Abstract

Falls are the most common type of home accidents among elderly people and are a major threat to their health and independence. Evaluating the risk of falling is important because it enables the provision of adapted assistance and of taking preventive measures with subjects deemed at risk of falling. Currently, the risk of falling has been evaluated by using questionnaires with their associated problems of subjectivity and limited accuracy in recall. The Kohonen Self-Organizing Map (SOM) has found applicability in a wide range of application areas. Our research as a whole has a final objective to employ the concept SOM to implement an adaptive fall risk detection and warning system. In this paper, we present the preliminary results from our research to utilize SOM to analyze the motion parameters from a miniature sensor with integrated gyroscopes and accelerometers attached to the chest of an individual. The results clearly indicate that SOM can be successfully used to cluster the activities by means of their motion parameters. This is very promising results to extend the concept to implement our final objective system.