Cited by 186Context-Aware Activity Recognition SystemsPhysical Activity and HealthHuman Mobility and Location-Based Analysis

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Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification

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ELLIS, KATHERINE, KERR, JACQUELINE, GODBOLE, SUNEETA, STAUDENMAYER, JOHN, LANCKRIET, GERT·2016·Medicine & Science in Sports & Exercise

DOI 10.1249/mss.0000000000000840PMC PMC4833514

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Abstract

PURPOSE: Accelerometers are a valuable tool for objective measurement of physical activity (PA). Wrist-worn devices may improve compliance over standard hip placement, but more research is needed to evaluate their validity for measuring PA in free-living settings. Traditional cut-point methods for accelerometers can be inaccurate and need testing in free living with wrist-worn devices. In this study, we developed and tested the performance of machine learning (ML) algorithms for classifying PA types from both hip and wrist accelerometer data. METHODS: Forty overweight or obese women (mean age = 55.2 ± 15.3 yr; BMI = 32.0 ± 3.7) wore two ActiGraph GT3X+ accelerometers (right hip, nondominant wrist; ActiGraph, Pensacola, FL) for seven free-living days. Wearable cameras captured ground truth activity labels. A classifier consisting of a random forest and hidden Markov model classified the accelerometer data into four activities (sitting, standing, walking/running, and riding in a vehicle). Free-living wrist and hip ML classifiers were compared with each other, with traditional accelerometer cut points, and with an algorithm developed in a laboratory setting. RESULTS: The ML classifier obtained average values of 89.4% and 84.6% balanced accuracy over the four activities using the hip and wrist accelerometer, respectively. In our data set with average values of 28.4 min of walking or running per day, the ML classifier predicted average values of 28.5 and 24.5 min of walking or running using the hip and wrist accelerometer, respectively. Intensity-based cut points and the laboratory algorithm significantly underestimated walking minutes. CONCLUSIONS: Our results demonstrate the superior performance of our PA-type classification algorithm, particularly in comparison with traditional cut points. Although the hip algorithm performed better, additional compliance achieved with wrist devices might justify using a slightly lower performing algorithm.

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Authors: ELLIS, K., KERR, J., GODBOLE, S., STAUDENMAYER, J., & LANCKRIET, G.
Affiliations: University of Massachusetts Amherst (US)
ORCID: ELLIS, KATHERINE
KERR, JACQUELINE
GODBOLE, SUNEETA
STAUDENMAYER, JOHN
Venue: Medicine & Science in Sports & Exercise
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2016-05
Volume: 48
Issue: 5
Pages: 933-940
ISSN: 0195-9131 (ISSN-L)
DOI: 10.1249/mss.0000000000000840
PMC: PMC4833514
OpenAlex: W2327453846
Cited by: 186
References: 36
Citations by year: 2026: 7 · 2025: 18 · 2024: 18 · 2023: 10 · 2022: 14 · 2021: 23 · 2020: 20 · 2019: 25 · 2018: 22 · 2017: 20 · 2016: 8
FWCI: 14.65
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 (R01CA164993)
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
Social Sciences › Transportation › Human Mobility and Location-Based Analysis
Keywords: hip-worn accelerometers, wrist-worn accelerometers, physical activity classification, free-living activity monitoring, random forest, hidden Markov models, accelerometer cut-points, walking and running detection, wearable cameras, ActiGraph GT3X+
MeSH terms: Machine Learning, Activities of Daily Living, Adult, Aged, Algorithms, Female, Hip, Humans, Markov Chains, Middle Aged, Obesity, Sensitivity and Specificity, Wrist, Exercise, Monitoring, Ambulatory, Overweight, Accelerometry

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Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification

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