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https://github.com/firefly-cpp/awesome-computational-intelligence-in-sports
A collection of literature on the use of computational intelligence methods in sports
https://github.com/firefly-cpp/awesome-computational-intelligence-in-sports
List: awesome-computational-intelligence-in-sports
artificial-intelligence awesome awesome-list computational-intelligence sport-analytics sport-application sport-science sports
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A collection of literature on the use of computational intelligence methods in sports
- Host: GitHub
- URL: https://github.com/firefly-cpp/awesome-computational-intelligence-in-sports
- Owner: firefly-cpp
- License: cc-by-sa-4.0
- Created: 2021-11-12T11:34:30.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-05-03T20:28:31.000Z (6 months ago)
- Last Synced: 2024-05-22T23:01:17.834Z (5 months ago)
- Topics: artificial-intelligence, awesome, awesome-list, computational-intelligence, sport-analytics, sport-application, sport-science, sports
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- Watchers: 3
- Forks: 4
- Open Issues: 2
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Metadata Files:
- Readme: README.md
- Contributing: contributing.md
- License: LICENSE
- Citation: CITATION.cff
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README
# Awesome Computational Intelligence in Sports [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10431418.svg)](https://doi.org/10.5281/zenodo.10431418)
---
We are curating **awesome** research and approaches to **CI in Sports**!
This repository serves as a list of knowledge for researchers working in Computational Intelligence in Sports. The list mainly comprises methods based on evolutionary algorithms, artificial neural networks, fuzzy systems, and swarm intelligence algorithms[^1]. The research citations were done with Mendeley in the MLA 8th edition format. The list includes books, scientific literature, datasets, and software from Computational Intelligence in Sports.
[^1]: Several included research papers are only partially based on these methods but are essential, especially for interdisciplinary research.
## Contents
- [Books π](#books-)
- [Review papers π](#review-papers-)
- [Research papers π¬](#research-papers-)
- [Dissertation or thesis π](#dissertation-or-thesis-)
- [Tutorials π](#tutorials-)
- [Perspectives π°](#perspectives-)
- [Datasets π](#datasets-)
- [Basketball π](#basketball-)
- [Cycling π²](#cycling-)
- [Soccer β½](#soccer-%EF%B8%8F)
- [Track and field πβ](#track-and-field-)
- [Triathlon π₯](#triathlon-)
- [Wrestling π€ΌββοΈ](#wrestling-%EF%B8%8F)
- [Benchmarks π§ͺ](#benchmarks-)
- [Software π»](#software-)
- [Web applications π](#web-applications-)## Books π
- Begg, Rezaul, and Marimuthu Palaniswami. β[Computational Intelligence for Movement Sciences](https://www.igi-global.com/book/computational-intelligence-movement-sciences/178).β Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques, edited by Rezaul Begg and Marimuthu Palaniswami, IGI Global, 2006, doi:[10.4018/978-1-59140-836-9](https://doi.org/10.4018/978-1-59140-836-9).
- Fister, Iztok, et al. β[Computational intelligence in sports](https://link.springer.com/book/10.1007%2F978-3-030-03490-0).β Edited by Yew Lim, Meng-Hiot Soon Ong, vol. 22, Springer International Publishing, 2019, doi:[10.1007/978-3-030-03490-0](https://doi.org/10.1007/978-3-030-03490-0).
## Review papers π
- Beal, Ryan, et al. β[Artificial intelligence for team sports: a survey](https://www.cambridge.org/core/journals/knowledge-engineering-review/article/abs/artificial-intelligence-for-team-sports-a-survey/2E0E32861D031C022603F670B23B55B3).β The Knowledge Engineering Review, vol. 34, Cambridge University Press, 2019, doi:[10.1017/S0269888919000225](https://doi.org/10.1017/S0269888919000225).
- Bonidia, Robson P., et al. β[Data Mining in Sports: A Systematic Review](https://ieeexplore.ieee.org/abstract/document/8291478/).β IEEE Latin America Transactions, vol. 16, no. 1, IEEE Computer Society, Jan. 2018, pp. 232β39, doi:[10.1109/TLA.2018.8291478](https://doi.org/10.1109/TLA.2018.8291478).
