Computer Vision Algorithms and Smart Home Devices Revolutionizing Real-Time Mobility Tracking and Emergency Response for Elderly Independence

As global populations age, with over 2 billion individuals projected to be 60+ by 2050, innovative solutions are essential to promote elderly independence amid rising mobility challenges and fall risks. This paper introduces an advanced framework that synergizes state-of-the-art computer vision algorithms with accessible smart home devices such as Raspberry Pi cameras and IoT hubs for real-time mobility tracking and intelligent emergency response. Core components include YOLOv8 for robust object and human detection (mAP 0.92), MediaPipe for 33-keypoint pose estimation, and a spatio-temporal graph convolutional network (ST-GCN) for activity recognition, attaining 97% accuracy on elderly-specific datasets. Multi-camera fusion via homography and deep SORT ensures seamless tracking across home environments, while autoencoder-based anomaly detection flags gait irregularities and falls with 96.5% precision and under 200ms edge-computed latency. Privacy is safeguarded through on-device processing, face blurring, and federated learning. A prototype tested in a 1,200 sq ft apartment with 50 participants (aged 65+) demonstrated 40% faster emergency responses, 85% user-reported confidence gains, and superiority over Kinect-based systems in multi-room scalability and cost-efficiency. Challenges like lighting variability are addressed, with future work exploring multimodal sensor fusion and reinforcement learning for predictive care. This work pioneers non-intrusive, deployable technology to empower aging-in-place, bridging computer vision, IoT, and gerontechnology for societal impact.