KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis

Abstract

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.

Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

“…Evidence-based clinical practice guidelines are sets of statements and recommendations used to improve health care by providing a trustworthy comparison of treatment options in terms of risks and benefits according to patient's status. The KAVAGait [45] tool is a knowledge-assisted VA environment for clinical gait analysis that supports analysts during diagnosis and clinical decision making. Users can load, visualize and compare patient gait data containing ground reaction forces (GRF) measurements gaining new knowledge, identify unseen pattern and recognize connections.…”

“…The following desiderata for a structural model of explicit domain knowledge were established in the reflective discussions of two recent design study projects, in which our collaboration with domain experts resulted in a VA environment [2,44,45] and preceding design study work that only sketched the integration of explicit knowledge [1,13,33]. Additional inputs result from analysis of the scientific work cited above.…”

“…Next, we demonstrate how the explicit knowledge of an exemplary VA environment can be structured according to our framework. This scenario is based on the KAVAGait [45] design study with physiotherapists and illustrated using the processes of the knowledge-assisted VA model [15,43].…”

“…KAVAGait [45] is a 'Knowledge-assisted VA System for Clinical Gait Analysis', whereby the analysts (clinicians) are supported during analysis and clinical decision making (see Fig. 4).…”

“…3). These desiderata originate from reflective discussions of three design studies that resulted in knowledge-assisted VA environments for different application domains (internal medicine [33], gait rehabilitation [45], and IT security [44]) and the review of the scientific literature, which is summarized in Sect. 2.…”