A robust covariate-invariant gait recognition based on pose features
Biometrics · Gait Recognition · Deep Learning · Pose Estimation
Academic research reference · 2022
A robust covariate-invariant gait recognition based on pose features
IET Biometrics · Wiley · IET
Biometrics · Gait Recognition · Deep Learning · Pose Estimation
Publication record
- Venue
- IET Biometrics
- Publisher
- Wiley · IET
- Published
- Published 20 October 2022 · Vol. 11, pp. 601–613
- Research area
- Biometrics · Gait Recognition · Deep Learning · Pose Estimation
Research summary
Research on pose features for gait recognition under covariate variation, connecting human pose estimation with movement biometrics.
Research topics
Research keywords
Research reference
Research → capability → product
This publication informs a research foundation. Connections below describe GaitAI's documented architecture; they do not establish product accuracy, clinical validation or regulatory clearance.
Pose-based gait analysis
See this paper in GaitScape →Modules directly informed at capability level
Camera-based gait assessment report
Product signals: Walking speed · Cadence & rhythm · Step symmetry · Posture markers
Sports gait, running & injury-risk analytics
Product signals: Step symmetry · Stride variability · Posture markers · Cadence & rhythm
Orthopedic & musculoskeletal gait analysis
Product signals: Posture markers · Step symmetry · Walking speed
Pediatric gait & developmental movement support
Product signals: Step symmetry · Posture markers
Shared architecture · 7 additional modules
These modules share platform capabilities. The publication does not directly address their applications.
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