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A Wearable System to Objectify Assessment of Motor Tasks for Supporting Parkinson’s Disease Diagnosis
Article

Entropy of Real-World Gait in Parkinson’s Disease Determined from Wearable Sensors as a Digital Marker of Altered Ambulatory Behavior

1
School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
2
Translational and Clinical Research Unit, Clinical Ageing Research Unit, Newcastle University, Newcastle upon Tyne NE4 5PL, UK
3
Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK
*
Author to whom correspondence should be addressed.
Received: 2 April 2020 / Revised: 29 April 2020 / Accepted: 2 May 2020 / Published: 5 May 2020
(This article belongs to the Collection Sensors in Biomechanics)
Parkinson’s disease (PD) is a common age-related neurodegenerative disease. Gait impairment is frequent in the later stages of PD contributing to reduced mobility and quality of life. Digital biomarkers such as gait velocity and step length are predictors of motor and cognitive decline in PD. Additional gait parameters may describe different aspects of gait and motor control in PD. Sample entropy (SampEnt), a measure of signal predictability, is a nonlinear approach that quantifies regularity of a signal. This study investigated SampEnt as a potential biomarker for PD and disease duration. Real-world gait data over a seven-day period were collected using an accelerometer (Axivity AX3, York, UK) placed on the low back and gait metrics extracted. SampEnt was determined for the stride time, with vector length and threshold parameters optimized. People with PD had higher stride time SampEnt compared to older adults, indicating reduced gait regularity. The range of SampEnt increased over 36 months for the PD group, although the mean value did not change. SampEnt was associated with dopaminergic medication dose but not with clinical motor scores. In conclusion, this pilot study indicates that SampEnt from real-world data may be a useful parameter reflecting clinical status although further research is needed involving larger populations. View Full-Text
Keywords: wearable technology; gait; Parkinson’s disease; sample entropy; variability; real-world wearable technology; gait; Parkinson’s disease; sample entropy; variability; real-world
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MDPI and ACS Style

Coates, L.; Shi, J.; Rochester, L.; Del Din, S.; Pantall, A. Entropy of Real-World Gait in Parkinson’s Disease Determined from Wearable Sensors as a Digital Marker of Altered Ambulatory Behavior. Sensors 2020, 20, 2631. https://0-doi-org.brum.beds.ac.uk/10.3390/s20092631

AMA Style

Coates L, Shi J, Rochester L, Del Din S, Pantall A. Entropy of Real-World Gait in Parkinson’s Disease Determined from Wearable Sensors as a Digital Marker of Altered Ambulatory Behavior. Sensors. 2020; 20(9):2631. https://0-doi-org.brum.beds.ac.uk/10.3390/s20092631

Chicago/Turabian Style

Coates, Lucy, Jian Shi, Lynn Rochester, Silvia Del Din, and Annette Pantall. 2020. "Entropy of Real-World Gait in Parkinson’s Disease Determined from Wearable Sensors as a Digital Marker of Altered Ambulatory Behavior" Sensors 20, no. 9: 2631. https://0-doi-org.brum.beds.ac.uk/10.3390/s20092631

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