- Bonidia, Robson P., et al. β[Computational Intelligence in Sports: A Systematic Literature Review](https://www.hindawi.com/journals/ahci/2018/3426178/).β Advances in Human-Computer Interaction, vol. 2018, Hindawi Limited, Oct. 2018, pp. 1β13, doi:[10.1155/2018/3426178](https://doi.org/10.1155/2018/3426178).
- Bunker, Rory, and Teo Susnjak. β[The Application of Machine Learning Techniques for Predicting Match Results in Team Sport: A Review](https://www.jair.org/index.php/jair/article/view/13509).β Journal of Artificial Intelligence Research, vol. 73, AI Access Foundation, Apr. 2022, pp. 1285β322, doi:[10.1613/JAIR.1.13509](https://doi.org/10.1613/JAIR.1.13509).
- Cardenas Hernandez, Fernando Pedro, et al. β[Beyond Hard Workout: A Multimodal Framework for Personalised Running Training with Immersive Technologies](https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13445).β British Journal of Educational Technology, doi:[10.1111/bjet.13445](https://doi.org/10.1111/bjet.13445).
- Farrokhi, Alireza, et al. β[Application of Internet of Things and Artificial Intelligence for Smart Fitness: A Survey](https://www.sciencedirect.com/science/article/abs/pii/S1389128621000360).β Computer Networks, vol. 189, Elsevier, Apr. 2021, p. 107859, doi:[10.1016/j.comnet.2021.107859](https://doi.org/10.1016/j.comnet.2021.107859).
- Fister Jr, Iztok, et al. β[Computational Intelligence in Sports: Challenges and Opportunities within a New Research Domain](https://www.sciencedirect.com/science/article/abs/pii/S0096300315004300).β Applied Mathematics and Computation, vol. 262, Elsevier, July 2015, pp. 178β86, doi:[10.1016/j.amc.2015.04.004](https://doi.org/10.1016/j.amc.2015.04.004).
- Frangoudes, Fotos, et al. β[Assessing Human Motion During Exercise Using Machine Learning: A Literature Review](https://ieeexplore.ieee.org/abstract/document/9857881).β IEEE Access, vol. 10, 2022, pp. 86874β903, doi:[10.1109/ACCESS.2022.3198935](https://doi.org/10.1109/ACCESS.2022.3198935).
- GΓ‘mez DΓaz, R.; Yu, Q.; Ding, Y.; Laamarti, F.; El Saddik, A. β[Digital Twin Coaching for Physical Activities: A Survey](https://www.mdpi.com/1424-8220/20/20/5936).β Sensors 2020, 20, 5936, doi:[10.3390/s20205936](https://doi.org/10.3390/s20205936).
- H. Pascual, X. M. Bruin, A. Alonso, J. Cerd`a, β[A systematic review on human modeling: Digging into human digital twin implementations](https://arxiv.org/abs/2302.03593).β, arXiv preprint, doi:[arXiv:2302.03593](https://doi.org/10.48550/arXiv.2302.03593).
- KrstiΔ, DuΕ‘an, et al. β[The Application and Impact of Artificial Intelligence on Sports Performance Improvement: A Systematic Literature Review](https://ieeexplore.ieee.org/abstract/document/10378750).β 2023 4th International Conference on Communications, Information, Electronic and Energy Systems (CIEES), IEEE, 2023, pp. 1β8, doi:[10.1109/CIEES58940.2023.10378750](https://doi.org/10.1109/CIEES58940.2023.10378750).
- Lai, Daniel T. H., et al. β[Computational Intelligence in Gait Research: A Perspective on Current Applications and Future Challenges](https://ieeexplore.ieee.org/abstract/document/4915787).β IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 5, 2009, pp. 687β702, doi:[10.1109/TITB.2009.2022913](https://doi.org/10.1109/TITB.2009.2022913).
- Lygouras, Dimosthenis, and Avgoustos Tsinakos. β[The Use of Immersive Technologies in Karate Training: A Scoping Review](https://www.mdpi.com/2414-4088/8/4/27).β Multimodal Technologies and Interaction, vol. 8, no. 4, 2024, [doi:10.3390/mti8040027](https://doi.org/10.3390/mti8040027).
- Milasi, Sadegh Fatahi, et al. β[Unlocking the Potential: A Comprehensive Meta-Synthesis of Internet of Things in the Sports Industry](https://journals.sagepub.com/doi/10.1177/17543371241229521).β Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, vol., no., p. 17543371241229520, doi:[10.1177/17543371241229521](https://doi.org/10.1177/17543371241229521).
