Early illness detection in elderly using sensor networks: A review of the TigerPlace experience

Many older adults in the US prefer to live independently for as long as they are able, despite the onset of conditions such as frailty and dementia. Elderly patients are particularly at-risk for late assessment of health changes due to factors such as their impression that such changes are simply a normal part of aging or their reluctance to admit to a problem. In-home sensors networks have emerged in the last ten years as a possible solution for early illness detection. Many projects have demonstrated the utility of inhome sensors for monitoring elderly but also have shown the necessity of developing new pattern recognition algorithms able to handle large amounts of diverse data. In TigerPlace, an aging in place facility from Columbia, MO, we created a unique living laboratory by deploying in-home sensors together with an electronic health record (EHR) system developed in-house that integrates clinical and sensor data. In-home monitoring devices such as infrared motion detectors, Kinect depth cameras, Doppler radars and bed sensors capture information related to the behavior of the residents from the monitored apartment and assist the clinical personnel in medical decision making. In this paper we present a review of our early illness detection (EID) and recognition (EIR) methodologies experimented in TigerPlace, together with results and lessons learned.