oru.sePublications
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms
Computer Engineering, School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.ORCID iD: 0000-0002-1548-5077
Dept. of Pharmacology, University of Gothenburg, Gothenbrug, Sweden.
Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
Show others and affiliations
2018 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Title: Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms

Objective: To assess the feasibility of measuring Parkinson’s disease (PD) motor symptoms with a multi-sensor data fusion method. More specifically, the aim is to assess validity, reliability and sensitivity to treatment of the methods.

Background: Data from 19 advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were collected in a single center, open label, single dose clinical trial in Sweden [1].

Methods: The patients performed leg agility and 2-5 meter straight walking tests while wearing motion sensors on their limbs. They performed the tests at baseline, at the time they received the morning dose, and at pre-specified time points until the medication wore off. While performing the tests the patients were video recorded. The videos were observed by three movement disorder specialists who rated the symptoms using a treatment response scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). The sensor data consisted of lower limb data during leg agility, upper limb data during walking, and lower limb data during walking. Time series analysis was performed on the raw sensor data extracted from 17 patients to derive a set of quantitative measures, which were then used during machine learning to be mapped to mean ratings of the three raters on the TRS scale. Combinations of data were tested during the machine learning procedure.

Results: Using data from both tests, the Support Vector Machines (SVM) could predict the motor states of the patients on the TRS scale with a good agreement in relation to the mean ratings of the three raters (correlation coefficient = 0.92, root mean square error = 0.42, p<0.001). Additionally, there was good test-retest reliability of the SVM scores during baseline and second tests with intraclass-correlation coefficient of 0.84. Sensitivity to treatment for SVM was good (Figure 1), indicating its ability to detect changes in motor symptoms. The upper limb data during walking was more informative than lower limb data during walking since SVMs had higher correlation coefficient to mean ratings.  

Conclusions: The methodology demonstrates good validity, reliability, and sensitivity to treatment. This indicates that it could be useful for individualized optimization of treatments among PD patients, leading to an improvement in health-related quality of life.

Place, publisher, year, edition, pages
2018.
National Category
Computer and Information Sciences Physiology
Research subject
Informatics
Identifiers
URN: urn:nbn:se:oru:diva-69855OAI: oai:DiVA.org:oru-69855DiVA, id: diva2:1258863
Conference
International Congress of Parkinson’s Disease and Movement Disorders (MDS), Hong Kong, Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden, 5-9 October, 2018
Available from: 2018-10-25 Created: 2018-10-25 Last updated: 2018-10-29Bibliographically approved

Open Access in DiVA

Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms(273 kB)10 downloads
File information
File name FULLTEXT01.pdfFile size 273 kBChecksum SHA-512
ef07be13003c418c965b05856881a6b035cb66631a83739120c1278b023e7e221304d535630bb4b48b87e86407c03845f3b146eb3514043db7a6c619448e8a4d
Type fulltextMimetype application/pdf

Authority records BETA

Aghanavesi, SomayehMemedi, Mevludin

Search in DiVA

By author/editor
Aghanavesi, SomayehMemedi, Mevludin
By organisation
Örebro University School of Business
Computer and Information SciencesPhysiology

Search outside of DiVA

GoogleGoogle Scholar
Total: 10 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 165 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf