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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

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Automated Video Quality Assessment for the Edinburgh Visual Gait Score (EVGS).

Rajkumar Arumugam Jeeva¹, Edward D Lemaire², Ramiro Olleac³

¹Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada.

Methods and Protocols

|July 23, 2025

Summary

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This summary is machine-generated.

This study introduces an automated video quality assessment framework for clinical gait analysis. The system ensures high-quality videos for accurate Edinburgh Visual Gait Score (EVGS) scoring, streamlining clinical workflows.

Area of Science:

Biomedical Engineering
Computer Vision
Clinical Biomechanics

Background:

Clinical gait analysis is crucial for diagnosing and monitoring conditions.
Manual video quality assessment for gait scoring is time-consuming and subjective.
Automating video quality control is essential for reliable and efficient gait analysis.

Purpose of the Study:

To develop an automated framework for assessing video quality in clinical gait analysis.
To support the Edinburgh Visual Gait Score (EVGS) by ensuring input video suitability.
To enhance the efficiency and reliability of gait analysis workflows.

Main Methods:

Utilized the MoveNet Lightning model for pose estimation and keypoint extraction.
Implemented algorithms for detecting multiple persons, tracking, plane orientation, overlaps, zoom artifacts, and resolution.

Keywords:

edinburgh visual gait score gait analysis pose estimation random forest video quality assessment

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Last Updated: Sep 14, 2025

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Integrated components into a random forest classifier for unified quality assessment.

Main Results:

Achieved 96% accuracy in detecting multiple persons and 95% in assessing overlaps.
Reached 92% accuracy in identifying zoom events, with an overall video quality categorization accuracy of 95%.
Demonstrated capability for automated video selection and provision of improvement suggestions.

Conclusions:

The automated framework significantly improves video selection for gait analysis.
The system reduces the need for manual quality checks in clinical settings.
Enables automated EVGS scoring by guaranteeing appropriate video input quality.