Identifying physical activity type in manual wheelchair users with spinal cord injury by means of accelerometers.
Study design:This was a cross-sectional study.Objectives:The main objective of this study was to develop and test classification algorithms based on machine learning using accelerometers to identify the activity type performed by manual wheelchair users with spinal cord injury (SCI).Setting:The stud...
| Publicado en: | Spinal Cord Vol. 53; no. 10; pp. 772 - 778 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110080347&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110080347 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13624393 DZQ jtl: Spinal Cord issn: 13624393 maglogo: Y pubinfo: dt: Oct2015 vid: 53 iid: 10 pid: 2579 pub: Springer Nature place: Dordrecht, <Blank> artinfo: ui: 110080347 110080347 110080347 10.1038/sc.2015.81 NLM25987002 110080347 ppf: 772 ppct: 6 formats: tig: atl: Identifying physical activity type in manual wheelchair users with spinal cord injury by means of accelerometers. aug: au: García-Massó, X Serra-Añó, P Gonzalez, L M Ye-Lin, Y Prats-Boluda, G Garcia-Casado, J affil: Departamento de Didáctica de la Expresión Musical, Plástica y Corporal, Universidad de Valencia, Valencia, Spain sug: subj: Physical Activity Accelerometers Spinal Cord Injuries Complications Wheelchairs Human Algorithms Spain Cross Sectional Studies Descriptive Statistics Discriminant Analysis Adult Middle Age Scales Data Analysis Software Wilcoxon Rank Sum Test Factor Analysis Validity Funding Source Adult: 19-44 years Middle Aged: 45-64 years ab: Study design:This was a cross-sectional study.Objectives:The main objective of this study was to develop and test classification algorithms based on machine learning using accelerometers to identify the activity type performed by manual wheelchair users with spinal cord injury (SCI).Setting:The study was conducted in the Physical Therapy department and the Physical Education and Sports department of the University of Valencia.Methods:A total of 20 volunteers were asked to perform 10 physical activities, lying down, body transfers, moving items, mopping, working on a computer, watching TV, arm-ergometer exercises, passive propulsion, slow propulsion and fast propulsion, while fitted with four accelerometers placed on both wrists, chest and waist. The activities were grouped into five categories: sedentary, locomotion, housework, body transfers and moderate physical activity. Different machine learning algorithms were used to develop individual and group activity classifiers from the acceleration data for different combinations of number and position of the accelerometers.Results:We found that although the accuracy of the classifiers for individual activities was moderate (55-72%), with higher values for a greater number of accelerometers, grouped activities were correctly classified in a high percentage of cases (83.2-93.6%).Conclusions:With only two accelerometers and the quadratic discriminant analysis algorithm we achieved a reasonably accurate group activity recognition system (>90%). Such a system with the minimum of intervention would be a valuable tool for studying physical activity in individuals with SCI. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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