The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Performance of Activity Classification Algorithms in Free-Living Older Adults
Open accessAbstract
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 [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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Self-citationDetection of Type, Duration, and Intensity of Physical Activity Using an Accelerometer
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Accelerometer-based analysis of gait and prediction of fall risk in older adults
Quicksall, Z. (2018) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
Application of Machine Learning in the Measurement of Free-living Physical Activity Behaviours
Narayanan, T. L., & Narayanan, A. (2019)
Ahmadi, M. N. (2020) · Queensland University of Technology · doi:10.5204/thesis.eprints.206178
Physical Sensors: Motion Sensors for Physical Activity Assessment
Sasaki, J. E., & Motl, R. W. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822548-6.00056-x
Self-citationMeaningful use of activity trackers in healthcare
Ummels, D. (2021) · doi:10.26481/dis.20211001du
Co-creating a change-adept organizational culture
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Factors influencing physical activity behaviour of hospitalised patients
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Modeling of ordinal longitudinal accelerometer data
Lazar, D., Alawad, F., Begum, M., & Montoye, A. H. K. (2024) · Journal of Statistical Research · doi:10.3329/jsr.v57i12.72965
Walker, R. L., Greenwood-Hickman, M. A., Bellettiere, J., LaCroix, A. Z., Wing, D., Higgins, M., Richmire, K., Larson, E. B., Crane, P. K., & Rosenberg, D. E. (2021) · Figshare · doi:10.6084/m9.figshare.14355469
Predicting Energy Expenditure in Preschool Children Using Accelerometer and Gyroscope Data
Coyle-Asbil, H. J., Osojnicki, K., Robertson, A. A., Baker, R. E., Grigore, M. M., Burr, J. F., Buck, C., Wright, M. N., & Vallis, L. A. (2026) · Pediatric Exercise Science · doi:10.1123/pes.2025-0014
Sliding Window based Physical Activity Classification using Smart Wearable Data
Ananthavaram, N., Jiang, X., & Ho, N. (2025) · IEEE International Conference on Future Machine Learning and Data Science (FMLDS) · doi:10.1109/fmlds67896.2025.00087
Multi-Sensor Based Activity Recognition˸ Development and Validation in Real-Life context
Rahman, H. A. (2017) · theses.fr (ABES) · doi:10.70675/8c855d1fz7000z4815zab29z33f2730fb1ee
Metadata
- Authors
- SASAKI, J. E., HICKEY, A. M., STAUDENMAYER, J. W., JOHN, D., KENT, J. A., & FREEDSON, P. S.
- Affiliations
- Northeastern University (US)
- ORCID
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2016-05
- Volume
- 48
- Issue
- 5
- Pages
- 941-950
- ISSN
- 0195-9131 (ISSN-L)
- 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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