The ability to walk and stair-climb is an essential motor function and a prerequisite to participate in activities of daily living. Hence, gait rehabilitation is a crucial issue for clinicians due to the severe associated health and socio-economic implications. Next to pure visual inspection, gait analysis tools allow the clinical expert in gait rehabilitation to describe and analyze a patient's gait performance and consequently base clinical decision making on objective data. The systems used for this purpose range from simple video cameras and force-distribution sensing walk ways to highly sophisticated motion capture systems. Clinics with a very high daily patient throughput often rely on the combination of force plates and cost-effective two-dimensional gait analysis tools to determine external forces applied to the ground (ground reaction force, GRF) during the stance phase in gait as well as gait kinematics (e.g. joint angles).
Even though necessary and highly important for clinical observations, these assessments generate a vast amount of data, which need to be interpreted by a clinician in a short period of time. Automated data analysis methods may bear the potential to support the clinician during this challenging process. Still, it is a difficult task to interpret the obtained data as several parameters are inter-linked and a lot of domain expertise is necessary. This combination of a large amount of inter-linked clinical data derived during a clinical examination with the aforementioned systems, the need for sophisticated data analysis methods, and clinical decision making requiring the judgment and expertise of clinicians lends itself very well to the notion of visual analytics (VA).
Design & implement a visualization system that provides visual overview of ground reaction forces for many patients, to allow summary and comparisin of patient cohorts. For this, dense visual metaphors like horizon graph or braided graph can be used in combination with domain-specific abstractions.
Introductory reading can be provided to the student.
We have access to domain experts and anonymized patient data.
Secondary supervisor: Alexander Rind