Intelligent elderly-care prototype for fall and disease detection

Abstract

Background: The number of elderly people in need of help with the activities of daily living in the EU is rapidly increasing, while the number of young workers is decreasing. Elderly care will, therefore, also have to be provided by intelligent computer systems. Methods: A prototype elderly-care system, developed at the Jozef Stefan Institute, mostly as part of the Confidence project, is presented. The prototype detects falls and behavior changes in the elderly. It learns from experience and is based on intelligent interpretation of movement patterns. Three sets of tests were performed to evaluate its properties on various subjects when engaged in normal activities, falling and imitations of several health problems under medical supervision. The key novelty was in locationbased sensors and advanced intelligent methods. Results: The prototype using the Ubisense sensor system, which detects the locations of tags worn on the body, correctly recognized 96 % of falls, significantly outperforming simple accelerometer- based systems. In addition, it recognized up to 99 % of abnormal behavior. Conclusions: Experimental results showed that an intelligent system coupled with advanced location sensors can achieve the level of performance needed in real life. The system offers significantly better performance than commercially available solutions, and once the price of sensors decreases, its widespread application seems likely.