Extracting information from the sensors installed in the homes of elderly pose a unique set of challenges. Add to it the short amount of time the clinicians and nurses have to analyze this data, and the problem becomes more complicated. The ongoing work in this project focuses on development of algorithms to glean information from in-home sensor data and then presenting it in the form of textual summaries using Natural Language Generation techniques.
We are developing an application to automate data collection for the physical function assessments that are normally measured manually by physical therapists or nurses. The assessment tool will be used every 6 months to assess older adults’ functional movement and range of motion as part of the Health Kiosk project.
In this randomized controlled study, we are investigating and refining health alerts produced by environmentally-embedded in-home sensor networks designed to detect early signs of health change and functional decline in older adults, the keys to successful intervention.