Performance of Activity Classification Algorithms in Free-Living Older Adults.
Purpose: The objective of this study is to compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults. Methods: Thirty-five older adults (21 females and 14 males, 70.8 ± 4.9 yr) performed selected activities i...
| Publicado en: | Medicine & Science in Sports & Exercise Vol. 48; no. 5; pp. 941 - 951 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
May2016
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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=114587497&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 114587497 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01959131 4DP jtl: Medicine & Science in Sports & Exercise issn: 01959131 maglogo: N pubinfo: dt: May2016 vid: 48 iid: 5 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 114587497 114587497 114587497 10.1249/MSS.0000000000000844 114587497 ppf: 941 ppct: 10 formats: tig: atl: Performance of Activity Classification Algorithms in Free-Living Older Adults. aug: au: SASAKI, JEFFER EIDI HICKEY, AMANDA M. STAUDENMAYER, JOHN W. JOHN, DINESH KENT, JANE A. FREEDSON, PATTY S. affil: Department of Kinesiology, University of Massachusetts, Amherst, MA sug: subj: Physical Activity Evaluation Classification Algorithms Age Factors Human Male Female Middle Age Receptors, Pattern Recognition Monitoring, Physiologic Accelerometers Data Analysis Software Descriptive Statistics Funding Source Aged Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Purpose: The objective of this study is to compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults. Methods: Thirty-five older adults (21 females and 14 males, 70.8 ± 4.9 yr) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors (in the dominant hip, wrist, and ankle; ActiGraph, LLC, Pensacola, FL). Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz. Fifteen of the participants also wore GT3X+ in free-living settings and were directly observed for 2-3 h. Time- and frequency-domain features from acceleration signals of each monitor were used to train random forest (RF) and support vector machine (SVM) models to classify five activity types: sedentary, standing, household, locomotion, and recreational activities. All algorithms were trained on laboratory data (RFLab and SVMLab) and free-living data (RFFL and SVMFL) using 20-s signal sampling windows. Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique. Results: Overall classification accuracy rates for the algorithms developed from laboratory data were between 49% (wrist) and 55% (ankle) for the SVMLab algorithms and 49% (wrist) to 54% (ankle) for the RFLab algorithms. The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58% (wrist) to 69% (ankle) and from 61% (wrist) to 67% (ankle), respectively. Conclusions: Our algorithms developed on free-living accelerometer data were more accurate in classifying the activity type in free-living older adults than those on our algorithms developed on laboratory accelerometer data. Future studies should consider using free-living accelerometer data to train machine learning algorithms in older adults. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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