Automated Vision-Based Wellness Analysis for Elderly Care Centers
This addresses the need for improved efficiency and quality of healthcare for the aging population in elderly care centers, though it appears incremental as it builds on existing vision and deep learning methods.
The study developed an automated vision-based system to monitor and analyze the physical and mental well-being of senior citizens in care centers, using deep neural networks to extract features from video data and assist caregivers with insights and wellness metrics.
The growth in the aging population requires caregivers to improve both efficiency and quality of healthcare. In this study, we develop an automatic, vision-based system for monitoring and analyzing the physical and mental well-being of senior citizens. Through collaboration with Haven of Hope Christian Service, we collect video recording data in the care center with surveillance cameras. We then process and extract personalized facial, activity, and interaction features from the video data using deep neural networks. This integrated health information systems can assist caregivers to gain better insights into the seniors they are taking care of. These insights, including wellness metrics and long-term health patterns of senior citizens, can help caregivers update their caregiving strategies. We report the findings of our analysis and evaluate the system quantitatively. We also summarize technical challenges and additional functionalities and technologies needed for offering a comprehensive system.