Activity-aware essential tremor evaluation using deep learning method based on acceleration data
Autor: | Sergio Labrador Marcos, Xiaochen Zheng, Alba Vieira, Joaquín Ordieres-Meré, Yolanda Aladro |
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Rok vydání: | 2018 |
Předmět: |
0301 basic medicine
Adult Male Activities of daily living Computer science Essential Tremor Monitoring Ambulatory Motor Activity Machine learning computer.software_genre Convolutional neural network Motion (physics) Activity recognition Smartwatch 03 medical and health sciences 0302 clinical medicine Deep Learning Microcomputers Rating scale Accelerometry medicine Humans Aged Aged 80 and over Essential tremor business.industry Deep learning Middle Aged medicine.disease nervous system diseases 030104 developmental biology Neurology Female Neurology (clinical) Artificial intelligence Geriatrics and Gerontology business computer 030217 neurology & neurosurgery |
Zdroj: | Parkinsonismrelated disorders. 58 |
ISSN: | 1873-5126 |
Popis: | 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 (r1 = 0.92, p1 = 0.008; r2 = 0.93, p2 = 0.007). 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. |
Databáze: | OpenAIRE |
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