Diploma and Master Theses (authored and supervised):
"BrainGait: Gait Event Detection and Visualization for Robotic Rehabilitation";
Supervisor: E. Gröller;
Institute of Visual Computing & Human-Centered Technology,
final examination: 2020-04-21.
Mobility impairment in adults is one of most prevalent types of disabilities in developed countries. Gait rehabilitation can be used to regain some or all motor functions, especially after a stroke. In recent years, robot-assisted gait training attracted increasing interest in rehabilitation facilities and scientific research. With this advent of robotic recovery comes the need to objectively measure the patientīs performance. Physiotherapists need essential information about the current status during training and how to improve the patientīs gait, presented in an easy to grasp and compact form. On the other hand, physicians rely on statistical measures in order to evaluate the patientīs progress throughout the therapy. This thesis discusses commonly used visualizations and statistics while proposing improvements and adaptations in the context of PerPedes, a novel robotic gait rehabilitation device. In order to measure the patientīs performance, a new algorithm for gait event detection was developed, based on force data from pressure plates. The following work demonstrates that standard algorithms fail with PerPedes, while the proposed solution can robustly handle highly distorted gait patterns, such as hemiplegic gait, foot drop, or walking backwards. The software application developed during this thesis provides feedback to the therapist and generates suggestions for gait improvement. Furthermore, gait statistics are inferred from each therapy session and collected in order to be used for future analysis and inter-patient comparison.
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.