- Nalbant, Kemal GΓΆkhan, and Sevgi AydΔ±n. β[Literature Review on the Relationship between Artificial Intelligence Technologies with Digital Sports Marketing and Sports Management](https://ejournal.unma.ac.id/index.php/ijsm/article/view/2876).β Indonesian Journal of Sport Management, vol. 2, no. 2, Oct. 2022, pp. 135β43, doi:[10.31949/ijsm.v2i2.2876](https://doi.org/10.31949/ijsm.v2i2.2876).
- RajΕ‘p, Alen, and Iztok Jr. Fister. β[A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training](https://www.mdpi.com/2076-3417/10/9/3013).β Applied Sciences, vol. 10, no. 9, Multidisciplinary Digital Publishing Institute, Apr. 2020, p. 3013, doi:[10.3390/app10093013](https://doi.org/10.3390/app10093013).
- Song, Yu (Wolf). β[Human Digital Twin, the Development and Impact on Design](https://asmedigitalcollection.asme.org/computingengineering/article/23/6/060819/1166203/Human-Digital-Twin-the-Development-and-Impact-on).β Journal of Computing and Information Science in Engineering, vol. 23, no. 6, Dec. 2023, doi:[10.1115/1.4063132](https://doi.org/10.1115/1.4063132).
- Stessens, Loes, et al. β[Physical Performance Estimation in Practice: A Systematic Review of Advancements in Performance Prediction and Modeling in Cycling](https://journals.sagepub.com/doi/10.1177/17479541241262385).β International Journal of Sports Science & Coaching, vol., no., p. 17479541241262384, doi:[10.1177/17479541241262385](https://doi.org/10.1177/17479541241262385).
- Szot, Tomasz. β[Evolution of Sport Wearable Global Navigation Satellite Systemsβ Receivers: A Look at the Garmin Forerunner Series](https://journals.sagepub.com/doi/10.1177/17543371241237319).β Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, doi:[10.1177/17543371241237319](https://doi.org/10.1177/17543371241237319).
- Wakelam, Edward, et al. β[The Collection, Analysis and Exploitation of Footballer Attributes: A Systematic Review](https://content.iospress.com/articles/journal-of-sports-analytics/jsa200554).β Journal of Sports Analytics, vol. 8, no. 1, IOS Press, Jan. 2022, pp. 31β67, doi:[10.3233/JSA-200554](https://doi.org/10.3233/JSA-200554).
- Yang, Luyao, et al. β[Intelligent Wearable Systems: Opportunities and Challenges in Health and Sports](https://dl.acm.org/doi/pdf/10.1145/3648469).β ACM Comput. Surv., vol. 56, no. 7, Association for Computing Machinery, Apr. 2024, [doi:10.1145/3648469](https://doi.org/10.1145/3648469).
## Research papers π¬
- Adeyemo, Victor Elijah, et al. β[Identification of Pattern Mining Algorithm for Rugby League Players Positional Groups Separation Based on Movement Patterns](http://arxiv.org/abs/2302.14058).β ArXiv, Feb. 2023, p. 2023, http://arxiv.org/abs/2302.14058.
- Ariyaratne, M. K. A., and R. M. Silva. β[Meta-Heuristics Meet Sports: A Systematic Review from the Viewpoint of Nature Inspired Algorithms](https://sciendo.com/article/10.2478/ijcss-2022-0003).β International Journal of Computer Science in Sport, vol. 21, no. 1, Mar. 2022, pp. 49β92, doi:[10.2478/ijcss-2022-0003](https://doi.org/10.2478/ijcss-2022-0003).
- Attigala, D. A., et al. β[Intelligent Trainer for Athletes Using Machine Learning](https://ieeexplore.ieee.org/abstract/document/8940477).β 2019 International Conference on Computing, Power and Communication Technologies (GUCON), 2019, pp. 898β903.
- Barshan, Billur, and M. C. Yuksek. β[Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units](https://ieeexplore.ieee.org/abstract/document/8130901).β The Computer Journal, vol. 57, no. 11, Oxford University Press, Nov. 2014, pp. 1649β67, doi:[10.1093/comjnl/bxt075](https://doi.org/10.1093/comjnl/bxt075).
