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

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.

Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson's Disease Severity

Introduction

Technology holds the potential to track disease progression and response to neuroprotective therapies in Parkinson's disease (PD). The sit-to-stand (STS) transition is a frequently occurring event which is important to people with PD. The aim of this study was to demonstrate an automatic approach to quantify STS duration and speed using a real-world free-living dataset and look at clinical correlations of the outcomes, including whether STS parameters change when someone withholds PD medications.

Methods

Eighty-five hours of video data were collected from 24 participants staying in pairs for 5-day periods in a naturalistic setting. Skeleton joints were extracted from the video data; the head trajectory was estimated and used to estimate the STS parameters of duration and speed.

Results

3.14 STS transitions were seen per hour per person on average. Significant correlations were seen between automatic and manual STS duration (Pearson rho - 0.419, p = 0.042) and between automatic STS speed and manual STS duration (Pearson rho - 0.780, p < 0.001). Significant and strong correlations were seen between the gold-standard clinical rating scale scores and both STS duration and STS speed; these correlations were not seen in the STS transitions when the participants were carrying something in their hand(s). Significant differences were seen at the cohort level between control and PD participants' ON medications' STS duration (U = 6,263, p = 0.018) and speed (U = 9,965, p < 0.001). At an individual level, only two participants with PD became significantly slower to STS when they were OFF medications; withholding medications did not significantly change STS duration at an individual level in any participant.

Conclusion

We demonstrate a novel approach to automatically quantify and ecologically validate two STS parameters which correlate with gold-standard clinical tools measuring disease severity in PD.