Cited by 3,825Physical Activity and HealthContext-Aware Activity Recognition SystemsCardiovascular and exercise physiology

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Calibration of the Computer Science and Applications, Inc. accelerometer

FREEDSON, PATTY S., MELANSON, EDWARD, SIRARD, JOHN·1998·Medicine & Science in Sports & Exercise

DOI 10.1097/00005768-199805000-00021

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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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Authors: FREEDSON, P. S., MELANSON, E., & SIRARD, J.
Affiliations: University of Massachusetts Amherst (US)
ORCID: FREEDSON, PATTY S.
MELANSON, EDWARD
SIRARD, JOHN
Venue: Medicine & Science in Sports & Exercise
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 1998-05
Volume: 30
Issue: 5
Pages: 777-781
ISSN: 0195-9131 (ISSN-L)
DOI: 10.1097/00005768-199805000-00021
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 › 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 › Physiology (medical) › 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

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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.

What counts as a claim ›How we measure confidence ›How we check this ›

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Calibration of the Computer Science and Applications, Inc. accelerometer

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