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A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring
Open accessAbstract
Characteristics of physical activity are indicative of one’s mobility level, latent chronic diseases and aging process. Accelerometers have been widely accepted as useful and practical sensors for wearable devices to measure and assess physical activity. This paper reviews the development of wearable accelerometry-based motion detectors. The principle of accelerometry measurement, sensor properties and sensor placements are first introduced. Various research using accelerometry-based wearable motion detectors for physical activity monitoring and assessment, including posture and movement classification, estimation of energy expenditure, fall detection and balance control evaluation, are also reviewed. Finally this paper reviews and compares existing commercial products to provide a comprehensive outlook of current development status and possible emerging technologies.
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“A centrally located sensor on the torso (embedded into vests, mounted by straps or attached to the skin) is best suited for detecting posture, acceleration and whole-body movements and offers the least errors compared with other body locations7.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [7]
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“These devices were selected based on review of sales data in 3 categories of wearable devices—smart watches, basic wearable bands, and portable navigation devices [13-16].”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [14]
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Metadata
- Authors
- Yang, C.-C., & Hsu, Y.-L.
- Affiliations
- Yuan Ze University (TW)
- Corresponding author
- Hsu, Yeh-Liang
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2010-08-20
- Volume
- 10
- Issue
- 8
- Pages
- 7772-7788
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC3231187
- OpenAlex
- W2083539525
- Cited by
- 1,058
- References
- 91
- Citations by year
- 2026: 43 · 2025: 51 · 2024: 51 · 2023: 55 · 2022: 59 · 2021: 71 · 2020: 117 · 2019: 83 · 2018: 113 · 2017: 88 · 2016: 80 · 2015: 67 · 2014: 69 · 2013: 60 · 2012: 32
- FWCI
- 4.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- accelerometer, wearable devices, physical activity monitoring, motion detection, posture recognition, movement classification, energy expenditure estimation, fall detection, balance control, sensor placement
- MeSH terms
- Acceleration, Energy Metabolism, Equipment Design, Humans, Motor Activity, Reproducibility of Results, Exercise, Monitoring, Ambulatory, Wireless Technology
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 ›
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