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

2025-04-24

AI Summary

Existing gait analysis tools suffer from narrow population coverage, fragmented functionality, and closed-source limitations, hindering clinical adoption and research advancement. To address these challenges, we propose the first open-source, vision-based gait analysis framework designed for diverse populations—including older adults, children, athletes, and patients with neurological or orthopedic conditions. Our framework uniquely integrates Jupyter computational notebooks with a modular Python library, enabling end-to-end processing: raw video input, pose estimation, spatiotemporal parameter extraction, disease progression modeling, and cross-population interpretable visualization. Validated by clinical experts, it significantly lowers usability barriers. The framework is publicly released under an open license and has been deployed in real-world rehabilitation settings for longitudinal gait monitoring and therapeutic outcome evaluation. This work establishes a通用, transparent, and extensible technical foundation for gait research and precision rehabilitation.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Humans and AI: AI for Accessibility
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Consent frameworks and practices on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Analyzing gait disorders in diverse populations for mobility improvement
• Addressing limitations of existing gait analysis tools and accessibility
• Developing open-access framework for gait data processing and visualization

Innovation

• Open-access visual analytics framework for gait analysis
• Integrated computational notebooks and Python library
• Supports disease progression and patient group comparison