Kaczmarek, SylviaSylviaKaczmarekWibbeling, SebastianSebastianWibbelingFiedler, MartinMartinFiedlerBongers, AndreasAndreasBongersGrzeszick, ReneReneGrzeszick2023-03-202023-03-202022-09-20https://publica.fraunhofer.de/handle/publica/437813A 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.enKünstliche IntelligenzMaschinelles LernenHuman-centered computingUbiquitous and mobile computingUbiquitous and mobile computing design and evaluationpresentation