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Motion sensor-based assessment of Parkinson's disease motor symptoms during leg agility tests: results from levodopa challenge
Department of Computer Engineering, Dalarna University, Borlänge, Sweden.
Department of Pharmacology, Institute of Neuroscience and Physiology, Gothenburg University, Gothenburg, Sweden.
Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
Vise andre og tillknytning
2020 (engelsk)Inngår i: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 24, nr 1, s. 111-118Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees and linear regression, using 10-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2020. Vol. 24, nr 1, s. 111-118
Emneord [en]
Legged locomotion, Diseases, Foot, Feature extraction, Machine learning, Standards, Acceleration, Leg agility, Parkinson's disease, support vector machines, stepwise regression, predictive models
HSV kategori
Forskningsprogram
Informatik
Identifikatorer
URN: urn:nbn:se:oru:diva-72361DOI: 10.1109/JBHI.2019.2898332ISI: 000506642000012Scopus ID: 2-s2.0-85077669455OAI: oai:DiVA.org:oru-72361DiVA, id: diva2:1287222
Forskningsfinansiär
Knowledge FoundationVinnova
Merknad

Funding Agencies:

Acreo  

Cenvigo  

Sensidose  

Uppsala University  

Örebro University  

Dalarna University 

Tilgjengelig fra: 2019-02-09 Laget: 2019-02-09 Sist oppdatert: 2020-03-17bibliografisk kontrollert

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