The Performance of an Algorithm for Classifying Gym-based Tasks across Individuals with Different Body Mass Index.
Previous activity classification studies have typically been performed on normal weight individuals. Therefore, it is unclear whether a generic classification algorithm could be developed that would perform consistently across individuals who fall within different BMI categories. Acceleration data w...
| Published in: | Measurement in Physical Education & Exercise Science Vol. 24; no. 4; pp. 282 - 291 |
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| Main Authors: | , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Taylor & Francis Ltd
Oct-Dec2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146525877&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146525877 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1091367X 7MM jtl: Measurement in Physical Education & Exercise Science issn: 1091367X maglogo: N pubinfo: dt: Oct-Dec2020 vid: 24 iid: 4 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 146525877 145583770 146525877 146525877 10.1080/1091367X.2020.1815749 146525877 ppf: 282 ppct: 9 formats: tig: atl: The Performance of an Algorithm for Classifying Gym-based Tasks across Individuals with Different Body Mass Index. aug: au: Gerrard-Longworth, Simon Preece, Stephen J Clarke-Cornwell, Alexandra M Goulermas, Yannis affil: Centre for Health Sciences Research, University of Salford, Manchester, UK sug: subj: Algorithms Body Mass Index Exercise Test Fitness Centers Human Discriminant Analysis Descriptive Statistics Activities of Daily Living Physical Fitness ab: Previous activity classification studies have typically been performed on normal weight individuals. Therefore, it is unclear whether a generic classification algorithm could be developed that would perform consistently across individuals who fall within different BMI categories. Acceleration data were collected from the hip and ankle joints of 50 individuals: 17 normal weight, 14 overweight, and 19 obese. Each participant performed a set of 10 dynamic tasks, which included activities of daily living and gym-based exercises. The performance of a generic classification algorithm, developed using linear discriminant analysis, was compared across the three separate BMI groups for each sensor. Higher classification accuracies (92–95%) were observed for the ankle sensor; however, both sensors demonstrated consistent performance across the three groups. This is the first study to demonstrate the effectiveness of a generic classification algorithm across individuals with different BMI and may be a first step toward automated activity profiling in weight-loss programs. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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