- Boillet, Alice, Laurent A. Messonnier, and Caroline Cohen. "[Individualized physiology-based digital twin model for sports performance prediction: a reinterpretation of the MargariaβMorton model](https://www.nature.com/articles/s41598-024-56042-0#Sec2)." Scientific Reports 14, no. 1 (2024): 5470, doi:[10.1038/s41598-024-56042-0](https://doi.org/10.1038/s41598-024-56042-0).
- Carey, David L., et al. β[Optimizing Preseason Training Loads in Australian Football](https://journals.humankinetics.com/view/journals/ijspp/13/2/article-p194.xml).β International Journal of Sports Physiology and Performance, vol. 13, no. 2, Human Kinetics, Feb. 2018, pp. 194β99, doi:[10.1123/ijspp.2016-0695](https://doi.org/10.1123/ijspp.2016-0695).
- Chacoma, AndrΓ©s, and Orlando V Billoni. β[Simple Mechanism Rules the Dynamics of Volleyball](http://arxiv.org/abs/2202.13765).β ArXiv, Feb. 2022, http://arxiv.org/abs/2202.13765.
- Chen, Shuxi, et al. β[Detecting Sports Fatigue from Speech by Support Vector Machine](https://ieeexplore.ieee.org/abstract/document/7586626).β 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), IEEE, 2016, pp. 96β99, doi:[10.1109/ICCSN.2016.7586626](https://doi.org/10.1109/ICCSN.2016.7586626).
- Cintia, Paolo, and Luca Pappalardo. β[Coach2vec: Autoencoding the Playing Style of Soccer Coaches](https://arxiv.org/abs/2106.15444)β. Arxiv, June 2021, doi:[10.48550/arxiv.2106.15444](https://doi.org/10.48550/arxiv.2106.15444). Preprint.
- Connor, Mark, et al. β[Optimising Team Sport Training Plans with Grammatical Evolution](https://ieeexplore.ieee.org/abstract/document/8790369).β 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., June 2019, pp. 2474β81, doi:[10.1109/CEC.2019.8790369](https://doi.org/10.1109/CEC.2019.8790369).
- Connor, Mark, et al. β[Adaptive Athlete Training Plan Generation: An Intelligent Control Systems Approach](https://www.sciencedirect.com/science/article/pii/S1440244021004679).β Journal of Science and Medicine in Sport, vol. 25, no. 4, Elsevier, Apr. 2022, pp. 351β55, doi:[10.1016/j.jsams.2021.10.011](https://doi.org/10.1016/j.jsams.2021.10.011).
- De Prisco, Roberto, et al. β[Providing Music Service in Ambient Intelligence: experiments with gym users](https://www.sciencedirect.com/science/article/abs/pii/S0957417421003924).β Expert Systems with Applications, vol. 177, Pergamon, Sept. 2021, p. 114951, doi:[10.1016/j.eswa.2021.114951](https://doi.org/10.1016/j.eswa.2021.114951).
- Deng, Huijian, et al. β[Prediction of Sports Aggression Behavior and Analysis of Sports Intervention Based on Swarm Intelligence Model](https://www.hindawi.com/journals/sp/2022/2479939/).β Scientific Programming, vol. 2022, Hindawi Limited, 2022, doi:[10.1155/2022/2479939](https://doi.org/10.1155/2022/2479939).
- DΓaz, Rogelio GΓ‘mez, Fedwa Laamarti, and Abdulmotaleb El Saddik. "[DTCoach: your digital twin coach on the edge during COVID-19 and beyond](https://ieeexplore.ieee.org/abstract/document/9513635)." IEEE Instrumentation & Measurement Magazine 24, no. 6 (2021): 22-28, doi:[10.1109/MIM.2021.9513635](https://doi.org/10.1109/MIM.2021.9513635).
- Ding, Xianqiong, et al. β[Sports Training Model Based on GA Optimized Neural Network](https://ieeexplore.ieee.org/abstract/document/9526701).β Proceedings - 2020 13th International Conference on Intelligent Computation Technology and Automation, ICICTA 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 227β30, doi:[10.1109/ICICTA51737.2020.00055](https://doi.org/10.1109/ICICTA51737.2020.00055).
- Eriksson, Rikard, et al. β[Generating Weekly Training Plans in the Style of a Professional Swimming Coach Using Genetic Algorithms and Random Trees](https://link.springer.com/chapter/10.1007/978-3-030-99333-7_9).β Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference, edited by Arnold Baca et al., Springer, Cham, 2022, pp. 61β68, doi:[10.1007/978-3-030-99333-7_9](https://doi.org/10.1007/978-3-030-99333-7_9).
- Farrokhi, Alireza, et al. β[A Decision Tree-Based Smart Fitness Framework in IoT](https://link.springer.com/article/10.1007/s42979-021-00940-x).β SN Computer Science, vol. 3, no. 1, Springer, Jan. 2022, p. 2, doi:[10.1007/s42979-021-00940-x](https://doi.org/10.1007/s42979-021-00940-x).
- Feely, Ciara, et al. β[A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries](<(https://link.springer.com/chapter/10.1007/978-3-030-86957-1_6)>).β Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12877 LNAI, Springer, Cham, 2021, pp. 79β93, doi:[10.1007/978-3-030-86957-1_6](https://doi.org/10.1007/978-3-030-86957-1_6).
- Feely, Ciara, et al. β[Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations](https://dl.acm.org/doi/pdf/10.1145/3565472.3592952).β Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, ACM, 2023, pp. 183β93, doi:[10.1145/3565472.3592952](https://doi.org/10.1145/3565472.3592952).
- Ferencsik, Dorina K., and Erika B. Varga. β[Cycling Activity Dataset Creation and Application for Feedback Giving](https://sciendo.com/article/10.2478/amset-2021-0015).β Acta Marisiensis. Seria Technologica, vol. 18, no. 2, Walter de Gruyter GmbH, Dec. 2021, pp. 29β35, doi:[10.2478/AMSET-2021-0015](https://doi.org/10.2478/amset-2021-0015).
- Fialho, Gabriel, et al. β[Predicting Sports Results with Artificial Intelligence β A Proposal Framework for Soccer Games](https://www.sciencedirect.com/science/article/pii/S1877050919322033).β Procedia Computer Science, vol. 164, Elsevier, Jan. 2019, pp. 131β36, doi:[10.1016/j.procs.2019.12.164](https://doi.org/10.1016/j.procs.2019.12.164).
- Fidelis, J. Vijay, and E. Karthikeyan. β[Player Management in Soccer Using Particle Swarm Optimization](https://ieeexplore.ieee.org/abstract/document/9114599).β 4th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques, ICEECCOT 2019, Institute of Electrical and Electronics Engineers Inc., Dec. 2019, pp. 303β08, doi:[10.1109/ICEECCOT46775.2019.9114599](https://doi.org/10.1109/ICEECCOT46775.2019.9114599).
- Fister, DuΕ‘an, et al. β[Visualization of cycling training](http://www.iztok-jr-fister.eu/static/publications/160.pdf).β Proceedings of the StuCoSReC: 3rd Student Computer Science Research Conference, Koper, Slovenia. 2016.
- Fister, Iztok, et al. β[Framework for Planning the Training Sessions in Triathlon](https://dl.acm.org/doi/abs/10.1145/3205651.3208242).β Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM, 2018, pp. 1829β34, doi:[10.1145/3205651.3208242](https://doi.org/10.1145/3205651.3208242).
- Fister, Iztok, et al. β[Planning the Sports Training Sessions with the Bat Algorithm](https://www.sciencedirect.com/science/article/abs/pii/S0925231214009710).β Neurocomputing, vol. 149, no. PB, Elsevier, Feb. 2015, pp. 993β1002, doi:[10.1016/J.NEUCOM.2014.07.034](https://doi.org/10.1016/j.neucom.2014.07.034).
- Fister, Iztok, et al. β[Synthetic Data Augmentation of Cycling Sport Training Datasets](https://link.springer.com/chapter/10.1007/978-3-030-93247-3_7).β Lecture Notes in Networks and Systems, vol. 371, Springer Science and Business Media Deutschland GmbH, 2022, pp. 65β74, doi:[10.1007/978-3-030-93247-3_7](https://doi.org/10.1007/978-3-030-93247-3_7).
- Fister Jr., Iztok. β[The Relevance of Nature-Inspired Metaheuristic Algorithms in Smart Sport Training](https://link.springer.com/chapter/10.1007/978-3-030-80216-5_1).β International Conference on Emerging Applications and Technologies for Industry 4.0 (EATI'2020), edited by Jemal H Abawajy et al., Springer International Publishing, 2021, pp. 1β8, doi:[10.1007/978-3-030-80216-5_1](https://doi.org/10.1007/978-3-030-80216-5_1).
- Fister Jr., Iztok., et al. β[Adaptation of Sport Training Plans by Swarm Intelligence](https://link.springer.com/chapter/10.1007/978-3-319-97888-8_5).β Recent Advances in Soft Computing, edited by Radek MatouΕ‘ek, Springer International Publishing, 2019, pp. 56β67, doi:[10.1007/978-3-319-97888-8_5](https://doi.org/10.1007/978-3-319-97888-8_5).
- Fister Jr, Iztok, et al. β[New Perspectives in the Development of the Artificial Sport Trainer](https://www.mdpi.com/2076-3417/11/23/11452/htm).β Applied Sciences, vol. 11, no. 23, Multidisciplinary Digital Publishing Institute, Dec. 2021, p. 11452, doi:[10.3390/app112311452](https://doi.org/10.3390/app112311452).
- Fister Jr, Iztok , et al. β[SportyDataGen: An Online Generator of Endurance Sports Activity Collections](http://www.iztok-jr-fister.eu/static/publications/225.pdf).β Proceedings of the Central European Conference on Information and Intelligent Systems, Faculty of Organization and Informatics, University of Zagreb, 2018, pp. 171β78.
- Fister Jr, Iztok, et al. β[The Importance of Monitoring and Maintaining Data in Sports Training Process](https://www.iztok-jr-fister.eu/static/publications/191.pdf).β Proceedings of the 8th Conference for Youth Sport, 2016.
- Fister Jr, Iztok, et al. β[Topology-Based Generation of Sport Training Sessions](https://link.springer.com/article/10.1007/s12652-020-02048-1).β Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, Springer Science and Business Media Deutschland GmbH, Jan. 2021, pp. 667β78, doi:[10.1007/s12652-020-02048-1](https://doi.org/10.1007/s12652-020-02048-1).
- Fister Jr, Iztok, et al. β[On Deploying the Artificial Sport Trainer into Practice](10.1109/ISCMI53840.2021.9654817).β 2021 8th International Conference on Soft Computing and Machine Intelligence, ISCMI 2021, Institute of Electrical and Electronics Engineers Inc., Sept. 2021, pp. 21β26, doi:[ISCMI53840.2021.9654817](https://doi.org/ISCMI53840.2021.9654817).
- Fister Jr, Iztok, and Iztok Fister. β[Generating the Training Plans Based on Existing Sports Activities Using Swarm Intelligence](https://link.springer.com/chapter/10.1007/978-3-319-50920-4_4).β Modeling and Optimization in Science and Technologies, vol. 10, Springer, Cham, 2017, pp. 79β94, doi:[10.1007/978-3-319-50920-4_4](https://doi.org/10.1007/978-3-319-50920-4_4).
- Fister Jr, Iztok, et al. β[Population-Based Metaheuristics for Planning Interval Training Sessions in Mountain Biking](https://link.springer.com/chapter/10.1007/978-3-030-26369-0_7).β Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11655 LNCS, Springer, Cham, 2019, pp. 70β79, doi:[10.1007/978-3-030-26369-0_7](https://doi.org/10.1007/978-3-030-26369-0_7).
- Fister Jr, Iztok, et al. β[Discovering Dependencies among Mined Association Rules with Population-Based Metaheuristics](https://dl.acm.org/doi/abs/10.1145/3319619.3326833).β Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM, 2019, pp. 1668β74, doi:[10.1145/3319619.3326833](https://doi.org/10.1145/3319619.3326833).
- Frevel, Nicolas, et al. β[The Impact of Technology on Sports β A Prospective Study](https://www.sciencedirect.com/science/article/abs/pii/S0040162522003626?via%3Dihub).β Technological Forecasting and Social Change, vol. 182, Sept. 2022, p. 121838. ScienceDirect, doi:[10.1016/j.techfore.2022.121838](https://doi.org/10.1016/j.techfore.2022.121838).
- Hrovat, Goran, et al. β[Interestingness Measure for Mining Sequential Patterns in Sports](https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs1676).β Journal of Intelligent \& Fuzzy Systems, vol. 29, no. 5, Jan. 2015, pp. 1981β94, doi:[10.3233/IFS-151676](https://doi.org/10.3233/IFS-151676).
