Abstract
In this paper, we present a methodology for analyzing passive infrared motion sensor data logged in the homes of seniors. The objective is to capture activity patterns that represent different health conditions. Recognizing changes in the activity patterns can then be used to provide early detection of health changes. A visualization of motion sensor data is introduced in the form of a density map that uses different colors to show varying levels of activity. For evaluating the activity density level accurately, time away from home is determined first using a system of fuzzy rules. In addition, a dissimilarity between two density maps is computed using texture features for automatically determining changes in activity patterns, which may indicate a health problem. The activity density maps are being used in an aging in place senior housing community to aid clinicians in early illness detection. Three case studies of elderly residents are included to illustrate how the density map and dissimilarity measure can be used to track general activity level and daily patterns over time, showing changes in physical, cognitive, and mental health.
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