Human Fall Detection: Real-time, pose-aware AI for accurate fall detection and faster response, enhancing elderly safety
Project Overview
We developed an automated fall detection system aimed at enhancing the safety of elderly individuals. By leveraging advanced video analysis techniques and pose estimation, the system is designed to improve response times in elderly care facilities, ensuring timely intervention in case of falls.
Problem Statement
The elderly population faces a high incidence of falls, often compounded by inadequate or slow monitoring systems. Existing fall detection solutions either lack accuracy, require expensive hardware, or both. This highlighted the need for a cost-effective, real-time detection system that can reliably identify falls and trigger timely interventions.
Key Findings
- Pose Identification: Through extensive testing, we successfully identified key pose changes that strongly indicate fall events, allowing for improved model precision and fewer false positives.
- Environmental Sensitivity: The model demonstrated high adaptability to varied environments and lighting conditions, significantly improving detection reliability in real-world scenarios.
- Improved Response Time: By replacing manual monitoring with real-time detection, the system drastically reduced alert response time, enabling faster interventions and potentially saving lives.
Implemented Solution
Human fall detection was developed To address these challenges, we implemented the following solutions:
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Pose Feature Extraction:
We used MediaPipe to extract skeletal pose features and trained a custom machine learning model tailored specifically to recognise fall-like movements and postures, based on domain-specific video data.
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Real-Time Video Processing:
OpenCV was integrated into the system to facilitate continuous video stream analysis, ensuring fall events are detected immediately as they occur—without lag.
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Alert System:
A responsive, user-friendly notification system was implemented to alert caregivers in real-time via sound, text, or visual indicators—enabling immediate action and enhancing peace of mind for families and care facilities.
Results
The AI-based fall detection system delivered impressive outcomes in safety and responsiveness. It achieved 90% accuracy in detecting falls, significantly reducing false alarms and missed incidents. Emergency response times improved by 30%, enabling caregivers to act more swiftly and prevent further injury. The system was well-received by both facility staff and families, offering a greater sense of security and trust. Its non-intrusive, camera-based setup ensured ease of adoption, making it a valuable addition to elderly care environments focused on proactive and responsive care.