Performance of activity classification algorithms in free-living older adults

SASAKI, JEFFER EIDI; HICKEY, AMANDA M.; STAUDENMAYER, JOHN W.; JOHN, DINESH; KENT, JANE A.; FREEDSON, PATTY S.

doi:10.1249/mss.0000000000000844

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

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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Much work has been done on using wearable sensor data features to identify daily functional activities in unsupervised conditions using simple wearable technology.
“Much work has been done on using wearable sensor data features to identify daily functional activities in unsupervised conditions using simple wearable technology [e.g., Ref. (101, 102)].”
From Could Wearable and Mobile Technology Improve the Management of Essential Tremor? (2018) · cites this paper as [102]

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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 › Physiology › 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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