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Projektpraktikum Human-Centered Neuroengineering: Neurorehabilitation 
Summer semester 2021
Chair of Cognitive Systems (Prof. Cheng)
(Contact information)
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"The students build teams to work on scientific, technical and social problems with respect to neurorehabilitation. On the technical side, a special focus is put on how exoskeletons, brain-machine interfaces (with EEG and EMG) as well as virtual reality can be used for the neurorehabilitation of stroke patients. The students will learn how to acquire neural signals from humans through EEG and EMG, process and analyse the signals and then use them for exoskeleton control. For motivational purposes during the neurorehabilitation process, the exoskeleton movement is connected to custom 3D and VR games.
The social dimension of the course is concerned with how the structural environments (from rehabilitation clinics to home-based rehabilitation) and key stakeholders are already supporting and can further support the rehabilitation process. Furthermore, the course deals with the individual interaction of stroke patients with rehabilitation technology. The later should be developed in a human-centered fashion with consideration of the needs and desires of the patient, e.g. through personalized rehabilitation programs and gamification. In the spirit of human-centered engineering it is important that the students try to incorporate (ideally participatively) the perspective of stroke patients into their project work.

The course consists of four phases:
- Kick-off phase: The students receive introductory lectures on neurorehabilitation, exoskeletons, human-centered engineering, responsible neuroengineering and virtual reality in robotics & neuroengineering. Additionally, they learn about technical methods (EMG, EEG, exoskeletons, VR).
- Project finding phase: The students develop and present project plans based on the theoretical lectures, scientific literature and the inclusion of stroke patients.
- Iterative development phase: The students develop and implement algorithms und hardware systems. This development connected with a testing and validation phase through objective measures and the inclusion of the user perspective
- Reflection phase: The students present their results and write a report in which they describe their technical and scientific work, but also discuss the social and ethical aspects of their work. "
"The students should possess programming skills. Prior knowledge in machine learning and signal processing is helpful.
"At the end of this course, students are able to:
- understand and develop robotic methods for neurorehabilitation by means of exoskeletons and virtual reality
- record and process neural signals (EEG, EMG)
- apply and develop machine learning algorithms on neural data
- integrate sensory feedback mechanisms

Additionally, non-technical skills are learned. The students are able to:
- organize and lead a technological project
- work in small teams and communicate the team's progress
- include (ideally participatorily) the perspective of people with stroke in their research and development work
- apply methods of human-centered engineering and reflect neurotechnology with respect to social and ethical perspectives

"Following teaching methods are used:
- Introductory lectures
- group work with methods of human-centered engineering
- Independent student work (including work in the laboratory with the neurotechnologies)
- regular colloquia with lecturers
Für die Anmeldung zur Teilnahme müssen Sie sich in TUMonline als Studierende*r identifizieren.
"Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. Mikhail A. Lebedev, Miguel A. L. Nicolelis. Physiological reviews (2017)
- Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients. Donati et al. (2016)
- Human-Machine Symbiosis: The Foundations of Human-centred Systems Design. Editor: Karamjit S. Gill (1996)
- Virtual Reality in Medicine. Robert Riener, Matthias Harders (2012)
- Motor learning: It's relevance to stroke recovery and neurorehabilitation. John W. Krakauer (2006)
- Robot-aided neurorehabilitation of the upper extremities. Robert Riener et al. (2005)"