Machine Learning Techniques for Parkinson’s Disease Detection using Wearables during a Timed-up-and-Go-Test

In this paper, the classification models for Idiopathic Parkinson's syndrome (iPS) detection through timed-up-and-go test performed on iPS-patients are given. The models are based on the supervised learning. The data are extracted via Myo gesture armband worn on two hands. The corresponding mod...

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Autores principales:	Hossein Tabatabaei Seyed Amir, Pedrosa David, Eggers Carsten, Wullstein Max, Kleinholdermann Urs, Fischer Patrick, Sohrabi Keywan
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	parkinson’s disease supervised learning convolutional neural network classification accuracy Medicine R
Acceso en línea:	https://doaj.org/article/ee3ed0d612dc4f4894c6e4ec4ab07546
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spelling	oai:doaj.org-article:ee3ed0d612dc4f4894c6e4ec4ab075462021-12-05T14:10:42ZMachine Learning Techniques for Parkinson’s Disease Detection using Wearables during a Timed-up-and-Go-Test2364-550410.1515/cdbme-2020-3097https://doaj.org/article/ee3ed0d612dc4f4894c6e4ec4ab075462020-09-01T00:00:00Zhttps://doi.org/10.1515/cdbme-2020-3097https://doaj.org/toc/2364-5504In this paper, the classification models for Idiopathic Parkinson's syndrome (iPS) detection through timed-up-and-go test performed on iPS-patients are given. The models are based on the supervised learning. The data are extracted via Myo gesture armband worn on two hands. The corresponding models are based on extracted features from signal data and raw signal data respectively. The achieved accuracy from both models are 0.91 and 0.93 with reasonable specificity and sensitivity.Hossein Tabatabaei Seyed AmirPedrosa DavidEggers CarstenWullstein MaxKleinholdermann UrsFischer PatrickSohrabi KeywanDe Gruyterarticleparkinson’s diseasesupervised learningconvolutional neural networkclassificationaccuracyMedicineRENCurrent Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 376-379 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	parkinson’s disease supervised learning convolutional neural network classification accuracy Medicine R
spellingShingle	parkinson’s disease supervised learning convolutional neural network classification accuracy Medicine R Hossein Tabatabaei Seyed Amir Pedrosa David Eggers Carsten Wullstein Max Kleinholdermann Urs Fischer Patrick Sohrabi Keywan Machine Learning Techniques for Parkinson’s Disease Detection using Wearables during a Timed-up-and-Go-Test
description	In this paper, the classification models for Idiopathic Parkinson's syndrome (iPS) detection through timed-up-and-go test performed on iPS-patients are given. The models are based on the supervised learning. The data are extracted via Myo gesture armband worn on two hands. The corresponding models are based on extracted features from signal data and raw signal data respectively. The achieved accuracy from both models are 0.91 and 0.93 with reasonable specificity and sensitivity.
format	article
author	Hossein Tabatabaei Seyed Amir Pedrosa David Eggers Carsten Wullstein Max Kleinholdermann Urs Fischer Patrick Sohrabi Keywan
author_facet	Hossein Tabatabaei Seyed Amir Pedrosa David Eggers Carsten Wullstein Max Kleinholdermann Urs Fischer Patrick Sohrabi Keywan
author_sort	Hossein Tabatabaei Seyed Amir
title	Machine Learning Techniques for Parkinson’s Disease Detection using Wearables during a Timed-up-and-Go-Test
title_short	Machine Learning Techniques for Parkinson’s Disease Detection using Wearables during a Timed-up-and-Go-Test
title_full	Machine Learning Techniques for Parkinson’s Disease Detection using Wearables during a Timed-up-and-Go-Test
title_fullStr	Machine Learning Techniques for Parkinson’s Disease Detection using Wearables during a Timed-up-and-Go-Test
title_full_unstemmed	Machine Learning Techniques for Parkinson’s Disease Detection using Wearables during a Timed-up-and-Go-Test
title_sort	machine learning techniques for parkinson’s disease detection using wearables during a timed-up-and-go-test
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/ee3ed0d612dc4f4894c6e4ec4ab07546
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Machine Learning Techniques for Parkinson’s Disease Detection using Wearables during a Timed-up-and-Go-Test

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