Discovering the Chances of Health Problems and Falls in the Elderly Using Data Mining

Abstract

The basis for this work was an invited paper at the International Joint Conference on Artificial Intelligence (IJCAI) workshop on Chance Discovery in 2011 and an additional invitation to submit an extended version of a paper to this book. Here we present a generalized approach to the detection of the chances of health problems and falls in the elderly for the purpose of prolonging their autonomous living using a novel data-mining approach. The movement of the user is captured with a motion-capture system that consists of body-worn tags, whose coordinates are acquired by sensors located in an apartment. The output time series of the coordinates are modeled with the proposed data-mining approach in order to recognize the specific health problem or fall. The approach is general in the sense that it uses a k-nearest-neighbor algorithm and dynamic time warping with the time series of all the measurable joint angles for the attributes instead of a more specific approach with medically defined attributes. It is a two-step approach: in the first step it classifies the person’s activities into five activities, including different types of falls. In the second step it classifies classified walking instances from the first step into five different health states: one healthy and four unhealthy. Even though the new approach is more general and can be used to differentiate other types of activities or health problems, it achieves very high classification accuracies, similar to the more specific approaches described in the literature.