Physical activity classification utilizing SenseWear activity monitor in manual wheelchair users with spinal cord injury.
Study design:Validation.Objectives:The primary aim of this study was to develop and evaluate activity classification algorithms for a multisensor-based SenseWear (SW) activity monitor that can recognize wheelchair-related activities performed by manual wheelchair users (MWUs) with spinal cord injury...
| Publicado en: | Spinal Cord Vol. 51; no. 9; pp. 705 - 710 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2013
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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=104223769&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104223769 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13624393 DZQ jtl: Spinal Cord issn: 13624393 maglogo: Y pubinfo: dt: Sep2013 vid: 51 iid: 9 pid: 2579 pub: Springer Nature place: Dordrecht, <Blank> artinfo: ui: 104223769 90130467 10.1038/sc.2013.39 NLM23689386 104223769 ppf: 705 ppct: 5 formats: tig: atl: Physical activity classification utilizing SenseWear activity monitor in manual wheelchair users with spinal cord injury. aug: au: Hiremath, S V Ding, D Farringdon, J Vyas, N Cooper, R A affil: 1] Department of Veterans Affairs, Human Engineering Research Laboratories, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA [2] Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA, USA sug: subj: Physical Activity Classification Spinal Cord Injuries Rehabilitation Wheelchairs Algorithms Human Descriptive Statistics Convenience Sample Data Analysis Software Male Female Sensitivity and Specificity Adolescence Adult Middle Age Pennsylvania Monitoring, Physiologic Methods Precision Validity Funding Source Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Study design:Validation.Objectives:The primary aim of this study was to develop and evaluate activity classification algorithms for a multisensor-based SenseWear (SW) activity monitor that can recognize wheelchair-related activities performed by manual wheelchair users (MWUs) with spinal cord injury (SCI). The secondary aim was to evaluate how the accuracy in activity classification affects the estimation of energy expenditure (EE) in MWUs with SCI.Setting:University-based laboratory.Methods:Forty-five MWUs with SCI wore a SW on their upper arm and participated in resting, wheelchair propulsion, arm-ergometery and deskwork activities. The investigators annotated the start and end of each activity trial while the SW collected multisensor data and a portable metabolic cart collected criterion EE. Three methods including linear discriminant analysis, quadratic discriminant analysis (QDA), and Naïve Bayes (NB) were used to develop classification algorithms for four activities based on the training data set from 36 subjects.Results:The classification accuracy was 96.3% for QDA and 94.8% for NB when the classification algorithms were tested on the validation data set from nine subjects. The average EE estimation errors using the activity-specific EE prediction model were 5.3±21.5% and 4.6±22.8% when the QDA and NB classification algorithms were applied, respectively, as opposed to 4.9±20.7% when 100% classification accuracy was assumed.Conclusion:The high classification accuracy and low EE estimation errors suggest that the SW can be used by researchers and clinicians to classify and estimate the EE for the four activities tested in this study among MWUs with SCI. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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