Elderly Wellness Companion With Voice and Video-Based Health Anomaly Detection

The elderly healthcare requires an innovative approach to address multifaceted challenges in tracking, monitoring, and reporting in real-time. The proposed solution harnesses the capabilities of voice and video-based anomaly detection systems to offer continuous monitoring, personalized support, and timely intervention for the physical and emotional well-being of elderly individuals. Central to the proposed system is the integration of real-time voice emotion recognition and video-based posture recognition modules, constructed using cutting-edge deep learning and transfer learning models respectively. These modules are deployed on the Raspberry Pi platform, ensuring accessibility and efficiency. Moreover, attention mechanisms are incorporated to boost accuracy and effectiveness in detecting health anomalies, with a particular focus on identifying falls. The proposed elderly companion system implemented on Raspberry Pi achieves a validation accuracy of 96.34% in voice module and 87.91% in video module in delivering comprehensive healthcare for the elderly. The proposed solution demonstrates a potential work for standardization through the ITU/WHO Focus Group on Al for Health (FG-AI4H).