KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis
This addresses the problem of time-consuming data interpretation for clinicians in gait rehabilitation, but it is incremental as it builds on existing visual analytics methods for a specific domain.
The researchers tackled the challenge of interpreting complex gait analysis data in clinical settings by developing KAVAGait, a visual analytics system that incorporates interactive interfaces and an explicit knowledge store, with validation through expert reviews and user studies showing it supports clinicians in practice.
In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of human locomotion enables clinicians to describe and analyze a patient's gait performance in detail and allows them to base clinical decisions on objective data. These assessments generate a vast amount of complex data which need to be interpreted in a short time period. We conducted a design study in cooperation with gait analysis experts to develop a novel Knowledge-Assisted Visual Analytics solution for clinical Gait analysis (KAVAGait). KAVAGait allows the clinician to store and inspect complex data derived during clinical gait analysis. The system incorporates innovative and interactive visual interface concepts, which were developed based on the needs of clinicians. Additionally, an explicit knowledge store (EKS) allows externalization and storage of implicit knowledge from clinicians. It makes this information available for others, supporting the process of data inspection and clinical decision making. We validated our system by conducting expert reviews, a user study, and a case study. Results suggest that KAVAGait is able to support a clinician during clinical practice by visualizing complex gait data and providing knowledge of other clinicians.