The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
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].”
Citations
Cites
Yu, S. M. (1998) · Maternal and Child Health Journal · doi:10.1023/a:1021801927353 · Cited by 3,876
Calibration of the Computer Science and Applications, Inc. accelerometer
FREEDSON, P. S., MELANSON, E., & SIRARD, J. (1998) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-199805000-00021 · Cited by 3,825
Medical Instrumentation-Application and Design
Webster, J. G. (1978) · Journal of Clinical Engineering · doi:10.1097/00004669-197807000-00017 · Cited by 1,846
Calibration of Accelerometer Output for Children
Freedson, P. S., POBER, D. M., & Janz, K. F. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185658.28284.ba · Cited by 1,150
Energy expenditure by doubly labeled water: validation in humans and proposed calculation
Schoeller, D. A., Ravussin, É., Schutz, Y., Acheson, K. J., Baertschi, P., & Jéquier, E. (1986) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.1986.250.5.r823 · Cited by 683
Calibration of Accelerometer Output for Adults
MATTHEW, C. E. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185659.11982.3d · Cited by 651
Eston, R. G., Rowlands, A. V., & Ingledew, D. K. (1998) · Journal of Applied Physiology · doi:10.1152/jappl.1998.84.1.362 · Cited by 643
Validity of four motion sensors in measuring moderate intensity physical activity
Bassett, D. R. (2000) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-200009001-00006 · Cited by 533
Self-citationValidity of accelerometry for the assessment of moderate intensity physical activity in the field
HENDELMAN, D., MILLER, K., BAGGETT, C., DEBOLD, E., & FREEDSON, P. (2000) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-200009001-00002 · Cited by 516
Estimation of energy expenditure using CSA accelerometers at hip and wrist sites
Swartz, A. M., Strath, S. J., Bassett, D. R., BRIEN, W. L. O., King, G. A., & Ainsworth, B. E. (2000) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-200009001-00003 · Cited by 514
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Validation of Accelerometer Wear and Nonwear Time Classification Algorithm
CHOI, L., LIU, Z., MATTHEWS, C. E., & BUCHOWSKI, M. S. (2011) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3181ed61a3 · Cited by 1,804
Strath, S. J., Kaminsky, L. A., Ainsworth, B. E., Ekelund, U., Freedson, P. S., Gary, R. A., Richardson, C. R., Smith, D. T., & Swartz, A. M. (2013) · Circulation · doi:10.1161/01.cir.0000435708.67487.da · Cited by 1,029
A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring
Yang, C.-C., & Hsu, Y.-L. (2010) · Sensors · doi:10.3390/s100807772 · Cited by 1,058
Evolution of accelerometer methods for physical activity research
Troiano, R. P., McClain, J. J., Brychta, R. J., & Chen, K. Y. (2014) · British Journal of Sports Medicine · doi:10.1136/bjsports-2014-093546 · Cited by 968
Self-citationAccelerometer Use in Physical Activity: Best Practices and Research Recommendations
WARD, D. S., EVENSON, K. R., VAUGHN, A., RODGERS, A. B., & TROIANO, R. P. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185292.71933.91 · Cited by 779
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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