Cited by 112Context-Aware Activity Recognition SystemsPhysical Activity and HealthBalance, Gait, and Falls Prevention

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Performance of Activity Classification Algorithms in Free-Living Older Adults

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SASAKI, JEFFER EIDI, HICKEY, AMANDA M., STAUDENMAYER, JOHN W., JOHN, DINESH, KENT, JANE A., FREEDSON, PATTY S.·2016·Medicine & Science in Sports & Exercise

DOI 10.1249/mss.0000000000000844PMC PMC4833628

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Abstract

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.

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Authors: SASAKI, J. E., HICKEY, A. M., STAUDENMAYER, J. W., JOHN, D., KENT, J. A., & FREEDSON, P. S.
Affiliations: Northeastern University (US)
ORCID: SASAKI, JEFFER EIDI
HICKEY, AMANDA M.
STAUDENMAYER, JOHN W.
JOHN, DINESH
KENT, JANE A.
FREEDSON, PATTY S.
Venue: Medicine & Science in Sports & Exercise
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2016-05
Volume: 48
Issue: 5
Pages: 941-950
ISSN: 0195-9131 (ISSN-L)
DOI: 10.1249/mss.0000000000000844
PMC: PMC4833628
OpenAlex: W2200539030
Cited by: 112
References: 18
Citations by year: 2026: 3 · 2025: 5 · 2024: 3 · 2023: 8 · 2022: 7 · 2021: 21 · 2020: 23 · 2019: 13 · 2018: 12 · 2017: 13 · 2016: 3
FWCI: 8.11
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Medicine & Science in Sports & Exercise (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Cancer Institute (R01CA121005)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
Keywords: activity classification, older adults, wearable accelerometers, ActiGraph GT3X+, random forest, support vector machine, sedentary behavior, housework, locomotion, recreational activities, activity monitoring
MeSH terms: Activities of Daily Living, Aged, Algorithms, Ankle, Female, Hip, Humans, Male, Wrist, Monitoring, Ambulatory, Support Vector Machine, Accelerometry

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Performance of Activity Classification Algorithms in Free-Living Older Adults

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