Alert System for Non-Responsive State of an Elderly Person

The prevalence of non-responsive states among elderly individuals, often associated with falls or medical emergencies, poses significant risks requiring immediate intervention. Addressing this challenge, this paper presents an intelligent system leveraging advanced computer vision, image processing, and machine learning technologies to detect such critical scenarios reliably. The proposed system integrates image segmentation for isolating relevant objects, feature extraction to discern crucial attributes like body posture, flow tracking to monitor motion patterns, and image classification algorithms to differentiate emergency situations from non-critical events. This comprehensive approach ensures robust accuracy and adaptability across diverse environments, accommodating varying lighting and spatial conditions. By enabling timely detection and alerts, the system empowers caregivers and emergency responders to intervene promptly, thereby enhancing the safety and well-being of elderly individuals and reducing the likelihood of adverse outcomes.