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System for automatic gait analysis based on a single RGB-D camera.

Ana Patrícia Rocha1, Hugo Miguel Pereira Choupina2, Maria do Carmo Vilas-Boas2

  • 1Institute of Electronics and Informatics Engineering of Aveiro (IEETA), and Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal.

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Summary
This summary is machine-generated.

This study introduces an automatic system for human gait analysis using a Kinect RGB-D camera. The system accurately detects walking and analyzes gait cycles for applications in sports, healthcare, and biometrics.

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Area of Science:

  • Biomechanics
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Human gait analysis offers insights into walking patterns.
  • RGB-D cameras like Kinect provide markerless 3D body joint data.
  • Objective, affordable, and non-intrusive gait analysis is desirable.

Purpose of the Study:

  • To present a fully automatic system for human gait analysis using a single RGB-D camera.
  • To develop a system capable of recognizing walking activity and identifying gait cycles without manual intervention.
  • To compute key gait parameters for diverse applications.

Main Methods:

  • Utilized a Microsoft Kinect (second version) RGB-D camera for 3D joint data acquisition.
  • Developed a predictive model (multilayer perceptron) for activity and posture recognition (walking, standing, marching; facing/away from sensor).
  • Implemented a gait cycle detection algorithm using ankle distance and velocity to estimate heel strike instants.

Main Results:

  • The activity recognition model achieved 98% accuracy and F1 score.
  • Gait cycle detection demonstrated low error rates for heel strike (15 ± 25 ms) and stride duration (1 ± 29 ms) estimation.
  • The system operates automatically, requiring only data acquisition initiation/termination.

Conclusions:

  • The developed system enables fully automatic, markerless human gait analysis using an affordable RGB-D camera.
  • The system accurately recognizes walking and precisely detects gait cycles.
  • This technology has potential applications in sports, healthcare, and biometric identification.