ID | 118913 |
Author |
Morigaki, Ryoma
Tokushima University
Tokushima University Educator and Researcher Directory
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Nakanishi, Hiroshi
Tokushima University|Beauty Life Corporation
Izumi, Yuishin
Tokushima University
Tokushima University Educator and Researcher Directory
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Keywords | Parkinson’s disease
essential tremor
diagnosis
stimulation
medical devices
machine learning
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Content Type |
Journal Article
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Description | Background: Parkinsonian tremors are sometimes confused with essential tremors or other conditions. Recently, researchers conducted several studies on tremor evaluation using wearable sensors and devices, which may support accurate diagnosis. Mechanical devices are also commonly used to treat tremors and have been actively researched and developed. Here, we aimed to review recent progress and the efficacy of the devices related to Parkinsonian tremors. Methods: The PubMed and Scopus databases were searched for articles. We searched for “Parkinson disease” and “tremor” and “device”. Results: Eighty-six articles were selected by our systematic approach. Many studies demonstrated that the diagnosis and evaluation of tremors in patients with PD can be done accurately by machine learning algorithms. Mechanical devices for tremor suppression include deep brain stimulation (DBS), electrical muscle stimulation, and orthosis. In recent years, adaptive DBS and optimization of stimulation parameters have been studied to further improve treatment efficacy. Conclusions: Due to developments using state-of-the-art techniques, effectiveness in diagnosing and evaluating tremor and suppressing it using these devices is satisfactorily high in many studies. However, other than DBS, no devices are in practical use. To acquire high-level evidence, large-scale studies and randomized controlled trials are needed for these devices.
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Journal Title |
Life
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ISSN | 20751729
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Publisher | MDPI
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Volume | 13
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Issue | 1
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Start Page | 78
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Published Date | 2022-12-27
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Rights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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EDB ID | |
DOI (Published Version) | |
URL ( Publisher's Version ) | |
FullText File | |
language |
eng
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TextVersion |
Publisher
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departments |
Medical Sciences
University Hospital
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