Insufficient physical activity is the 4th leading risk factor for mortality. Methods for assessing the individual daily life activity (DLA) are of major interest in order to monitor the health status and to provide mandatory feedback about the individual quality of life. The assessment of DLAs with self-reports induces problems like reliability, validity, and sensitivity. In recent years, small and light-weight inertial sensors were used to provide objective measurements of physical activity. One major research field is the classification of DLAs, e.g. walking, washing dishes or climbing stairs.

We propose a hierarchical classifier structure that is flexible in the integration of new activities. Furthermore, the sensor fusion of accelerometer and gyroscope improves the distinction of activities like ascending/descending stairs.

Since it is difficult to compare newly proposed methods to existing approaches, we provide an publicly available dataset (www.activitynet.org). The hierarchical, multi-sensor based classification system was compared to state-of-the-art algorithms using a benchmark dataset.