Calibration of the Computer Science and Applications, Inc. accelerometer
Abstract
PURPOSE: We established accelerometer count ranges for the Computer Science and Applications, Inc. (CSA) activity monitor corresponding to commonly employed MET categories. METHODS: Data were obtained from 50 adults (25 males, 25 females) during treadmill exercise at three different speeds (4.8, 6.4, and 9.7 km x h(-1)). RESULTS: Activity counts and steady-state oxygen consumption were highly correlated (r = 0.88), and count ranges corresponding to light, moderate, hard, and very hard intensity levels were < or = 1951, 1952-5724, 5725-9498, > or = 9499 cnts x min(-1), respectively. A model to predict energy expenditure from activity counts and body mass was developed using data from a random sample of 35 subjects (r2 = 0.82, SEE = 1.40 kcal x min(-1)). Cross validation with data from the remaining 15 subjects revealed no significant differences between actual and predicted energy expenditure at any treadmill speed (SEE = 0.50-1.40 kcal x min(-1)). CONCLUSIONS: These data provide a template on which patterns of activity can be classified into intensity levels using the CSA accelerometer.
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Freedson et al. cut-points were used to identify time spent in moderate, vigorous, and moderate-to-vigorous physical activity.
“Freedson et al. [31] cut-points were used to identify time spent in moderate (≥1,952 counts/min), vigorous (≥5,725 counts/min), and moderate-to-vigorous physical activity (MVPA, sum of moderate and vigorous) during wear time on valid days (i.e., ≥10 hours of wear time per day, without any excessive counts ≥20,000 counts/min).”
From High-Intensity Interval Training as an Efficacious Alternative to Moderate-Intensity Continuous Training for Adults with Prediabetes (2015) · cites this paper as [31]
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- Authors
- FREEDSON, P. S., MELANSON, E., & SIRARD, J.
- Affiliations
- University of Massachusetts Amherst (US)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 1998-05
- Volume
- 30
- Issue
- 5
- Pages
- 777-781
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2011781303
- Cited by
- 3,825
- References
- 10
- Citations by year
- 2026: 128 · 2025: 171 · 2024: 194 · 2023: 209 · 2022: 259 · 2021: 323 · 2020: 274 · 2019: 286 · 2018: 238 · 2017: 198 · 2016: 219 · 2015: 228 · 2014: 194 · 2013: 171 · 2012: 142
- FWCI
- 5.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- ScholarWorks@UMassAmherst (University of Massachusetts Amherst) (submittedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Complementary and alternative medicine › Cardiovascular and exercise physiology
- Keywords
- accelerometer calibration, wearable activity trackers, exercise intensity classification, energy expenditure estimation, treadmill exercise, oxygen consumption, body mass, physical activity monitoring
- MeSH terms
- Adult, Calibration, Energy Metabolism, Exercise Test, Female, Humans, Male, Physical Fitness, Reference Values
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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