  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.
