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BY 4.0 license Open Access Published by De Gruyter November 26, 2020

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

Seyed Amir Hossein Tabatabaei, David Pedrosa, Carsten Eggers, Max Wullstein, Urs Kleinholdermann, Patrick Fischer and Keywan Sohrabi


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.

Published Online: 2020-11-26
Published in Print: 2020-09-01

© 2020 by Walter de Gruyter Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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