The DOI matches its Crossref record; 35 of the 36 references it cites were found in a registry or an index.
Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification
Open accessAbstract
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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“a strong random forest model with tried-and-tested time series features, which has often been neglected in baseline model comparisons8,48–50.”
From Self-supervised learning for human activity recognition using 700,000 person-days of wearable data (2024) · cites this paper as [50]
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“However, our data and that from Ellis et al 30 . shows that walking is challenging to classify in free-living conditions (sensitivity = 0.71, specificity = 0.96), probably due to it being part of many everyday activities.”
From Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants (2018) · cites this paper as [30]
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Migueles, J. H., Cadenas‐Sánchez, C., Ekelund, U., Nyström, C. D., Mora-González, J., Löf, M., Labayen, I., Ruiz, J. R., & Ortega, F. B. (2017) · Sports Medicine · doi:10.1007/s40279-017-0716-0 · Cited by 2,022
Chicco, D., Tötsch, N., & Jurman, G. (2021) · BioData Mining · doi:10.1186/s13040-021-00244-z · Cited by 893
Safaei, M., Sundararajan, E. A., Driss, M., Boulila, W., & Shapi'i, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104754 · Cited by 720
Willetts, M., Hollowell, S., Aslett, L., Holmes, C., & Doherty, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-26174-1 · Cited by 250
“Our method is device agnostic, and could be reused in other large sensor datasets 10,11,32,33 , provided model tuning takes place in a relevant population with free-living groundtruth validation tools such as wearable cameras 13,30 .”
MethodResultInfluentialBarnett, T. A., Kelly, A. S., Young, D. R., Perry, C. K., Pratt, C. A., Edwards, N. M., Rao, G., Vos, M. B., & On behalf of the American Heart Association Obesity Committee of the Council on Lifestyle and Cardiometabolic Health; Council on Cardiovascular Disease in the Young; and Stroke Council (2018) · Circulation · doi:10.1161/cir.0000000000000591 · Cited by 249
Metadata
- Authors
- ELLIS, K., KERR, J., GODBOLE, S., STAUDENMAYER, J., & LANCKRIET, G.
- Affiliations
- University of Massachusetts Amherst (US)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2016-05
- Volume
- 48
- Issue
- 5
- Pages
- 933-940
- ISSN
- 0195-9131 (ISSN-L)
- 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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