- Araújo, D., Couceiro, M., Seifert, L., Sarmento, H., Davids, K., 2021a. Artificial intelligence in sport performance analysis. Artif. Intell. Sport Perform. Anal. 1–196. https://doi.org/10.4324/9781003163589/ARTIFICIAL-INTELLIGENCE-SPORT-PERFORMANCE-ANALYSIS-DUARTE-ARA
- Araújo, D., Couceiro, M., Seifert, L., Sarmento, H., Davids, K., 2021b. Artificial intelligence in sport performance analysis. Artif. Intell. Sport Perform. Anal. 1–196. https://doi.org/10.4324/9781003163589/ARTIFICIAL-INTELLIGENCE-SPORT-PERFORMANCE-ANALYSIS-DUARTE-ARA
- Baca, A., Dabnichki, P., Heller, M., Kornfeind, P., 2009. Ubiquitous computing in sports: A review and analysis. https://doi.org/10.1080/02640410903277427 27, 1335–1346. https://doi.org/10.1080/02640410903277427
- Baca, A., Kornfeind, P., 2006. Rapid feedback systems for elite sports training. IEEE Pervasive Comput. 5, 70–76. https://doi.org/10.1109/MPRV.2006.82
- Barris, S., Button, C., 2012. A Review of Vision-Based Motion Analysis in Sport. Sport. Med. 2008 3812 38, 1025–1043. https://doi.org/10.2165/00007256-200838120-00006
- Bartlett, R., 2006. Artificial Intelligence in Sports Biomechanics: New Dawn or False Hope? J. Sports Sci. Med. 5, 474.
- Bezobrazov, S., Sheleh, A., Kislyuk, S., Golovko, V., Sachenko, A., Komar, M., Dorosh, V., Turchenko, V., 2019. Artificial Intelligence for Sport Activitity Recognition. Proc. 2019 10th IEEE Int. Conf. Intell. Data Acquis. Adv. Comput. Syst. Technol. Appl. IDAACS 2019 2, 628–632. https://doi.org/10.1109/IDAACS.2019.8924243
- Brumann, C., Kukuk, M., Reinsberger, C., 2021. Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash. Sensors 2021, Vol. 21, Page 4550 21, 4550. https://doi.org/10.3390/S21134550
- Chakraborty, A., Kar, A.K., 2017. Swarm intelligence: A review of algorithms. Model. Optim. Sci. Technol. 10, 475–494. https://doi.org/10.1007/978-3-319-50920-4_19/COVER
- Charlwood, A., 2021. Artificial Intelligence and Talent Management. Digit. Talent Manag. 122–136. https://doi.org/10.4324/9780429265440-7-7
- Chen, X., Sun, L., 2021. Campus football application based on FPGA system and GPS wearable electronic equipment. Microprocess. Microsyst. 81, 103784. https://doi.org/10.1016/J.MICPRO.2020.103784
- Chi, E.H., Borriello, G., Hunt, G., Davies, N., 2005. Pervasive computing in sports technologies. IEEE Pervasive Comput. 4, 22–25. https://doi.org/10.1109/MPRV.2005.58
- Claudino, J.G., Capanema, D. de O., de Souza, T.V., Serrão, J.C., Machado Pereira, A.C., Nassis, G.P., 2019. Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review. Sport. Med. - Open 5, 1–12. https://doi.org/10.1186/S40798-019-0202-3/FIGURES/3
- Deng, H., Cao, S., Tang, J., 2022. Prediction of Sports Aggression Behavior and Analysis of Sports Intervention Based on Swarm Intelligence Model. Sci. Program. 2022. https://doi.org/10.1155/2022/2479939
- Ding, D., Jiang, J., Liu, C., 2021. Exercise Behavior Prediction and Injury Assessment Based on Swarm Intelligence Algorithm. Discret. Dyn. Nat. Soc. 2021. https://doi.org/10.1155/2021/1541816
- Fister, I., Fister, I., 2017. Generating the training plans based on existing sports activities using swarm intelligence. Model. Optim. Sci. Technol. 10, 79–94. https://doi.org/10.1007/978-3-319-50920-4_4/COVER
- Fister, I., Iglesias, A., Galvez, A., Deb, S., Fister, D., Fister, I., 2021. On Deploying the Artificial Sport Trainer into Practice. 2021 8th Int. Conf. Soft Comput. Mach. Intell. ISCMI 2021 21–26. https://doi.org/10.1109/ISCMI53840.2021.9654817
- Fister, I., Iglesias, A., Osaba, E., Mlakar, U., Brest, J., 2019. Adaptation of sport training plans by swarm intelligence. Adv. Intell. Syst. Comput. 837, 56–67. https://doi.org/10.1007/978-3-319-97888-8_5/COVER
- Geng, X., 2021. Research on athlete’s action recognition based on acceleration sensor and deep learning. J. Intell. Fuzzy Syst. 40, 2229–2240. https://doi.org/10.3233/JIFS-189221
- Hong, C., Jeong, I., Vecchietti, L.F., Har, D., Kim, J.H., 2021. AI World Cup: Robot-Soccer-Based Competitions. IEEE Trans. Games 13, 330–341. https://doi.org/10.1109/TG.2021.3065410
