Measuring Tremor Progression in Free-Living Conditions Using Wearable Sensors
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2022-06-30
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en
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Abstract
Wearable sensors have been used
successfully to detect tremor in patients, but most studies
have been conducted in highly controlled laboratory conditions,
and the patients were only monitored for a short
amount of time.
Objectives: This project aims to investigate (1) whether
we can use wearable sensors to accurately and objectively
detect tremor in daily life and (2) if we can measure the
progression of tremor over time.
Methods: 74 features based on existing literature are
extracted from the raw signal of the triaxial accelerometer
of a smartwatch.We train and validate a logistic regression
classifier on the labelled dataset Parkinson@Home. Subsequently,
we apply the trained classifier to the unlabelled
longitudinal dataset of the Personalised Parkinson Project
(PPP). PPP is unprecedented in cohort size (650 PD
patients) and length (2 years) of the study.
Results and Conclusion: The results on PD@Home indicate
that the classifier can accurately detect tremor with a
sensitivity of 65:90% given a 94:99% specificity. When applied
to PPP, the predicted daily tremor strongly correlates
with the MDS-UPDRS tremor items. However, there is no
apparent progression measurable. We conclude that two
years might not be enough to measure the progression of
tremor sufficiently and a study even longer than PPP is
required to provide more definite evidence.
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Faculteit der Sociale Wetenschappen