3. A computer vision approach for innovating fall detection

Tóm tắt

As the population ages, the need for effective safety monitoring systems increases, especially for vulnerable groups such as the elderly. This paper introduces a novel fall detection system that utilizes advanced computer vision techniques, including feature point decomposition and a lightweight GRU model, to deliver real-time monitoring and interventions during falls. The system achieves frame processing times between 0.02 and 0.11 seconds, with response times consistently under 0.09 seconds. The proposed system achieves 95% accuracy in distinguishing between normal activities and falls, significantly reducing false positives through a robust algorithm and careful preprocessing. By integrating intelligent systems into everyday environments, our approach demonstrates the effectiveness of feature extraction methods and reveals significant potential for healthcare applications. These findings underscore the system's ability to enhance safety and well-being, suggesting future improvements through machine learning models that can optimize detection over time. This research promises to revolutionize automated healthcare monitoring, fostering greater independence and quality of life for at-risk individuals. We hope our work inspires further advancements in this vital area, contributing to a comprehensive suite of safety solutions for aging populations worldwide.