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A heterogeneous sensing suite for multisymptom quantification of Parkinson’s disease

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Title: A heterogeneous sensing suite for multisymptom quantification of Parkinson’s disease
Authors: Huo, W
Angeles, P
Tai, YF
Pavese, N
Wilson, S
Hu, MT
Vaidyanathan, R
Item Type: Journal Article
Abstract: Parkinson’s disease (PD) is the second most common neurodegenerative disease affecting millions worldwide. Bespoke subject-specific treatment (medication or deep brain stimulation (DBS)) is critical for management, yet depends on precise assessment cardinal PD symptoms - bradykinesia, rigidity and tremor. Clinician diagnosis is the basis of treatment, yet it allows only a cross-sectional assessment of symptoms which can vary on an hourly basis and is liable to inter- and intra-rater subjectivity across human examiners. Automated symptomatic assessment has attracted significant interest to optimise treatment regimens between clinician visits, however, no wearable has the capacity to simultaneously assess all three cardinal symptoms. Challenges in the measurement of rigidity, mapping muscle activity outof-clinic and sensor fusion have inhibited translation. In this study, we address all through a novel wearable sensor system and learning algorithms. The sensor system is composed of a force-sensor, two inertial measurement units (IMUs) and four custom mechanomyography (MMG) sensors. The system was tested in its capacity to predict Unified Parkinson’s Disease Rating Scale (UPDRS) scores based on quantitative assessment of bradykinesia, rigidity and tremor in PD patients. 23 PD patients were tested with the sensor system in parallel with exams conducted by treating clinicians and 10 healthy subjects were recruited as a comparison control group. Results prove the system accurately predicts UPDRS scores for all symptoms (85.4% match on average with physician assessment) and discriminates between healthy subjects and PD patients (96.6% on average). MMG features can also be used for remote monitoring of severity and fluctuations in PD symptoms out-of-clinic. This closedloop feedback system enables individually tailored and regularly updated treatment, facilitating better outcomes for a very large patient population.
Issue Date: 1-Jun-2020
Date of Acceptance: 10-Feb-2020
URI: http://hdl.handle.net/10044/1/77054
DOI: 10.1109/TNSRE.2020.2978197
ISSN: 1534-4320
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 1397
End Page: 1406
Journal / Book Title: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume: 28
Issue: 6
Copyright Statement: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Sponsor/Funder: Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Office Of Naval Research Global
Funder's Grant Number: EP/R511547/1
Keywords: Science & Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Parkinson's disease symptoms
wearable sensor system
machine learning
muscle stiffness
0903 Biomedical Engineering
0906 Electrical and Electronic Engineering
Biomedical Engineering
Publication Status: Published
Open Access location: https://ieeexplore.ieee.org/document/9064818
Online Publication Date: 2020-04-13
Appears in Collections:Mechanical Engineering
Faculty of Medicine
Department of Brain Sciences
Faculty of Engineering