Human Fall Detection: Detects elderly falls using pose estimation
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: We successfully identified specific pose changes that are indicative of fall events, enhancing the model's accuracy in detecting falls.
- Environmental Sensitivity: The model showed high sensitivity to variations in different environments and lighting conditions, improving its adaptability and real-time processing capabilities.
- Improved Response Time: The system significantly reduced response times, offering faster alerts and intervention compared to traditional fall detection methods.
Implemented Solution
Human fall detection was developed To address these challenges, we implemented the following solutions:
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Pose Feature Extraction:
Using MediaPipe, we extracted pose features and trained a machine learning model on a custom dataset tailored for fall detection.
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Real-Time Video Processing:
We integrated OpenCV to enable real-time video analysis, ensuring prompt detection of fall events.
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Alert System:
A user-friendly notification system was developed to instantly alert caregivers when a fall is detected, allowing for swift response.
Results
The implementation of this fall detection system achieved 90% accuracy in identifying falls, which significantly enhanced monitoring capabilities. The solution reduced emergency response times by 30% compared to traditional methods, providing caregivers and families with greater peace of mind through timely and reliable fall detection.