Real-time fall detection and alerting system designed for hospital waiting rooms to prevent patient incidents from going unnoticed in understaffed emergency environments.
Built at Carnegie Mellon University's NexHacks (24-hour hackathon).
Hospital ER waiting rooms are often understaffed.
Patients may:
- Slump in chairs
- Faint while seated
- Slide off chairs
- Collapse without immediate supervision
Traditional camera monitoring requires constant human attention. In busy environments, subtle fall-related incidents can go unnoticed.
WatchCare-AI provides automated real-time monitoring and alerting to reduce response time and improve patient safety.
WatchCare-AI is a computer vision pipeline that:
- Monitors live video feed
- Performs real-time pose estimation
- Detects abnormal body angles and motion persistence
- Triggers instant nurse alerts
- Enables claim-and-resolve tracking for accountability
The system is designed to minimize false positives in safety-critical environments.
- Python
- OpenCV
- Pose estimation model
- Body angle threshold detection
- Motion persistence filtering logic
The detection logic evaluates:
- Torso angle deviation
- Sudden vertical displacement
- Sustained abnormal posture duration
This prevents alerts from being triggered by normal seated movements.
- Firebase Firestore
- Real-time event synchronization
- Audit logging
- Claim-and-resolve workflow for nurse accountability
Alerts include:
- Timestamp
- Camera ID
- Incident classification
- Status tracking (Open / Claimed / Resolved)
Collaborated with Android developers to deliver:
- Instant push notifications
- Real-time alert dashboard
- Ownership tracking
- Resolution confirmation
- Real-time responsiveness
- False-positive minimization
- Clear ownership assignment
- Scalable system design
- Audit-ready logging
- Python
- OpenCV
- Pose Estimation
- Firebase Firestore
- Android (integration layer)
The system evaluates:
- Body angle > predefined threshold
- Vertical drop speed exceeding tolerance
- Motion persistence over defined time window
Only if multiple conditions are satisfied is an alert triggered.
This layered validation reduces alert noise in busy hospital environments.
Developed in a 24-hour hackathon environment at Carnegie Mellon University's NexHacks.
Focused on:
- Real-world healthcare safety gaps
- Scalable architecture
- Deployable MVP logic
- Reduced ER monitoring burden
- Faster incident response time
- Improved patient safety
- Real-time accountability tracking
- Edge-device deployment for latency reduction
- HIPAA-compliant cloud architecture
- Multi-camera synchronization
- Fall severity scoring
- Machine learning refinement using real incident datasets
- Integration with hospital paging systems
- Privacy-preserving anonymized pose detection
Built by:
- Varnika Yadav
- Devaj Solanki
- Anant Patel
- Anusha Agarwal
WatchCare-AI demonstrates applied computer vision, real-time systems engineering, and safety-critical system design under time constraints.
It showcases practical AI deployment beyond theoretical modeling.