A sparse deep autoencoder trained on normal gait skeletons produces a per-skeleton gait abnormality index, and sequence averaging of a weighted reconstruction error reaches 0.945 AUC on a nine-subject simulated dataset.
Stanford university cs231n: Convolutional neural networks for visual recognition
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Estimating skeleton-based gait abnormality index by sparse deep auto-encoder
A sparse deep autoencoder trained on normal gait skeletons produces a per-skeleton gait abnormality index, and sequence averaging of a weighted reconstruction error reaches 0.945 AUC on a nine-subject simulated dataset.