AIGaitor is the first claimed end-to-end on-device monocular motion-capture and deep-learning gait analysis pipeline demonstrated on consumer smartphones.
An Explainable Spatial-Temporal Graphical Convolutional Network to Score Freezing of Gait in Parkinsonian Patients
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Hierarchical CNN-LSTM plus vision transformer detects tremor from raw time-domain kinematic data across nine body parts with average F1 of 0.765 and attention-based explanations.
Markerless pipeline estimates Rodda-Graham knee (R²=0.80) and ankle (R²=0.57) z-scores from single-view videos in 152 children with 60 diagnoses, achieving AUROC=0.88 for excess knee flexion screening.
citing papers explorer
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AIGaitor: Privacy-preserving and cloud-free motion analysis for everyone, using edge computing
AIGaitor is the first claimed end-to-end on-device monocular motion-capture and deep-learning gait analysis pipeline demonstrated on consumer smartphones.
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An explainable hierarchical self attention-based approach for tremor detection in the time domain
Hierarchical CNN-LSTM plus vision transformer detects tremor from raw time-domain kinematic data across nine body parts with average F1 of 0.765 and attention-based explanations.
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Quantifying Rodda and Graham Gait Classification from 3D Markerless Kinematics derived from a Single-view Video in a Heterogeneous Pediatric Clinical Cohort
Markerless pipeline estimates Rodda-Graham knee (R²=0.80) and ankle (R²=0.57) z-scores from single-view videos in 152 children with 60 diagnoses, achieving AUROC=0.88 for excess knee flexion screening.