Activity Recognition in Parkinson's Patients from Motion Data Using a CNN Model Trained by Healthy Subjects

Davidashvilly, Shelly; Hssayeni, Murtadha D.; Chi, Christopher; Jimenez‐Shahed, Joohi; Ghoraani, Behnaz

doi:10.1109/embc48229.2022.9871181

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Cited by 6 publications

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Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions

Kumar,

Chauhan,

Awasthi

2023

Arch Computat Methods Eng

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Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions

Kumar,

Chauhan,

Awasthi

2023

Arch Computat Methods Eng

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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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