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In the context of Ambient Assisted Living (AAL), the detection of daily activities is an active field of research. In this study, we present an algorithm for the performed Activities of Daily Living (ADLs) related to personal hygiene, which is based on the evaluation of a personâs proximity to objects and pose information. To this end, we have employed a person detection algorithm that provides a personâs position within a room. By fusing the obtained position with the objectsâ position, we were able to deduce whether the person was occupied with a certain object and to draw conclusions about the performed ADLs. One prerequisite for a reliable modelling of human activities is the knowledge about the accuracy of the person detection algorithm. We have, therefore, analysed the algorithm with regard to its accuracy under different, application-specific conditions. The results show that the considered algorithm ensures high accuracy for our AAL application and that it is even suitable for environments, in which objects are very close to each other. On the basis of these findings, tests with video sequences have been conducted in an AAL environment. This evaluation confirmed that the reasoning algorithm can reliably recognise activities related to personal hygiene.

Activity Recognition based on High-Level Reasoning - An Experimental Study Evaluating Proximity to Objects and Pose Information