The paper presents AIGaitor, a privacy-preserving on-device monocular motion analysis system that performs end-to-end pose estimation and deep learning gait analysis on consumer smartphones.
Gait analysis methods in rehabilitation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Markerless single-view video pipeline estimates Rodda and Graham knee and ankle z-scores in a heterogeneous pediatric cohort of 152 children, achieving R²=0.80 for knee and R²=0.57 for ankle versus 3D-IGA ground truth.
citing papers explorer
-
AIGaitor: Privacy-preserving and cloud-free motion analysis for everyone, using edge computing
The paper presents AIGaitor, a privacy-preserving on-device monocular motion analysis system that performs end-to-end pose estimation and deep learning gait analysis on consumer smartphones.
-
Quantifying Rodda and Graham Gait Classification from 3D Makerless Kinematics derived from a Single-view Video in a Heterogeneous Pediatric Clinical Cohort
Markerless single-view video pipeline estimates Rodda and Graham knee and ankle z-scores in a heterogeneous pediatric cohort of 152 children, achieving R²=0.80 for knee and R²=0.57 for ankle versus 3D-IGA ground truth.