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## Dissertation or thesis π
- GΓ‘mez DΓaz, Rogelio. "[Digital Twin Coaching for Edge Computing Using Deep Learning Based 2D Pose Estimation](https://ruor.uottawa.ca/items/7b1bdb69-dc2e-4602-9566-d811ee67f39f)." PhD diss., UniversitΓ© d'Ottawa/University of Ottawa, 2021, doi:[10.20381/ruor-26229](http://dx.doi.org/10.20381/ruor-26229).
- Laamarti, Fedwa. "[Towards Standardized Digital Twins for Health, Sport, and Well-being](https://ruor.uottawa.ca/items/3736b7d6-8512-4f6e-bc91-962ab4d1d4f6)." PhD diss., UniversitΓ© d'Ottawa/University of Ottawa, 2019, doi:[10.20381/ruor-23746](http://dx.doi.org/10.20381/ruor-23746).
- Murillo Burford, Esteban. β[Predicting Cycling Performance Using Machine Learning](https://www.proquest.com/openview/f0f80c5fd5c48f67edd9b7f3a375563d/).β Wake Forest University Graduate School of Arts and Sciences, 2020.
- Eriksson, Rikard, and Johan Nicander. β[Automated Generation of Training Programs for Swimmers Generating Weekly Training Plans in the Style of a Professional Swimming Coach Using Genetic Algorithms and Random Trees](https://odr.chalmers.se/handle/20.500.12380/302927).β Chalmers University of Technology, 2021, doi:[20.500.12380/302927](https://doi.org/20.500.12380/302927).
## Tutorials π
- Bock, Marius, et al. β[Tutorial on Deep Learning for Human Activity Recognition](https://arxiv.org/abs/2110.06663).β Oct. 2021, doi:[10.48550/arxiv.2110.06663](https://doi.org/10.48550/arxiv.2110.06663).
## Perspectives π°
- Chmait, Nader, and Hans Westerbeek. β[Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-Data Scientists](https://www.frontiersin.org/articles/10.3389/fspor.2021.682287).β Frontiers in Sports and Active Living, vol. 3, Frontiers Media S.A., Dec. 2021, p. 363, doi:[10.3389/fspor.2021.682287](https://doi.org/10.3389/fspor.2021.682287).
## Datasets π
- Rico-Garcia, Mateo, et al. β[Vertical Jump Data from Inertial and Optical Motion Tracking Systems](https://www.mdpi.com/2306-5729/7/8/116).β Data, vol. 7, no. 8, Aug. 2022, p. 116, doi:[10.3390/data7080116](https://doi.org/10.3390/data7080116).
- Romagnoli, Sofia, et al. β[Sport DB 2.0: A New Database of Data Acquired by Wearable and Portable Devices While Practicing Sport](https://ieeexplore.ieee.org/abstract/document/10364234).β 2023 Computing in Cardiology (CinC), vol. 50, 2023, pp. 1β4, doi:[10.22489/CinC.2023.067](https://doi.org/10.22489/CinC.2023.067).### Basketball π
- Hoelzemann, Alexander, et al. β[Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors](https://arxiv.org/pdf/2305.13124.pdf).β ArXiv, May 2023, doi:[10.48550/arXiv.2305.13124](https://doi.org/10.48550/arXiv.2305.13124).
### Cycling π²
- Fister Jr., et al. β[A collection of sport activity datasets for data analysis and data mining 2017a](http://iztok-jr-fister.eu/static/publications/Sport4.zip).β Technical report 2017a, University of Maribor, 2017
- Iztok Fister Jr., Samo Rauter, DuΕ‘an Fister, Iztok Fister. β[A collection of sport activity datasets with an emphasis on powermeter data](http://iztok-jr-fister.eu/static/publications/Sport5.zip).β Technical report, University of Maribor, November 2017
- RajΕ‘p, Alen, and Iztok Fister Jr. β[Neo4j Graph Dataset of Cycling Paths in Slovenia](https://www.sciencedirect.com/science/article/pii/S2352340923003700).β Data in Brief, vol. 48, June 2023, p. 109251, doi:[10.1016/j.dib.2023.109251](https://doi.org/10.1016/j.dib.2023.109251).
