A central characteristic of autonomous people is the ability to move around independently. Walking significantly characterizes the mobility status of an individual and is highly associated with quality of life. Gait analysis therefore plays a crucial role in mobility diagnostics. Especially in neurological diseases such as Parkinson's disease, stroke, multiple sclerosis, spinal cord diseases and polyneuropathy, gait disturbances are an important marker of disease severity and progression. So far, biomechanical (e.g. gait speed, step-stride variability, gait width) and sensomotoric (e.g. dynamic balance) gait parameters are primarily collected in a scientific context by the use of hardware extensive laboratory settings.
Therefore, there is a need in neurological rehabilitation for accessible, observer-independent (objective) and easy-to-use technologies for valid and reliable quantification of disease-specific gait parameters to evaluate and optimize the therapy process.
So far, biomechanical (e.g. gait speed, step-stride variability, gait width) and sensomotoric (e.g. dynamic balance) gait parameters are primarily collected in a scientific context by the use of hardware extensive laboratory settings. The resulting data is often complex and inaccessible for clinicians and patients, but has the potential to provide feedback.
- Extract standard (e.g., cadence, step length, step height, …) to advanced (e.g., margin of stability) gait parameters
- Research novel gait parameters
- Provide visual assessment of singular gait tests
- Implement and visualize aggregated exercise data (development over time)
- Intermediate knowledge about data mining and information visualization
- Scope: Bachelor/Master
- 3 Month Project, 3 Thesis (Bachelor)/6 Month Thesis (Master)
- Start: immediately