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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Cites
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Activity identification using body-mounted sensors—a review of classification techniques
Preece, S. J., Goulermas, J. Y., Kenney, L. P. J., Howard, D., Meijer, K., & Crompton, R. H. (2009) · Physiological Measurement · doi:10.1088/0967-3334/30/4/r01 · Cited by 614
Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle
MANNINI, A., INTILLE, S. S., ROSENBERGER, M., SABATINI, A. M., & HASKELL, W. (2013) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31829736d6 · Cited by 383
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Self-citationDetection of Type, Duration, and Intensity of Physical Activity Using an Accelerometer
BONOMI, A. G., Goris, A. H. C., Yin, B., & Westerterp, K. R. (2009) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3181a24536 · Cited by 247
Physical Activity Classification Using the GENEA Wrist-Worn Accelerometer
ZHANG, S., ROWLANDS, A. V., MURRAY, P., & HURST, T. L. (2012) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31823bf95c · Cited by 233
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Willetts, M., Hollowell, S., Aslett, L., Holmes, C., & Doherty, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-26174-1 · Cited by 250
“This labelled dataset for machine learning development and held-out validation is many times larger (~159,504 minutes of behaviour) than previous lab-based studies [330–3,600 mins 15,18,22 ] and free-living studies with short periods of direct observation [3,400–24,000 mins 23,24 ].”
ResultA systematic review of smartphone-based human activity recognition methods for health research
Strączkiewicz, M., James, P., & Onnela, J.-P. “. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00514-4 · Cited by 278
An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing
John, D., Tang, Q., Albinali, F., & Intille, S. S. (2019) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0068 · Cited by 227
Self-citationAccelerometer-assessed Physical Activity in Epidemiology: Are Monitors Equivalent?
ROWLANDS, A. V., MIRKES, E. M., YATES, T., CLEMES, S., DAVIES, M., KHUNTI, K., & EDWARDSON, C. L. (2018) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001435 · Cited by 187
Field evaluation of a random forest activity classifier for wrist-worn accelerometer data
Pavey, T. G., Gilson, N. D., Gomersall, S. R., Clark, B., & Trost, S. G. (2017) · Journal of Science and Medicine in Sport · doi:10.1016/j.jsams.2016.06.003 · Cited by 174
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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