du.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Assessment of Parkinson gait through digital signal processing and machine learning
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
2017 (Engelska)Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
Abstract [en]

It would be of both patients’ as well as clinicians’ interest, if diagnosis of Parkinson’s disease (PD) as well as following check-up methods were perfectly sensitive, accurate, reproducible and feasible of objectively classifying motor symptoms of PD. This is an arduous task due to the possible subjectivity of clinical evaluations. In the past decade, attention turns into a multitude of technology based measures (TBMs) to address this need, among which the method of this research is positioned. Author hopes to contribute with a motor assessment method that addresses not only the issue of subjectivity of measurement, but also does not require extensive installments and is easy to use. For this study, data from a clinical trial conducted at Uppsala University Hospital, Sweden in 2015 are used. 7 PD patients and 7 healthy controls each performed 7-13 times each the same motoric gait test, which has been was video recorded. These recordings were showed to clinicians, who rated subjects’ gait and possible dyskinesia on the unified Parkinson's disease rating scale (0-4 rating). Thus the aim of this research was to imitate and automate the tasks of clinicians when diagnosing PD and its symptoms through motoric ratings, using various gait features. These gait features were obtained through quantification of signals from different body parts while patient performs walking motoric test, using image processing. Diagnosis of PD and its symptoms was twofold, as to firstly identify whether the subject has PD and to secondly predict the severity of PD patients symptoms. When classifying subjects into healthy controls and PD patients, classification trees and support vector machines have been deployed, while these achieved 76- 85% accuracy depending on features selected. Following focus was to diagnose severity of PD among patients, while using UPDRS ratings by clinicians as a target variable for supervised learning. Herein, linear regression has been deployed, while average absolute prediction error was 0.25 and correlation of UPDRS ratings with predicted values was 0.84.

Ort, förlag, år, upplaga, sidor
2017.
Nyckelord [en]
Parkinson’s Disease, Technology Based Measures, Signal Processing
Nationell ämneskategori
Tvärvetenskapliga studier inom samhällsvetenskap
Identifikatorer
URN: urn:nbn:se:du-25846OAI: oai:DiVA.org:du-25846DiVA, id: diva2:1135413
Tillgänglig från: 2017-08-23 Skapad: 2017-08-23 Senast uppdaterad: 2018-01-13

Open Access i DiVA

fulltext(1306 kB)113 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 1306 kBChecksumma SHA-512
64974c301a8b39713a7c7db2744dbc00386539ce37e423483e6a4ac14e6f8813cc924d2e4ffc33d175b01a3de51ac07029be2e9d0e50300e9f9888004b29074e
Typ fulltextMimetyp application/pdf

Av organisationen
Mikrodataanalys
Tvärvetenskapliga studier inom samhällsvetenskap

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 113 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 274 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf