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Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0002-1548-5077
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
Halmstad University.
2020 (English)In: Journal of Sensors, ISSN 1687-725X, E-ISSN 1687-7268, Vol. 2020, no 3265795Article in journal (Refereed) Epub ahead of print
Abstract [en]

The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson’s disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD.

Place, publisher, year, edition, pages
2020. Vol. 2020, no 3265795
Keywords [en]
Dynamic Time Warping, Parkinson's disease, signal processing
National Category
Medical Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-31909DOI: 10.1155/2020/3265795OAI: oai:DiVA.org:du-31909DiVA, id: diva2:1393398
Available from: 2020-02-16 Created: 2020-02-16 Last updated: 2020-02-26Bibliographically approved
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Aghanavesi, SomayehFleyeh, HasanDougherty, Mark

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