Motor Signatures in Digitized Cognitive and Memory Tests Enhances Characterization of Parkinson’s Disease

Although interest in using wearable sensors to characterize movement disorders is growing, there is a lack of methodology for developing clinically interpretable biomarkers. Such digital biomarkers would provide a more objective diagnosis, capturing finer degrees of motor deficits, while retaining t...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-06, Vol.22 (12), p.4434
Main Authors: Ryu, Jihye, Torres, Elizabeth B.
Format: Article
Language:English
Subjects:
Amplitudes
Biomarkers
Biometrics
Biosensors
digital biomarkers
Digitization
Kinematics
Memory
Memory tasks
micro-movement spikes
Open source software
Parkinson's disease
Sensory feedback
Signal to noise ratio
stochastic analysis
Voice control
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Summary:Although interest in using wearable sensors to characterize movement disorders is growing, there is a lack of methodology for developing clinically interpretable biomarkers. Such digital biomarkers would provide a more objective diagnosis, capturing finer degrees of motor deficits, while retaining the information of traditional clinical tests. We aim at digitizing traditional tests of cognitive and memory performance to derive motor biometrics of pen-strokes and voice, thereby complementing clinical tests with objective criteria, while enhancing the overall characterization of Parkinson’s disease (PD). 35 participants including patients with PD, healthy young and age-matched controls performed a series of drawing and memory tasks, while their pen movement and voice were digitized. We examined the moment-to-moment variability of time series reflecting the pen speed and voice amplitude. The stochastic signatures of the fluctuations in pen drawing speed and voice amplitude of patients with PD show a higher signal-to-noise ratio compared to those of neurotypical controls. It appears that contact motions of the pen strokes on a tablet evoke sensory feedback for more immediate and predictable control in PD, while voice amplitude loses its neurotypical richness. We offer new standardized data types and analytics to discover the hidden motor aspects within the cognitive and memory clinical assays.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22124434