Optimizing Fall Detection System as an Early Warning System for the Elderly to Enhance Quality of Life

Abstract

Purpose: The main objective of this research is to develop a fall detection system and improve rapid emergency response or early warning systems for falls in the elderly.

Methods: In this research, the waterfall method was used for image analysis to detect falls with high accuracy. We also used Raspberry Pi 3, and OpenCV3 to set up a server to receive fall detection alerts and forward them to email.

Results: This system integrated a camera mounted on a Raspberry Pi 3 to continuously monitor the area captured by the camera. In the fall detection system, the results of testing with data showed that the system accuracy was 72.22%, sensitivity 72.72%, and error 27.77%.

Novelty: The approach this research adopted can be used in a variety of settings, including home healthcare, elderly care facilities, or places that require safety monitoring. With this system, we hope to improve rapid response in emergency situations, thereby protecting and improving the quality of life for people in need.