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The Technology of Accelerometry-Based Activity Monitors: Current and Future
Abstract
PURPOSE: This paper reviews accelerometry-based activity monitors, including single-site first-generation devices, emerging technologies, and analytical approaches to predict energy expenditure, with suggestions for further research and development. METHODS: The physics and measurement principles of the accelerometer are described, including the sensor properties, data collections, filtering, and integration analyses. The paper also compares these properties in several commonly used single-site accelerometers. The emerging accelerometry technologies introduced include the multisensor arrays and the combination of accelerometers with physiological sensors. The outputs of accelerometers are compared with criterion measures of energy expenditure (indirect calorimeters and double-labeled water) to develop mathematical models (linear, nonlinear, and variability approaches). RESULTS: The technologies of the sensor and data processing directly influence the results of the outcome measurement (activity counts and energy expenditure predictions). Multisite assessment and combining accelerometers with physiological measures may offer additional advantages. Nonlinear approaches to predict energy expenditure using accelerometer outputs from multiple sites and orientation can enhance accuracy. CONCLUSIONS: The development of portable accelerometers has made objective assessments of physical activity possible. Future technological improvements will include examining raw acceleration signals and developing advanced models for accurate energy expenditure predictions.
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“Despite the numerous validation studies already published, the validity of physical activity questionnaires for activity EE estimation remains unclear [63].”
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Loprinzi, P. D., & Pariser, G. L. (2013) · American Journal of Health Promotion · doi:10.4278/ajhp.130227-quan-83 · Cited by 3
Johnstone, J., Hughes, G., Mitchell, A., Ford, P. A., Watson, T., Duffield, R., Gordon, D. A., Roberts, J., & Garrett, A. T. (2017) · PubMed Central · Cited by 3
Metadata
- Authors
- CHEN, K. Y., & BASSETT, D. R.
- Affiliations
- Vanderbilt University (US)
- University of Tennessee at Knoxville (US)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2005-11
- Volume
- 37
- Issue
- 11
- Pages
- S490-S500
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2033727719
- Cited by
- 996
- References
- 61
- Citations by year
- 2026: 15 · 2025: 12 · 2024: 38 · 2023: 20 · 2022: 40 · 2021: 37 · 2020: 51 · 2019: 48 · 2018: 42 · 2017: 65 · 2016: 83 · 2015: 57 · 2014: 84 · 2013: 83 · 2012: 82
- FWCI
- 14.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Diabetes and Digestive and Kidney Diseases (DK 02973, DK 069465, R01DK069465, DK26557, K25DK002973)
- National Center for Research Resources (RR00095)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Keywords
- accelerometry, wearable activity trackers, energy expenditure estimation, multi-sensor array, nonlinear prediction model, physical activity measurement, indirect calorimetry
- MeSH terms
- Acceleration, Diffusion of Innovation, Equipment Design, Humans, Locomotion, United States, Ergometry
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