- Huang, Y., 2021. The Application of Artificial Intelligence Technology in the On-site Decision System of Sports Competitions. Proc. - 2021 Int. Conf. Big Data, Artif. Intell. Risk Manag. ICBAR 2021 106–109. https://doi.org/10.1109/ICBAR55169.2021.00031
- Li, B., Xu, X., 2021. Application of Artificial Intelligence in Basketball Sport. J. Educ. Heal. Sport 11, 54–67. https://doi.org/10.12775/JEHS.2021.11.07.005
- Li, C., Cui, J., 2021. Intelligent Sports Training System Based on Artificial Intelligence and Big Data. Mob. Inf. Syst. 2021. https://doi.org/10.1155/2021/9929650
- Li, H., Manickam, A., Samuel, R.D.J., 2022. Automatic detection technology for sports players based on image recognition technology: the significance of big data technology in China’s sports field. Ann. Oper. Res. 2021 1–18. https://doi.org/10.1007/S10479-021-04409-1
- LIMA, G., MUNIZ-PARDOPARDOS, B., KOLLIARI-TURNER, A., HAMILTON, B., GUPPY, F.M., GRIVAS, G., BOSCH, A., BORRIONE, P., DIGIANFRANCESCO, A., FOSSATI, C., PIGOZZI, F., PITSILADIS, Y., 2021. Anti-doping and other sport integrity challenges during the COVID-19 pandemic. J. Sports Med. Phys. Fitness 61, 1173–1183. https://doi.org/10.23736/S0022-4707.21.12777-X
- Liu, Y., Ji, Y., 2021. Target recognition of sport athletes based on deep learning and convolutional neural network. J. Intell. Fuzzy Syst. 40, 2253–2263. https://doi.org/10.3233/JIFS-189223
- Luis Ordonez-Avila, J., Daniel Pineda, A., Daniel Rodriguez, J., Max Carrasco, A., 2022. Design of Badminton Training Robot with Athlete Detection. 2022 7th Int. Conf. Control Robot. Eng. ICCRE 2022 26–31. https://doi.org/10.1109/ICCRE55123.2022.9770263
- Ma, K., 2020. Artificial Intelligence Aided Training in Ping Pong Sport Education. Proc. - 2020 2nd Int. Conf. Transdiscipl. AI, TransAI 2020 43–49. https://doi.org/10.1109/TRANSAI49837.2020.00012
- Mamo, Y., Su, Y., Andrew, D.P.S., 2022. The transformative impact of big data applications in sport marketing: current and future directions. Int. J. Sport. Mark. Spons. 23, 594–611. https://doi.org/10.1108/IJSMS-03-2021-0073/FULL/XML
- Mirmohammad, Y., Khorsandi, S., Shahsavari, M.N., Yazdankhoo, B., Sadeghnejad, S., 2022. Ball Path Prediction for Humanoid Robots: Combination of k-NN Regression and Autoregression Methods. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 13132 LNAI, 3–14. https://doi.org/10.1007/978-3-030-98682-7_1/COVER
- Molfino, R., Muscolo, G.G., Puig, D., Recchiuto, C.T., Solanas, A., Williams, A.M., 2014. An embodied-simplexity approach to design humanoid robots bioinspired by taekwondo athletes. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 8069 LNAI, 311–312. https://doi.org/10.1007/978-3-662-43645-5_34/COVER
- Needham, L., Evans, M., Cosker, D.P., Colyer, S.L., 2021. Development, evaluation and application of a novel markerless motion analysis system to understand push-start technique in elite skeleton athletes. PLoS One 16, e0259624. https://doi.org/10.1371/JOURNAL.PONE.0259624
- Nithya, N., Nallavan, G., 2021. Role of Wearables in Sports based on Activity recognition and biometric parameters: A Survey. Proc. - Int. Conf. Artif. Intell. Smart Syst. ICAIS 2021 1700–1705. https://doi.org/10.1109/ICAIS50930.2021.9395761
- Nojima, T., Phuong, N., Kai, T., Sato, T., Koike, H., 2015. Augmented dodgeball: An approach to designing augmented sports. ACM Int. Conf. Proceeding Ser. 11, 137–140. https://doi.org/10.1145/2735711.2735834
- Oubre, B., Daneault, J.F., Boyer, K., Kim, J.H., Jasim, M., Bonato, P., Lee, S.I., 2020. A Simple Low-Cost Wearable Sensor for Long-Term Ambulatory Monitoring of Knee Joint Kinematics. IEEE Trans. Biomed. Eng. 67, 3483–3490. https://doi.org/10.1109/TBME.2020.2988438
- Paschos, N.K., 2021. Editorial Commentary: Artificial Intelligence in Sports Medicine Diagnosis Needs to Improve. Arthrosc. J. Arthrosc. Relat. Surg. 37, 782–783. https://doi.org/10.1016/J.ARTHRO.2020.11.023
