Field evaluation of a random forest activity classifier for wrist-worn accelerometer data.
Objectives: Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance. Previous work has generally relied on choreographed activity trials to train and test classification models. However, validity in free-living contexts is starting to emerge. Study aims were...
| Publicado en: | Journal of Science & Medicine in Sport Vol. 20; no. 1; pp. 75 - 81 |
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| Autores principales: | , , , , |
| Formato: | clinical trial research Journal Article |
| Publicado: |
Elsevier B.V.
Jan2017
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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=120347142&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120347142 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14402440 1FKA jtl: Journal of Science & Medicine in Sport issn: 14402440 maglogo: N pubinfo: dt: Jan2017 vid: 20 iid: 1 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 120347142 120347142 NLM27372275 120347142 10.1016/j.jsams.2016.06.003 NLM27372275 120347142 ppf: 75 ppct: 6 formats: tig: atl: Field evaluation of a random forest activity classifier for wrist-worn accelerometer data. aug: au: Pavey, Toby G. Gilson, Nicholas D. Gomersall, Sjaan R. Clark, Bronwyn Trost, Stewart G. affil: School of Exercise and Nutrition Sciences, Queensland University of Technology, Australia sug: subj: Monitoring, Physiologic Methods Accelerometry Methods Exercise Algorithms Human Adult Sensitivity and Specificity Wrist Young Adult Walking Running Male Random Assignment Sedentary Behavior Female Validation Studies Comparative Studies Evaluation Research Multicenter Studies Arthritis Impact Measurement Scales Scales Clinical Trials Adult: 19-44 years Male Female ab: Objectives: Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance. Previous work has generally relied on choreographed activity trials to train and test classification models. However, validity in free-living contexts is starting to emerge. Study aims were: (1) train and test a random forest activity classifier for wrist accelerometer data; and (2) determine if models trained on laboratory data perform well under free-living conditions.Design: Twenty-one participants (mean age=27.6±6.2) completed seven lab-based activity trials and a 24h free-living trial (N=16).Methods: Participants wore a GENEActiv monitor on the non-dominant wrist. Classification models recognising four activity classes (sedentary, stationary+, walking, and running) were trained using time and frequency domain features extracted from 10-s non-overlapping windows. Model performance was evaluated using leave-one-out-cross-validation. Models were implemented using the randomForest package within R. Classifier accuracy during the 24h free living trial was evaluated by calculating agreement with concurrently worn activPAL monitors.Results: Overall classification accuracy for the random forest algorithm was 92.7%. Recognition accuracy for sedentary, stationary+, walking, and running was 80.1%, 95.7%, 91.7%, and 93.7%, respectively for the laboratory protocol. Agreement with the activPAL data (stepping vs. non-stepping) during the 24h free-living trial was excellent and, on average, exceeded 90%. The ICC for stepping time was 0.92 (95% CI=0.75-0.97). However, sensitivity and positive predictive values were modest. Mean bias was 10.3min/d (95% LOA=-46.0 to 25.4min/d).Conclusions: The random forest classifier for wrist accelerometer data yielded accurate group-level predictions under controlled conditions, but was less accurate at identifying stepping verse non-stepping behaviour in free living conditions Future studies should conduct more rigorous field-based evaluations using observation as a criterion measure. pubtype: Academic Journal doctype: clinical trial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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