Activity-aware essential tremor evaluation using deep learning method based on acceleration data.
Blockchain
Convolutional neural network
Deep learning
Essential tremor
Human activity recognition
IoTA
Journal
Parkinsonism & related disorders
ISSN: 1873-5126
Titre abrégé: Parkinsonism Relat Disord
Pays: England
ID NLM: 9513583
Informations de publication
Date de publication:
01 2019
01 2019
Historique:
received:
26
03
2018
revised:
06
07
2018
accepted:
02
08
2018
pubmed:
21
8
2018
medline:
3
3
2020
entrez:
21
8
2018
Statut:
ppublish
Résumé
Essential tremor (ET), one of the most common neurological disorders is typically evaluated with validated rating scales which only provide a subjective assessment during a clinical visit, underestimating the fluctuations tremor during different daily activities. Motion sensors have shown favorable performances in both quantifying tremor and voluntary human activity recognition (HAR). To create an automated system of a reference scale using motion sensors supported by deep learning algorithms to accurately rate ET severity during voluntary activities, and to propose an IOTA based blockchain application to share anonymously tremor data. A smartwatch-based tremor monitoring system was used to collect motion data from 20 subjects while they were doing standard tasks. Two neurologists rated ET by Fahn-Tolosa Marin Tremor Rating Scale (FTMTRS). Supported by deep learning techniques, activity classification models (ACMs) and tremor evaluation models (TEMs) were created and algorithms were implemented, to distinguish voluntary human activities and evaluate tremor severity respectively. A practical application example showed that the proposed ACMs can classify six typical activities with high accuracy (89.73%-98.84%) and the results produced by the TEMs are significantly correlated with the FTMTRS ratings of two neurologists (r This study demonstrated that motion sensor data, supported by deep learning algorithms, can be used to classify human activities and evaluate essential tremor severity during different activities.
Sections du résumé
BACKGROUND
Essential tremor (ET), one of the most common neurological disorders is typically evaluated with validated rating scales which only provide a subjective assessment during a clinical visit, underestimating the fluctuations tremor during different daily activities. Motion sensors have shown favorable performances in both quantifying tremor and voluntary human activity recognition (HAR).
OBJECTIVE
To create an automated system of a reference scale using motion sensors supported by deep learning algorithms to accurately rate ET severity during voluntary activities, and to propose an IOTA based blockchain application to share anonymously tremor data.
METHOD
A smartwatch-based tremor monitoring system was used to collect motion data from 20 subjects while they were doing standard tasks. Two neurologists rated ET by Fahn-Tolosa Marin Tremor Rating Scale (FTMTRS). Supported by deep learning techniques, activity classification models (ACMs) and tremor evaluation models (TEMs) were created and algorithms were implemented, to distinguish voluntary human activities and evaluate tremor severity respectively.
RESULT
A practical application example showed that the proposed ACMs can classify six typical activities with high accuracy (89.73%-98.84%) and the results produced by the TEMs are significantly correlated with the FTMTRS ratings of two neurologists (r
CONCLUSION
This study demonstrated that motion sensor data, supported by deep learning algorithms, can be used to classify human activities and evaluate essential tremor severity during different activities.
Identifiants
pubmed: 30122598
pii: S1353-8020(18)30331-6
doi: 10.1016/j.parkreldis.2018.08.001
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
17-22Informations de copyright
Copyright © 2018. Published by Elsevier Ltd.