We offer master thesis topics at the intersection of rehabilitation engineering, computer
vision, AI, wheelchair mobility analysis, and accessibility-aware navigation.
1. Wheelchair Navigation and SLAM in Urban Environments
Pitch
How can a manual wheelchair navigate complex urban spaces in a robust and intelligent way?
This topic explores SLAM, visual localization, and scene understanding for real-world
wheelchair navigation. Methods
Computer vision, SLAM, visual odometry, mapping, multimodal sensing.
2. AI for Understanding User–Wheelchair Interaction
Pitch
This topic investigates how AI can model propulsion, posture, movement strategies, and userspecific
interaction patterns in manual wheelchair use.
Methods
Machine learning, pattern recognition, biomechanical data analysis, explainable AI.
3. Computer Vision for Rehabilitation Engineering
Pitch
This topic uses video analysis to better understand wheelchair use, functional activities, and
interaction with the environment in rehabilitation contexts.
Methods
Computer vision, pose estimation, action recognition, video analytics, movement analysis.
4. Accessibility-Aware Intelligent Navigation Systems
Pitch
This topic explores navigation systems that consider accessibility needs, not only distance or
time, for wheelchair users in urban environments.
Methods
AI/ML, route modelling, accessibility-aware decision making, sensor fusion, human-centred
design.