@article{186,
  keywords = {aging in place, computer vision, emergency, health monitoring},
  author = {Tony Tam and Alf Dolan and Jennifer Boger and Alex Mihailidis},
  title = {An Intelligent Emergency Response System: Preliminary Development and Testing of a Functional Health Monitoring System},
  abstract = {<p>Changes in a person’s routine of daily activities can signal a change in health. To support the growing elderly popu-lation who want to age-in-place, techniques and algorithms have been developed to build a system that monitors functional health in the home environment. This health monitoring system has been developed with machine vision and pattern analysis components to track the occupant, learn his/her pattern of activity, and detect significant deviations that could indicate a change in health status. The ef-fectiveness of the health monitoring system was investigated with a pilot study capturing video footage of a 28-day simulation including 21 days of normal activ-ity and seven days of abnormal scenarios. The system was effective in learning an occupant’s pattern of activity and detecting deviations that were indicative of changes in the occupant’s functional health status. Overall, the results indicate that a health monitoring system could be developed that uses machine vision and basic artificial intelligence with promising potential to support aging-in-place.</p>
},
  year = {2006},
  journal = {Gerontechnology},
  volume = {4},
  chapter = {209},
  url = {https://journals.sagepub.com/doi/10.1258/1357633054068946},
  doi = {doi.org/10.4017/gt.2006.04.04.005.00},
}