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.

Fig. 1

CONSORT diagram illustrating study recruitment and drop-out.

Fig. 2

Layout of test-bed house used for data collection.

Fig. 3

Example of a typical study schedule across five days for participants. RGB: red-green-blue video data; MDS-UPDRS: Movement Disorder Society sponsored revisin of the Unified Parkinson’s Disease Rating Scale; TUG-test: Timed-Up-and-Go test; PD medications: Parkinson’s disease medications.

Fig. 4

Layout of 2-dimensional skeleton joints used in sit-to-stand and turning.

Fig. 5

Illustration of the use-case automatic sit-to-stand speed and duration estimation approach.