CoChat
Sign Up
Cited by 996Physical Activity and HealthContext-Aware Activity Recognition SystemsObesity, Physical Activity, Diet

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

CHEN, KONG Y., BASSETT, DAVID R.·2005·Medicine & Science in Sports & Exercise
DOI 10.1249/01.mss.0000185571.49104.82
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1249/01.mss.0000185571.49104.82

Cites

61 of 61 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Cited by

Sorted by citations

See all 996

Related

See all

Metadata

Authors
CHEN, K. Y., & BASSETT, D. R.
Affiliations
  • Vanderbilt University (US)
  • University of Tennessee at Knoxville (US)
ORCID
Venue
Medicine & Science in Sports & Exercise
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2005-11
Volume
37
Issue
11
Pages
S490-S500
ISSN
0195-9131 (ISSN-L)
DOI
10.1249/01.mss.0000185571.49104.82
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

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.

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

Are you an author, or do you see a problem with this page? Request a review