L. Alvarez et al. (Eds.): CIARP 2012, LNCS 7441, pp. 463–471, 2012. © Springer-Verlag Berlin Heidelberg 2012 Recognition of Patterns of Health Problems and Falls in the Elderly Using Data Mining Bogdan Pogorelc 1,2,3 and Matjaž Gams 1,2,3 1 Jožef Stefan Institute, Department of Intelligent Systems, Ljubljana, Slovenia 2 Špica International d. o. o. 3 Jozef Stefan International Postgraduate School, Slovenia {bogdan.pogorelc,matjaz.gams}@ijs.si Abstract. We present a generalized data mining approach to the detection of health problems and falls in the elderly for the purpose of prolonging their au- tonomous living. The input for the data mining algorithm is the output of the motion-capture system. The approach is general since 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. Even though the presented approach is more general and can be used to differentiate other types of activities or health prob- lems, it achieves very high classification accuracies, similar to the more specific approaches described in the literature. Keywords: health problems, activities, falls, elderly, machine learning, data mining. 1 Introduction The number of elderly people in the developed countries is increasing [19], and they tend to lead isolated lives away from their offspring. In many cases they fear being unable to obtain help if they are injured or ill. In recent decades this fear has resulted in research attempts to find assistive technologies to make the living of elderly people easier and more independent. The aim of this study is to provide ambient assistive- living services to improve the quality of life of older adults living at home. We propose a generalized approach to an intelligent care system to recognize a few of the most common and important health problems in the elderly, which can be de- tected by observing and analyzing the characteristics of their movement. It is a two-step approach as shown in Figure 1. In the first step it classifies the per- son's activities into five activities, including two types of falls. These are: fall (F), unconscious fall (UF), walking (W), standing/sitting (SS), lying down/lying (L). In the second step it classifies classified walking instances from the first step into five different health states: one healthy (N) and four unhealthy. The types of abnormal health states are: hemiplegia (H), Parkinson’s disease (P), pain in the leg (L), pain in the back (B).