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Gait Type Analysis Using Dynamic Bayesian Networks.

Patrick Kozlow1, Noor Abid2, Svetlana Yanushkevich3

  • 1Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada. pkozlow@ucalgary.ca.

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|October 6, 2018
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Summary

This study shows that recognizing antalgic gait using body movement data is a reliable biometric for forensic applications. Dynamic Bayesian networks achieved an 88.68% recognition rate for gait abnormality classification.

Keywords:
Microsoft Kinect sensorbiometricsdynamic Bayesian networkgaithuman identification

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Area of Science:

  • Biometrics
  • Computer Science
  • Forensic Science

Background:

  • Gait analysis is crucial for identifying individuals and detecting abnormalities.
  • Antalgic gait, characterized by pain-induced walking patterns, presents a unique classification challenge.
  • Biometric identification systems can be enhanced by incorporating gait analysis.

Purpose of the Study:

  • To investigate the potential of gait abnormality type identification, specifically antalgic gait.
  • To evaluate gait recognition as a biometric for forensic applications.
  • To develop and assess a classification model for gait types.

Main Methods:

  • Utilized Microsoft Kinect v2 to capture body joint coordinates.
  • Extracted gait features including cadence, stride length, and joint angles.
  • Employed dynamic Bayesian networks for gait type modeling and classification.

Main Results:

  • Achieved an 88.68% recognition rate for gait abnormality classification.
  • Demonstrated the viability of gait type as a biometric.
  • Compared the dynamic Bayesian network approach with other classification techniques.

Conclusions:

  • Dynamic Bayesian networks show significant potential for gait abnormality classification.
  • Gait recognition can serve as a valuable addition to existing biometric systems.
  • The proposed method offers a robust approach to identifying specific gait types.