The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review
Open accessAbstract
Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Cochrane Handbook for Systematic Reviews of Interventions: Cochrane Book Series
(2008) · doi:10.1002/9780470712184 · Cited by 25,048
Measuring orientation of human body segments using miniature gyroscopes and accelerometers
Luinge, H. J., & Veltink, P. H. (2005) · Medical & Biological Engineering & Computing · doi:10.1007/bf02345966 · Cited by 647
Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing
Sabatini, A. M. (2011) · Sensors · doi:10.3390/s110201489 · Cited by 349
Advances in the application of information technology to sport performance
Liebermann, D. G., Katz, L., Hughes, M. D., Bartlett, R. M., McClements, J., & Franks, I. M. (2002) · Journal of Sports Sciences · doi:10.1080/026404102320675611 · Cited by 338
The relationship between maximal jump-squat power and sprint acceleration in athletes
Sleivert, G., & Taingahue, M. (2004) · European Journal of Applied Physiology · doi:10.1007/s00421-003-0941-0 · Cited by 278
Biomechanics feedback for rowing
Smith, R. M., & Loschner, C. (2002) · Journal of Sports Sciences · doi:10.1080/026404102320675639 · Cited by 143
McNamara, D. J., Gabbett, T. J., Chapman, P., Naughton, G., & Farhart, P. (2015) · International Journal of Sports Physiology and Performance · doi:10.1123/ijspp.2014-0062 · Cited by 69
Reliability and validity of instrumented soccer equipment
Akins, J. S., Heebner, N. R., Lovalekar, M., & Sell, T. C. (2015) · Journal of Applied Biomechanics · doi:10.1123/jab.2014-0191 · Cited by 17
Cited by
Deep learning tools for the measurement of animal behavior in neuroscience
Mathis, M. W., & Mathis, A. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.10.008 · Cited by 450
Exploring the Role of Wearable Technology in Sport Kinematics and Kinetics: A Systematic Review
Adesida, Y., Papi, E., & McGregor, A. H. (2019) · Sensors · doi:10.3390/s19071597 · Cited by 288
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives
Mathis, A., Schneider, S., Lauer, J., & Mathis, M. W. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.017 · Cited by 231
Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning
Stetter, B. J., Ringhof, S., Krafft, F. C., Sell, S., & Stein, T. (2019) · Sensors · doi:10.3390/s19173690 · Cited by 130
Cereatti, A., Gurchiek, R., Mündermann, A., Fantozzi, S., Horak, F., Delp, S., & Aminian, K. (2024) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2024.112225 · Cited by 79
Self-citationScardulla, F., D’Acquisto, L., Colombarini, R., Hu, S., Pasta, S., & Bellavia, D. (2020) · Sensors · doi:10.3390/s20185052 · Cited by 72
On-field player workload exposure and knee injury risk monitoring via deep learning
Johnson, W. R., Mian, A., Lloyd, D. G., & Alderson, J. A. (2019) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2019.07.002 · Cited by 69
Doherty, C., Baldwin, M., Lambe, R., Altini, M., & Caulfield, B. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01757-1 · Cited by 55
He, X., & Wei, L. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1661936 · Cited by 11
Muscles in Motion: Wearables for Sports and Fitness
Upadhyay, P., Mittal, V., & Dass, R. (2025) · Smart Textiles and Wearables for Health and Fitness · doi:10.1002/9781394302895.ch12 · Cited by 1
Metadata
- Authors
- Camomilla, V., Bergamini, E., Fantozzi, S., & Vannozzi, G.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-03-15
- Volume
- 18
- Issue
- 3
- Pages
- 873
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2793667086
- Cited by
- 591
- References
- 9
- FWCI
- 4.05
- Type
- journal article
- Language
- en
- Open access
- gold
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.