Non-wear or sleep? Evaluation of five non-wear detection algorithms for raw accelerometer data.
Detection of non-wear periods is an important step in accelerometer data processing. This study evaluated five non-wear detection algorithms for wrist accelerometer data and two rules for non-wear detection when non-wear and sleep algorithms are implemented in parallel. Non-wear algorithms were base...
| Publicado en: | Journal of Sports Sciences Vol. 38; no. 4; pp. 399 - 405 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Feb2020
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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=141626922&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141626922 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02640414 5BV jtl: Journal of Sports Sciences issn: 02640414 maglogo: Y pubinfo: dt: Feb2020 vid: 38 iid: 4 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 141626922 141626922 141626922 10.1080/02640414.2019.1703301 141626922 ppf: 399 ppct: 6 formats: tig: atl: Non-wear or sleep? Evaluation of five non-wear detection algorithms for raw accelerometer data. aug: au: Ahmadi, Matthew N. Nathan, Nicole Sutherland, Rachel Wolfenden, Luke Trost, Stewart G. affil: Institute of Health and Biomedical Innovation at QLD Centre for Children's Health Research, School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Australia sug: subj: Detection Algorithms Sleep Accelerometry Monitoring, Physiologic Human Evaluation Research Wrist Wearable Sensors Child Adult Measurement Issues and Assessments Child: 6-12 years Adult: 19-44 years ab: Detection of non-wear periods is an important step in accelerometer data processing. This study evaluated five non-wear detection algorithms for wrist accelerometer data and two rules for non-wear detection when non-wear and sleep algorithms are implemented in parallel. Non-wear algorithms were based on the standard deviation (SD), the high-pass filtered acceleration, or tilt angle. Rules for differentiating sleep from non-wear consisted of an override rule in which any overlap between non-wear and sleep was deemed non-wear; and a 75% rule in which non-wear periods were deemed sleep if the duration was < 75% of the sleep period. Non-wear algorithms were evaluated in 47 children who wore an ActiGraph GT3X+ accelerometer during school hours for 5 days. Rules for differentiating sleep from non-wear were evaluated in 15 adults who wore a GeneActiv Original accelerometer continuously for 24 hours. Classification accuracy for the non-wear algorithms ranged between 0.86–0.95, with the SD of the vector magnitude providing the best performance. The override rule misclassified 37.1 minutes of sleep as non-wear, while the 75% rule resulted in no misclassification. Non-wear algorithms based on the SD of the acceleration signal can effectively detect non-wear periods, while application of the 75% rule can effectively differentiate sleep from non-wear when examined concurrently. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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