FuryExplorer: visual-interactive exploration of horse motion capture data

Wilhelm, Nils; Vögele, Anna; Zsoldos, Rebeka R.; Licka, Theresia; Krüger, Björn; Bernard, Jürgen

doi:10.1117/12.2080001

Cited by 18 publications

(12 citation statements)

References 40 publications

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“…Dimensionality reduction builds the basis for a shared‐space variant with the principle to map high‐dimensional data to a 2D path of time stamps [HWX∗10, BWS∗12, BSH∗16]. The technique scales for many dimensions and time stamps, and allows the visual exploration, comparison, and summarization of complex phenomena such as anomalies [BWS∗12], frequent patterns [BWK∗13], and periodicities [WVZ∗15]. Drawbacks of dimensionality reduction are the introduction of errors caused by the mapping, overplotting of similar time stamps, and axes which cannot be interpreted easily [SZS ∗16].…”

Section: Related Workmentioning

confidence: 99%

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Bernard

Hutter

Reinemuth

et al. 2019

Computer Graphics Forum

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Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre‐processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre‐processing pipelines, human‐in‐the‐loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in‐depth research in visual analytics. We present a visual‐interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre‐processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty‐aware pre‐processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre‐processing in general and for uncertainty‐aware pre‐processing of multivariate time series in particular.

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Section: Related Workmentioning

confidence: 99%

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Bernard

Hutter

Reinemuth

et al. 2019

Computer Graphics Forum

Self Cite

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“…Other techniques visualize human body configurations, allowing the analysis motion over time, e.g., to assess health recovery. Along these lines, the FuryExplorer approach [38] uses a stick figure metaphor for the assessment of health recovery. Similar to our static dashboard, the visualizations can represent multiple objects, conveying a notion of the variance.…”

Section: Visualization Of Multivariate Attribute Setsmentioning

confidence: 99%

Using Dashboard Networks to Visualize Multiple Patient Histories: A Design Study on Post-Operative Prostate Cancer

Bernard

Sessler

Kohlhammer

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.

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“…To make these multivariate time-series data accessible to clinicians, their VA solution allows hierarchical clustering and navigation in time. The VA system 'FuryExplorer' [21] improves analytical workflows for evaluation of horse motion by interactive exploration of captured multivariate time-oriented data. Visual Analytics for Multivariate Time Series: The analysis of time-oriented data is an important problem for many other domains beyond movement data.…”

Section: Related Workmentioning

confidence: 99%

KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis

Wagner

Slijepčević

Horsak

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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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.

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FuryExplorer: visual-interactive exploration of horse motion capture data

Cited by 18 publications

References 40 publications

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Using Dashboard Networks to Visualize Multiple Patient Histories: A Design Study on Post-Operative Prostate Cancer

KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis

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