Dr. Skubic will describe ongoing interdisciplinary research investigating the use of in-home sensor technology and machine learning to detect early signs of illness and functional decline, as a strategy towards proactively managing chronic health conditions. The sensor network includes a variety of sensors such as passive infrared motion sensors, a stove sensor, and a bed sensor that captures pulse and respiration. The network is being tested in TigerPlace, an aging in place facility in Columbia, MO, designed to help residents manage illness and impairments and stay as healthy and independent as possible. Nearly 50 sensor networks have been installed in TigerPlace since Fall, 2005, with an average installation time of 2 years. Automated health change alerts are sent to the clinical staff, based on recognized changes in the sensor data patterns. In addition, fall detection and gait analysis systems are being developed using vision, radar, acoustic arrays, and the Microsoft Kinect depth camera. Gait analysis systems have been installed in 10 TigerPlace apartments and are continuously capturing gait through passive observation of residents as they move about the home in their normal activities. The team has recently started installing systems in senior housing in Cedar Falls, Iowa, with motion, bed, and Kinect sensors to test health change alerts and remote care coordination. The talk will include case studies from several senior apartments.