- Ramkumar, P.N., Luu, B.C., Haeberle, H.S., Karnuta, J.M., Nwachukwu, B.U., Williams, R.J., 2022. Sports Medicine and Artificial Intelligence: A Primer. Am. J. Sports Med. 50, 1166–1174. https://doi.org/10.1177/03635465211008648
- Römer, K., Domnitcheva, S., 2002. Smart Playing Cards: A Ubiquitous Computing Game. Pers. Ubiquitous Comput. 2002 65 6, 371–377. https://doi.org/10.1007/S007790200042
- Saheb, T., 2018. Big data analytics in the context of internet of things and the emergence of real-time systems: a systematic literature review. Int. J. High Perform. Syst. Archit. 8, 34–50. https://doi.org/10.1504/IJHPSA.2018.10015191
- Saheb, T., Cabanillas, F.J.L., Higueras, E., 2022a. The risks and benefits of Internet of Things (IoT) and their influence on smartwatch use. Spanish J. Mark. - ESIC. https://doi.org/10.1108/SJME-07-2021-0129/FULL/PDF
- Saheb, Tahereh, Dehghani, M., Saheb, Tayebeh, 2022b. Artificial intelligence for sustainable energy: A contextual topic modeling and content analysis. Sustain. Comput. Informatics Syst. 35, 100699. https://doi.org/10.1016/J.SUSCOM.2022.100699
- Saheb, Tahereh, Sabour, E., Qanbary, F., Saheb, Tayebeh, 2022c. Delineating privacy aspects of COVID tracing applications embedded with proximity measurement technologies & digital technologies. Technol. Soc. 69, 101968. https://doi.org/10.1016/J.TECHSOC.2022.101968
- Saheb, Tahereh, Saheb, Tayebeh, 2021. Predicting the Adoption of Health Wearables with an Emphasis on the Perceived Ethics of Biometric Data.
- Sugano, Y., Ohtsuji, J., Usui, T., Mochizuki, Y., Okude, N., 2006. Shootball: The tangible ball sport in ubiquitous computing. Int. Conf. Adv. Comput. Entertain. Technol. 2006. https://doi.org/10.1145/1178823.1178862
- Sun, Y., Ma, Y., Chen, M.R., Liu, Y., Feng, Y., Jiang, H., Kudinov, Y., Izumov, A., Lobanov, S., Obuhov, P., Marchenko, E., 2021. RUN-BOT: mobile robot for running exercises. J. Phys. Conf. Ser. 2131, 022115. https://doi.org/10.1088/1742-6596/2131/2/022115
- Tao, W., Liu, T., Zheng, R., Feng, H., 2012. Gait Analysis Using Wearable Sensors. Sensors 2012, Vol. 12, Pages 2255-2283 12, 2255–2283. https://doi.org/10.3390/S120202255
- van Eck, N.J., Waltman, L., 2014. Visualizing Bibliometric Networks, in: Measuring Scholarly Impact. Springer International Publishing, Cham, pp. 285–320. https://doi.org/10.1007/978-3-319-10377-8_13
- van Eck, N.J., Waltman, L., 2010. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523–538. https://doi.org/10.1007/s11192-009-0146-3
- Van Eck, N.J., Waltman, L., 2009. How to normalize cooccurrence data? An analysis of some well-known similarity measures. J. Am. Soc. Inf. Sci. Technol. 60, 1635–1651. https://doi.org/10.1002/ASI.21075
- Waqar, A., Ahmad, I., Habibi, D., Hart, N., Phung, Q.V., 2021. Enhancing Athlete Tracking Using Data Fusion in Wearable Technologies. IEEE Trans. Instrum. Meas. 70. https://doi.org/10.1109/TIM.2021.3069520
- Xie, J., Chen, G., Liu, S., 2021. Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning. Front. Neurorobot. 15, 621196. https://doi.org/10.3389/FNBOT.2021.621196
- Yang, B., Habibi, G., Lancaster, P.E., Boots, B., Smith, J.R., 2022. Motivating Physical Activity via Competitive Human-Robot Interaction.
- Zhu, Q., 2022. Classification and Optimization of Basketball Players’ Training Effect Based on Particle Swarm Optimization. J. Healthc. Eng. 2022. https://doi.org/10.1155/2022/2120206
- Rouhiainen, L. (2018). Artificial Intelligence: 101 things you must know today about our future. Lasse Rouhiainen
- Hasanzadeh, N., Moharramzadeh, M., & Naghizadeh-Baghi, A. (2021). Needs Assessment and Reasons for Consuming Food Supplements for Body-Building and Weightlifting Athletes in Ardabil Province. Research in Sport Management and Marketing, 2(1), 10-23. doi: 10.22098/rsmm.2021.1255
- Shabani Bahar, Gholamreza, Keshavarz, Lokman, Farahani, Abul-Fadl, & Farid Fathi, Akbar. (1395). The influence of the legal environment on the borders of Sazmani, Zarat, Rzesh and Javan. Reviews of the Department of Raftar Sasmani in Warzesh, 3(3), 55-64.
-
|