TAA-GCN: A temporally aware Adaptive Graph Convolutional Network for age estimation

Korban, Matthew; Youngs, Peter; Acton, Scott T.

doi:10.1016/j.patcog.2022.109066

Cited by 7 publications

(1 citation statement)

References 49 publications

(66 reference statements)

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“…if someone had a habit of standing in a certain way that was particularly identifiable). It has been shown that skeleton data can reveal gender 77 , age to a 10-year range 78 and, if full gait cycles are shown, be re-identifiable to the person in the video to a high degree of accuracy (>80%) 79 , 80 . Therefore, before this kind of data is released openly, careful consideration must be given to minimising re-idenfiability whilst retaining the useful content of the data.…”

Section: Methodsmentioning

confidence: 99%

A multimodal dataset of real world mobility activities in Parkinson’s disease

Morgan,

Tonkin,

Masullo

et al. 2023

Sci Data

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Parkinson’s disease (PD) is a neurodegenerative disorder characterised by motor symptoms such as gait dysfunction and postural instability. Technological tools to continuously monitor outcomes could capture the hour-by-hour symptom fluctuations of PD. Development of such tools is hampered by the lack of labelled datasets from home settings. To this end, we propose REMAP (REal-world Mobility Activities in Parkinson’s disease), a human rater-labelled dataset collected in a home-like setting. It includes people with and without PD doing sit-to-stand transitions and turns in gait. These discrete activities are captured from periods of free-living (unobserved, unstructured) and during clinical assessments. The PD participants withheld their dopaminergic medications for a time (causing increased symptoms), so their activities are labelled as being “on” or “off” medications. Accelerometry from wrist-worn wearables and skeleton pose video data is included. We present an open dataset, where the data is coarsened to reduce re-identifiability, and a controlled dataset available on application which contains more refined data. A use-case for the data to estimate sit-to-stand speed and duration is illustrated.

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Section: Methodsmentioning

confidence: 99%