Change search
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
A multimodal sensor fusion platform for objective assessment of motor states in Parkinson's disease
Örebro University, Örebro University School of Business.ORCID iD: 0000-0002-2372-4226
th Computer Engineering, Dalarna University, Sweden.
Department of Pharmacology, Gothenburg University, Sweden.
Department of Neuroscience, Uppsala University, Sweden (.
Show others and affiliations
2019 (English)In: IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI 19), 2019Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

This study proposes a platform to objectively assess motor states in Parkinson’s disease (PD) using sensor technology and machine learning. The platform uses sensor information gathered during standardized motor tasks and fuses them in a data-driven manner to produce an index representing motor states of the patients. After investigating clinimetric properties of the platform it was found that the platform had good validity and responsiveness to treatment, which are essential for developing systems to individualize treatments.

Place, publisher, year, edition, pages
2019.
National Category
Computer and Information Sciences Information Systems
Research subject
Informatics
Identifiers
URN: urn:nbn:se:oru:diva-74621OAI: oai:DiVA.org:oru-74621DiVA, id: diva2:1321122
Conference
IEEE Conference on Biomedical and Health Informatics 2019, Chicago, IL, USA, 19-22 May, 2019
Funder
Knowledge FoundationAvailable from: 2019-06-07 Created: 2019-06-07 Last updated: 2019-06-10Bibliographically approved

Open Access in DiVA

A multimodal sensor fusion platform for objective assessment of motor states in Parkinson’s disease(170 kB)17 downloads
File information
File name FULLTEXT02.pdfFile size 170 kBChecksum SHA-512
711d05c4c010bee3ea360227b087534028080d601cd826c3304230fdb4206e72072b7368fb793bfa7a31d7bb4f14edd0fbdf184c24686fe2992a2e992dc042ea
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Memedi, Mevludin
By organisation
Örebro University School of Business
Computer and Information SciencesInformation Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 17 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: 223 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