Tomber
Link to open source: https://github.com/Kanavmh/FallDetection
Tomber - A Fall Detection System using OpenCV and MediaPipe
Falls are a leading cause of injury, especially among the elderly and individuals with mobility challenges. Delayed medical response can lead to severe health complications, prolonged recovery times, and even fatalities.
This project aims to develop a real-time, non-intrusive fall detection system using OpenCV and MediaPipe, leveraging computer vision to analyze human movements. By detecting falls accurately and triggering immediate alerts, this solution ensures faster emergency response, ultimately improving healthcare outcomes and personal safety.
– Utilizes MediaPipe to detect and track key body landmarks such as shoulders, elbows, and hips.
– Analyzes movement patterns and body orientation to accurately identify falls.
– Sends notifications to caregivers, emergency services, or designated contacts upon fall detection.
– Provides critical information to healthcare authorities to facilitate timely medical assistance.
This project has significant applications in elderly care, healthcare monitoring, and workplace safety, ensuring timely intervention and emergency response.
