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Found 3 papers, 3 papers with code
Benchmarking Skeleton-based Motion Encoder Models for Clinical Applications: Estimating Parkinson's Disease Severity in Walking Sequences
Lastly, we establish a benchmark for the analysis of skeleton-based motion encoder models in clinical settings.
Pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia
In this work we train a deep neural network to map from a two dimensional pose sequence, extracted from a video of an individual walking down a hallway toward a wall-mounted camera, to a set of three-dimensional spatiotemporal gait features averaged over the walking sequence.
Estimating Parkinsonism Severity in Natural Gait Videos of Older Adults with Dementia
This work leverages novel spatial-temporal graph convolutional network (ST-GCN) architectures and training procedures to predict clinical scores of parkinsonism in gait from video of individuals with dementia.
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