VIGMA: An Open-Access Framework for Visual Gait and Motion Analytics

TL;DR

VIGMA addresses the need for open, end-to-end gait analytics by integrating a Python data-processing library with a visual analytics frontend designed for multivariate time-series gait data. The framework supports data standardization, feature extraction, missing-value imputation, and normalization, and links to raw trial videos for validation. Its visualization module offers four time-series ensemble views, a spatiotemporal radar summary, and dual-box distribution views with cross-view interactions and video synchronization to support disease progression tracking and group comparisons. The authors validate VIGMA via three usage scenarios with gait experts and provide qualitative and quantitative feedback, demonstrating usability and the potential to foster cross-lab collaboration. Limitations include a single-lab dataset and scope to stroke rehabilitation, with future work aiming to broaden cohorts, enhance notebooks with widgets, and expand training materials.

Abstract

Gait disorders are commonly observed in older adults, who frequently experience various issues related to walking. Additionally, researchers and clinicians extensively investigate mobility related to gait in typically and atypically developing children, athletes, and individuals with orthopedic and neurological disorders. Effective gait analysis enables the understanding of the causal mechanisms of mobility and balance control of patients, the development of tailored treatment plans to improve mobility, the reduction of fall risk, and the tracking of rehabilitation progress. However, analyzing gait data is a complex task due to the multivariate nature of the data, the large volume of information to be interpreted, and the technical skills required. Existing tools for gait analysis are often limited to specific patient groups (e.g., cerebral palsy), only handle a specific subset of tasks in the entire workflow, and are not openly accessible. To address these shortcomings, we conducted a requirements assessment with gait practitioners (e.g., researchers, clinicians) via surveys and identified key components of the workflow, including (1) data processing and (2) data analysis and visualization. Based on the findings, we designed VIGMA, an open-access visual analytics framework integrated with computational notebooks and a Python library, to meet the identified requirements. Notably, the framework supports analytical capabilities for assessing disease progression and for comparing multiple patient groups. We validated the framework through usage scenarios with experts specializing in gait and mobility rehabilitation. VIGMA is available at https://github.com/komar41/VIGMA.

Figures (12)

• Figure 1: VIGMA's components. Using Jupyter Notebook and VIGMA's library, users begin by harmonizing collected data to standard CSV format. Afterwards, they perform essential feature extraction steps (e.g., extracting joint angles from motion data, extracting step times from ground reaction forces). Next, they prepare the data by performing tasks like imputing missing values and filtering out noise. Finally, users save the processed data and upload it to the visual analytics system, categorized by patient group. The visual analytics system consists of a control panel that lets users select ensemble data files and select chart configurations. The system is composed of four time-series ensemble views - to support the analysis of time-series gait data (e.g., joint angles). Additionally, it features a spatiotemporal summary view and a spatiotemporal distribution view to support single-valued gait parameter analysis. Users can choose to alternate between the spatiotemporal views (, ) and the video exploration view (Fig. \ref{['fig:video-exploration-view']}) using the checkbox from the control panel and get access to raw trial videos. to highlight interactive details-on-demand features of the system. Users can identify anomalies (e.g., ), trace errors to patients' trial videos, and return to the computational notebook to correct the data.
• Figure 2: Data collection process yang2013generalizationpai2014perturbationwang2019canwang2022near. A 7-meter walkway with embedded low-friction movable platforms is used to collect slip and walking data. The platforms move along low-friction aluminum tracks beneath the surface and rest on four force plates. The force plates record ground reaction forces, which also trigger the platforms to create the slipping effect. The platforms lock firmly during regular walking and unlock electromechanically without the participants’ awareness in the slip trial. Participants are protected by a safety harness connected to a low-friction trolley-and-beam system above the walkway. Kinematic data from all trials is recorded by an eight-camera motion capture system, synchronized with the force plates and load cell data.
• Figure 3: Overview of the workflow. The framework comprises two main components: computational notebooks and visualization frontend. The notebooks facilitate processing gait data (e.g., motion data) by performing standardization steps (e.g., feature extraction, noise filtering) before sending it to the visualization frontend. Users can interact with the frontend to select ensembles of trials and generate charts for time-series and spatiotemporal gait data using time-series ensemble view, spatiotemporal summary view, and spatiotemporal distribution view and have access to raw video data through video exploration view. If anomalies are identified in the data, users can return to the notebooks to correct the data and then visualize the corrected data in the frontend.
• Figure 4: Accessing data through the control panel of the visualization frontend with each trial hierarchically organized by patient ID and patient group.
• Figure 5: Time-series ensemble view. Displays the ensemble mean (green and orange solid lines) along with - individual trials' data (faded dashed lines) or the confidence interval - for two different groups (i.e., stroke patients and healthy controls). Users can filter and highlight a trial in by clicking on a faded dashed line, which will then appear as a bold dashed line within its view, other time-series ensemble views (e.g., ), and the spatiotemporal summary view (Fig. \ref{['fig:spatiotemporal-summary-view']}). The filtered trials are also highlighted in the spatiotemporal distribution view (Fig. \ref{['fig:spatiotemporal-distribution-view']}) with rectangular brushes, indicating their range of values for each single-valued spatiotemporal parameter. Conversely, adjusting the rectangular brushing on any box plot in the spatiotemporal distribution view will highlight a different set of trials in the time series ensemble views (, ) and spatiotemporal summary view (Fig. \ref{['fig:spatiotemporal-summary-view']}).