Journal article
A multimodal dataset of real world mobility activities in Parkinson's disease
- Abstract:
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 3.0MB, Terms of use)
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- Publisher copy:
- 10.1038/s41597-023-02663-5
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/R005273/1
- Publisher:
- Springer Nature
- Journal:
- Scientific Data More from this journal
- Volume:
- 10
- Issue:
- 1
- Article number:
- 918
- Publication date:
- 2023-12-20
- Acceptance date:
- 2023-10-19
- DOI:
- EISSN:
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2052-4463
- Pmid:
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38123584
- Language:
-
English
- Keywords:
- Pubs id:
-
1593814
- Local pid:
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pubs:1593814
- Deposit date:
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2025-02-24
- ARK identifier:
Terms of use
- Copyright holder:
- Morgan et al.
- Copyright date:
- 2023
- Rights statement:
- Copyright © 2023, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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