The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Calibration and Validation of Wearable Monitors
Open accessAbstract
BACKGROUND: Wearable monitors are increasingly being used to objectively monitor physical activity in research studies within the field of exercise science. Calibration and validation of these devices are vital to obtaining accurate data. This article is aimed primarily at the physical activity measurement specialist, although the end user who is conducting studies with these devices also may benefit from knowing about this topic. BEST PRACTICES: Initially, wearable physical activity monitors should undergo unit calibration to ensure interinstrument reliability. The next step is to simultaneously collect both raw signal data (e.g., acceleration) from the wearable monitors and rates of energy expenditure, so that algorithms can be developed to convert the direct signals into energy expenditure. This process should use multiple wearable monitors and a large and diverse subject group and should include a wide range of physical activities commonly performed in daily life (from sedentary to vigorous). FUTURE DIRECTIONS: New methods of calibration now use "pattern recognition" approaches to train the algorithms on various activities, and they provide estimates of energy expenditure that are much better than those previously available with the single-regression approach. Once a method of predicting energy expenditure has been established, the next step is to examine its predictive accuracy by cross-validating it in other populations. In this article, we attempt to summarize the best practices for calibration and validation of wearable physical activity monitors. Finally, we conclude with some ideas for future research ideas that will move the field of physical activity measurement forward.
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Cites
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
The Technology of Accelerometry-Based Activity Monitors: Current and Future
CHEN, K. Y., & BASSETT, D. R. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185571.49104.82 · Cited by 996
Self-citationBrage, S., Brage, N., Franks, P. W., Ekelund, U., Wong, M.-Y., Andersen, L. B., Froberg, K., & Wareham, N. J. (2004) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00703.2003 · Cited by 473
Cited by
Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth
TROST, S. G., LOPRINZI, P. D., MOORE, R., & PFEIFFER, K. A. (2011) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e318206476e · Cited by 1,360
Self-citationRecent Advances in Wearable Sensors for Health Monitoring
Rodgers, M. M., Pai, V. M., & Conroy, R. S. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2357257 · Cited by 325
How consumer physical activity monitors could transform human physiology research
Wright, S. P., Hall Brown, T. S., Collier, S. R., & Sandberg, K. (2017) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00349.2016 · Cited by 234
Janssen, X., Cliff, D. P., Reilly, J. J., Hinkley, T., Jones, R. A., Batterham, M., Ekelund, U., Brage, S., & Okely, A. D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079124 · Cited by 190
Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification
ELLIS, K., KERR, J., GODBOLE, S., STAUDENMAYER, J., & LANCKRIET, G. (2016) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000840 · Cited by 186
Metadata
- Authors
- BASSETT, D. R., ROWLANDS, A., & TROST, S. G.
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2012-01
- Volume
- 44
- Issue
- 1S
- Pages
- S32-S38
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2084779848
- Cited by
- 292
- References
- 3
- FWCI
- 11.94
- Type
- journal article
- Language
- en
- Open access
- green
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