International Journal of Science, Engineering and TechnologyAuthors: Sahil Jagtap
Abstract: With the growing elderly population, ensuring their safety, especially for those living alone, has become a critical concern. This paper presents a real-time elderly monitoring system that leverages computer vision and deep learning to detect falls and hazardous objects such as knives and guns. The system integrates YOLOv8 for fall detection and object recognition, OpenCV for real-time video frame processing, and Pushbullet API for instant caregiver notifications. A Flask-based user interface allows caregivers to monitor live annotated video feeds remotely, offering a non-intrusive alternative to wearable sensors that require user compliance.The proposed system achieves 95% accuracy in fall detection and 98% accuracy in hazardous object recognition, with an average alert delay of under 3 seconds. This approach ensures rapid emergency response, enhances elderly safety, and minimizes caregiver burden. Despite challenges such as privacy concerns and reliance on internet connectivity, the system provides an effective, scalable, and adaptable solution for use in homes, care facilities, and hospitals. Future enhancements include multi-camera integration, AI-based anomaly detection, and wearable device support for health monitoring to further improve elderly care and safety.
