SafeFall is an advanced fall detection system designed to enhance patient safety in healthcare settings. Using OpenCV technology, SafeFall monitors hospital rooms to detect falls in real-time and alerts medical staff immediately, ensuring prompt response to patient needs.
- Fall Detection: Detects sudden patient falls from their bed using advanced image processing.
- Alert System: Instantly notifies the nearest medical professionals or nurses for immediate assistance.
- Increased Efficiency: Reduces the workload on nurses by automating patient monitoring and fall detection.
Each year, millions of older people—those 65 and older—fall. In fact, more than one out of four older people falls each year, but less than half tell their doctor. Falling once doubles your chances of falling again. SafeFall aims to address this critical issue by providing a reliable and efficient system to detect and alert falls in hospital environments.
The SafeFall demo showcases how our system can identify a simulated fall in a hospital room environment. Notice how the system swiftly recognizes the fall and issues alerts, demonstrating the potential of SafeFall to improve patient care and safety.
SafeFall uses a combination of camera input and OpenCV algorithms to monitor real-time video for unusual movements or falls. The system is designed to be highly adaptable and can be customized for different hospital settings and patient needs.
Interested in how SafeFall works under the hood? Check out our code:
- Abhisar Anand - LinkedIn | Github | Instagram
- Mohammad Akif - LinkedIn | Github | Instagram
- Prakhar Verma - LinkedIn | Github | Instagram
For more details about the project or queries, feel free to email us.