In this course, students will learn about AI with a focus on building and evaluating machine learning models applied in patient data and personalized rehabilitation. Beyond performance we will emphasize fairness of AI in healthcare through collaborative projects, directly offered from leading healthcare companies with real-world rehabilitation and healthcare challenges.

This course integrates clinical foundations of motor neurorehabilitation with evidence-based practice and innovation. Students analyse motor symptoms, apply structured clinical reasoning to cases, and critically appraise guidelines and their limitations. They also develop and justify evidence-informed concepts for future rehabilitation within a continuum-of-care perspective.

“Rehabilitation” is the (re)integration of an individual with a disability into society. Rehabilitation engineering is “the application of science and technology to ameliorate the handicaps of individuals with disability”. Such handicaps can be classified into motor, sensor, and cognitive disabilities. In general, one can distinguish orthotic and prosthetic methods to overcome these disabilities.

This course provides an in-depth view on the future of (neuro)rehabilitation with a strong focus on place-independent rehabilitation strategies and behavioral adaptations. Current clinical concepts will be introduced and their translation into telerehabilitation investigated.