Development of a new low-cost computer vision system for human gait analysis: A case study.

Area of Science:

• Biomechanics
• Computer Vision
• Biomedical Engineering

Background:

• Human gait analysis is crucial for clinical diagnosis, rehabilitation, and sports performance.
• Existing motion capture systems (optoelectronic sensors, IMUs, depth cameras) are often expensive and lack detailed design guidelines.
• There is a need for cost-effective and accessible gait analysis solutions.

Purpose of the Study:

• To develop and propose a novel computer vision-based system (CVS) for human gait analysis.
• To address the gap in literature regarding the design, algorithms, and methodologies for gait analysis systems.
• To provide a precise, accurate, and low-cost solution for measuring gait metrics.

Main Methods:

• Developed a computer vision-based system (CVS) for gait analysis.
• Employed a linear computer vision method utilizing the non-homogeneous solution of the calibration matrix.
• Implemented and calculated spatio-temporal and angular gait parameters.
• Incorporated strategies for denoising spatial gait trajectories and detecting gait events.

Main Results:

• The proposed CVS demonstrated satisfactory precision and accuracy in human gait analysis.
• The system achieved acceptable computational performance.
• The developed system offers a low-cost alternative to commercial motion capture solutions.

Conclusions:

• The developed computer vision-based system provides a precise, accurate, and cost-effective solution for human gait analysis.
• This system can bridge the accessibility gap for low-income institutions requiring gait analysis capabilities.
• The study contributes valuable insights into the design and implementation of gait analysis systems using computer vision.