Detection of Abnormal Sensor Patterns in Eldercare

Abstract- Tn TigerPlace, an aging in place facility from Columbia, MO, we deployed 47 sensor networks together with an electronic health record (EHR) system to provide early illness recognition. In this paper, we describe a framework for predicting abnormal health patterns using non-wearable sensor sequence similarity. To compute the similarity between two sensor patterns we employ the Temporal Smith Waterman. A sensor pattern is classified as "abnormal " ifit is much smaller than the mean of the distribution of "normal " patterns similarities. "Abnormal " days are defined by unusual sensor activity patterns that require a nurse's assessment of the resident. On a pilot data set of 1685 sensor days and 626 nursing records, we obtained a classification performance with an average precision of o. 70 and a recall o