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Found 6 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.
Undersampling and Cumulative Class Re-decision Methods to Improve Detection of Agitation in People with Dementia
Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver's safety at risk.
Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videos
Our work differs from most existing approaches for video anomaly detection that focus on appearance-based features, which can put the privacy of a person at risk and is also susceptible to pixel-based noise, including illumination and viewing direction.
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.
Tracking agitation in people living with dementia in a care environment
Agitation is a symptom that communicates distress in people living with dementia (PwD), and that can place them and others at risk.
