PhD candidate with focus on analysis of movement sensor data for the project “Towards a tailored...

The personal and societal burden of chronic conditions such as stroke is enormous. At-home training will become common and technology can play an important supporting role in this. In line with past and ongoing research, we start with the 4-year project "Towards a tailored monitoring and training tool for at-home rehabilitation". The aim of this project is to develop knowledge and demonstrators that contribute to the future development and application of "tailor-made" at-home rehabilitation systems for all types of people. We will use upper-limb rehabilitation after stroke as showcase. The developed knowledge will facilitate the future transition from hospital to home using care supporting technology in the home context of the patient, and eventually contribute to more cost-effective stroke rehabilitation.

You will focus on the data-analytical part of the project, based on data from wearable sensor units such as accelerometers. This part aims at 1) the at-home assessment of patients' arm-hand skills in standardized conditions, and 2) giving feedback during daily life about the execution of arm-hand movements (such as reaching or grasping) that are tailored to the patient. Developing and applying innovative signal-analytical techniques (e.g., machine learning) based on wearable sensor data to assess and improve arm-hand movements are core topics of this part.