- Samo Rauter, Iztok Fister Jr., Iztok Fister. β[A collection of sport activity files for data analysis and data mining 2016a](http://iztok-jr-fister.eu/static/css/datasets/Sport2.zip).β Technical report 0101, University of Ljubljana and University of Maribor 2016a, 2016
- Iztok Fister Jr., Samo Rauter, DuΕ‘an Fister, Iztok Fister. β[A collection of sport activity datasets for data analysis and data mining 2016b](http://iztok-jr-fister.eu/static/publications/Sport3.zip).β Technical report 2016b, University of Maribor, 2016
- Samo Rauter, Iztok Fister Jr., Iztok Fister. β[A collection of sport activity files for data analysis and data mining](http://iztok-jr-fister.eu/static/css/datasets/Sport.zip).β Ver 12 05, University of Maribor, 2015
### Soccer β½οΈ
- Rouissi, Mehdi, et al. β[Data concerning isometric lower limb strength of dominant versus not-dominant leg in young elite soccer players](https://www.sciencedirect.com/science/article/pii/S2352340918300258).β (2018).
- Pappalardo, Luca, et al. β[A public data set of spatio-temporal match events in soccer competitions](https://www.nature.com/articles/s41597-019-0247-7).β Scientific data 6.1 (2019): 1-15.
- Slimani, Maamer, Armin ParavliΔ, and Nicola Luigi Bragazzi. β[Data concerning the effect of plyometric training on jump performance in soccer players: A meta-analysis](https://www.sciencedirect.com/science/article/pii/S2352340917304857).β Data in brief 15 (2017): 324-334.
### Track and field πβ
- Aguilera-Castells, Joan, et al. β[Correlational data concerning body centre of mass acceleration, muscle activity, and forces exerted during a suspended lunge under different stability conditions in high-standard track and field athletes](https://www.sciencedirect.com/science/article/pii/S2352340919312673).β Data in brief 28 (2020): 104912.
### Triathlon π₯
- Iztok Fister Jr., DuΕ‘an Fister. β[A collection of IRONMAN, IRONMAN 70.3 and Ultra-triathlon race results](http://iztok-jr-fister.eu/static/publications/158.pdf).β, version 0.1, Technical Report 0110, 2016
### Wrestling π€ΌββοΈ
- Okagbue, Hilary I., et al. β[Statistical analysis of frequencies of opponentsΧ³ eliminations in Royal Rumble wrestling matches](https://www.sciencedirect.com/science/article/pii/S2352340918306747).β, 1988β2018.β Data in brief 19 (2018): 1458-1465.
## Benchmarks π§ͺ
- [Tcx test files](https://github.com/firefly-cpp/tcx-test-files) - A collection of the sports activity (tcx) test files for benchmarking the parsers
## Software π»
- [ast-monitor](https://github.com/firefly-cpp/AST-Monitor) - A wearable Raspberry Pi computer for cyclists.
- [ast-tdl](https://github.com/firefly-cpp/ast-tdl) - Training Description Language.
- [gpx](https://cran.r-project.org/web/packages/gpx/index.html) - Process GPX Files into R Data Structures.
- [gpxpy](https://github.com/tkrajina/gpxpy) - A simple Python library for parsing and manipulating GPX files.
- [openant](https://github.com/Tigge/openant) - ANT and ANT-FS Python Library.
- [python-tcxparser](https://github.com/vkurup/python-tcxparser) - Simple parser for Garmin TCX files.
- [sport-activities-features](https://github.com/firefly-cpp/sport-activities-features) - A minimalistic toolbox for extracting features from sport activity files written in Python.
- [tcxread](https://github.com/firefly-cpp/tcxread) - A parser for TCX files.
- [tcxreader](https://github.com/alenrajsp/tcxreader) - Reader / parser for Garmin's TCX file format.
- [TCXWriter](https://github.com/firefly-cpp/TCXWriter) - Library for writing/creating TCX files on Arduino & ESP32 devices
- [tcx2gpx](https://gitlab.com/nshephard/tcx2gpx) - Python package for converting tcx GPS files to gpx files.
- [TCXReader.jl](https://github.com/firefly-cpp/TCXReader.jl) - Julia package designed for parsing TCX files.## Web applications π
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## Cite us
Fister Jr., I. (2023). firefly-cpp/awesome-computational-intelligence-in-sports: 1.0 (1.0). Zenodo. [https://doi.org/10.5281/zenodo.10431418](https://doi.org/10.5281/zenodo.10431418)