with 2D RGB Camera in well lit indoor environments.
Project description
AI Posture Monitor
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.
GITHUB: https://github.com/pat2echo/
Features
- Real-Time Activity Monitoring: Tracks and analyzes movements continuously.
- Pose Detection: Identifies key postures**: standing, sitting, and lying down.
- Fall Detection: Detects falls with minimal false alarms.
- Fuzzy Logic Analysis: Utilizes fuzzy logic for accurate movement interpretation.
- User-Centric Design: Tailored for elderly individuals living alone.
- Environmental Adaptability: Functions effectively in well-lit indoor settings.
- Scalable and Cost-Effective: Affordable solution for diverse applications.
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