This paper proposes a model for early detection of health decline in seniors using personalized normals and continuous in-home monitoring with embedded sensors. Sensors embedded in senior housing apartments unobtrusively capture behavior and activity patterns. Changes in patterns are detected and analyzed as potential signs of changing health. Results in 21 seniors over nine months show similar performance for two techniques, where one approach uses only domain knowledge and the second uses supervised learning for training. We propose a health change detection model based on these results and clinical expertise that recognizes very early signs of health decline passively, without requiring the user to wear anything, charge batteries, or even notice the sensors. Identifying health decline early provides a window of opportunity for early treatment and intervention that can address health problems before they become catastrophic. This offers the potential for improved health outcomes, reduced healthcare costs, continued independence, and better quality of life.
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