A new design of a fall detection system integrating landmark identification and deep learning techniques

Abstract

This article introduces an innovative system that integrates landmark identification with deep learning to enhance fall detection accuracy and reliability. By utilising advanced computer vision techniques, such as MediaPipe for spatial recognition, the system effectively differentiates between routine movements and actual falls. The integration of landmarks with a deep learning prediction algorithm minimises false alarms, ensuring timely responses to genuine falls. Comprehensive experimentation underscores the system’s versatility across various scenarios, emphasising its potential to improve safety and independence for older adults. The training process demonstrates a steady increase in accuracy, stabilising by the 40th cycle, while error rates decline significantly during the initial cycles. Real-time experiments, involving both male and female participants aged 8 to 50, recorded a remarkable 95% detection rate of falls, showcasing the system’s effectiveness and promising future applications in elder care and smart health monitoring environments.