Voice and Video-Based Health Anomaly Detection in Elderly Healthcare

Elderly healthcare needs to provide solutions to the complex challenges of real-time tracking, monitoring, and health anomaly detection. The proposed system integrates voice and video-based health anomaly detection to provide continuous monitoring and personalized care to elderly individuals in a home setting. It includes emotion recognition in voice and video-based posture identification. The voice module is developed using convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models, while a transfer learning technique is used in the implementation of the video module. The system is implemented on the Raspberry Pi platform to ensure a cost-effective solution. Attention mechanisms are incorporated into machine learning models to enhance accuracy, particularly for fall detection. Validation experiments with the Raspberry Pi implementation achieved an accuracy of 96.34% for the voice module and 87.91% for the video module on standard datasets for elderly care. This method aligns with the ITU standardization efforts.