Evaluation of semi-automated care processes in long-term care using AI-based movement monitoring as an example.

In the ETAP project, we are developing and investigating AI solutions, as well as their application and possible relief effects for caregivers in everyday care. The focus is on automatic fall detection, fall risk assessments, and documentation of movements of residents in care facilities. These tasks are achieved by modern machine learning algorithms, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs) and Hidden Markov Models (HMMs), and integrated in a co-creation process into the long-term care daily routine.

The ETAP project website can be found here:


Persons in Charge: Yale HartmannDr. Hui Liu, and Prof. Dr. Tanja Schultz