Thesis
On-field respiratory monitoring techniques to track performance and exertion of team-sports athletes
- Abstract:
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Participation in sports has become an essential part of healthy living in today’s world. And with advancements in sensor technology and data analytics over the years, many team sports have turned to technology-aided, data-driven, on-field monitoring techniques to help players optimize their games and training. With the adoption of sensing technology, there is a growing interest in capturing fatigue and injury risk for the purpose of preventing injuries and improving overall athlete well-being...
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- Files:
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(Preview, Dissemination version, pdf, 28.9MB, Terms of use)
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Authors
Contributors
+ Bergmann, J
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Engineering Science
- Role:
- Supervisor
- ORCID:
- 0000-0001-7306-2630
+ Savaget, P
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Engineering Science
- Role:
- Supervisor
- ORCID:
- 0000-0001-7780-3010
+ Villarroel Montoya, M
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Engineering Science
- Role:
- Examiner
- ORCID:
- 0000-0003-4787-6053
+ Preece, S
- Institution:
- University of Salford
- Role:
- Examiner
- ORCID:
- 0000-0002-2434-732X
+ Biotechnology and Biological Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/00cwqg982
- Funding agency for:
- Bergmann, J
- Grant:
- UKRI012 Bioguard
- Programme:
- Follow-on Fund
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Funding agency for:
- Bergmann, J
- Grant:
- EP/R511742/1
- Programme:
- Impact Acceleration Grant
- DOI:
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
- Language:
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English
- Keywords:
- Subjects:
- Deposit date:
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2026-08-08
- ARK identifier:
Terms of use
- Copyright holder:
- Runbei Cheng
- Copyright date:
- 2026
- Notes:
- Feature importance for estimating rating of perceived exertion from cardiorespiratory signals using machine learning, Impact and workload are dominating on-field data monitoring techniques to track health and well-being of team-sports athletes, Applying ubiquitous sensing to estimate perceived exertion based on cardiorespiratory features, and A case for an oral cavity based respiratory rate sensor system are derived from this thesis.
- Licence:
- CC Attribution (CC BY)
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