Dataset and Methods for Recognizing Care Activities

A major challenge in stationary care in hospitals is the limited amount of time for each patient due to a large overhead being created by manual documentation efforts. Studies show that it is common for caregivers to spend more than one hour per day for documentation efforts. In this paper a novel concept for reducing the manual documentation effort by leveraging methods of human activity recognition is introduced and a corresponding dataset is published. The dataset captures different care activities like repositioning, sitting up, transfer and patient mobilization using body worn sensors in a realistic setting with multiple patients and caregivers. For evaluation of the data, two experimental setups are presented: an unsegmented case, where the duration of the care activity is unknown and a segmented case, where the beginning and the end of the activity is known beforehand. First experiments show the feasibility of recognizing care activities using different types of Neural Networks.