آمار

تعداد نشریات26
تعداد شماره‌ها326
تعداد مقالات2,803
تعداد مشاهده مقاله4,228,532
تعداد دریافت فایل اصل مقاله2,881,768
Shahlaei Bagheri, Javad. (1403). Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm. سامانه مدیریت نشریات علمی, 5(2), 76-86. doi: 10.22098/rsmm.2024.14524.1319
Javad Shahlaei Bagheri. "Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm". سامانه مدیریت نشریات علمی, 5, 2, 1403, 76-86. doi: 10.22098/rsmm.2024.14524.1319
Shahlaei Bagheri, Javad. (1403). 'Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm', سامانه مدیریت نشریات علمی, 5(2), pp. 76-86. doi: 10.22098/rsmm.2024.14524.1319
Shahlaei Bagheri, Javad. Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm. سامانه مدیریت نشریات علمی, 1403; 5(2): 76-86. doi: 10.22098/rsmm.2024.14524.1319

Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm

Research in Sport Management and Marketing
مقاله 6، دوره 5، شماره 2، تیر 2024، صفحه 76-86    اصل مقاله (400.08 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22098/rsmm.2024.14524.1319
نویسنده
Javad Shahlaei Bagheri*
Associate Professor, Department of Sports Management, Faculty of Physical Education and Sports Sciences, Allameh Tabatabai University, Tehran, Iran
چکیده
Purpose: This study aimed to predict and manage physical injuries caused by gym equipment and facilities using the SVM algorithm.

Method: This study was of a developmental-applied type. The snowball method was used to select the subjects. Subjects were asked to answer the questionnaire online and send it to friends and acquaintances of athletes. The validity of the instrument was confirmed through the opinions of university professors and convergent validity. Cronbach's alpha was used to check reliability. The sample questionnaire included 612 athletes in the age group of 18 to 60 years. 158 people were healthy, 54 people had head injuries, 211 people had leg injuries, and 189 people had hand injuries. The SVM algorithm was used to classify people. In addition, MATLAB software version 2022 was used for data analysis. The evaluation was conducted based on the clutter matrix and accuracy criteria.

Results: The results showed that the SVM algorithm can predict head, arm and leg injuries with 74.6% accuracy and 73.2% accuracy, respectively.

Conclusion: This study showed that by discovering hidden patterns and relationships in the data, this algorithm can probably be used correctly to improve the quality of sports facilities management to prevent physical injuries of athletes.
کلیدواژه‌ها
Sports Facilities؛ Prediction؛ SVM؛ Physical Injury
مراجع
  • Araújo, D., Couceiro, M., Seifert, L., Sarmento, H., & Davids, K. (2021). Artificial intelligence in sport performance analysis: Routledge.
  • Ayala, F., López-Valenciano, A., Martín, J. A. G., Croix, M. D. S., Vera-Garcia, F. J., del Pilar García-Vaquero, M., . . . Myer, G. D. (2019). A preventive model for hamstring injuries in professional soccer: Learning algorithms. International journal of sports medicine, 40(05), 344-353.
  • Bullock, G. S., Collins, G. S., Peirce, N., Arden, N. K., & Filbay, S. R. (2020). Playing sport injured is associated with osteoarthritis, joint pain and worse health-related quality of life: a cross-sectional study. BMC musculoskeletal disorders, 21, 1-11.
  • Carey, D. L., Ong, K., Whiteley, R., Crossley, K. M., Crow, J., & Morris, M. E. (2018). Predictive modelling of training loads and injury in Australian football. International Journal of Computer Science in Sport, 17(1), 49-66.
  • Chelladurai, P. (2014). Managing organizations for sport and physical activity: A systems perspective: Taylor & Francis.
  • Chmait, N., & Westerbeek, H. (2021). Artificial intelligence and machine learning in sport research: An introduction for non-data scientists. Frontiers in Sports and Active Living, 3, 363.
  • Dijksma, I., Sharma, J., & Gabbett, T. J. (2021). Training load monitoring and injury prevention in military recruits: considerations for preparing soldiers to fight sustainably. Strength & Conditioning Journal, 43(2), 23-30.
  • Faritha Banu, J., Neelakandan, S., Geetha, B., Selvalakshmi, V., Umadevi, A., & Martinson, E. O. (2022). Artificial intelligence based customer churn prediction model for business markets. Computational Intelligence and Neuroscience, 2022.
  • Fasihi, L., Tartibian, B., & Eslami, R. (2022). Presenting a Model for Detecting Osteoporosis In Active Older Men Using the Support Vector Machine Algorithm. The Scientific Journal of Rehabilitation Medicine, 11(5), 742-753.
  • Fasihi, L., Tartibian, B., Eslami, R., & Fasihi, H. (2022). Artificial intelligence used to diagnose osteoporosis from risk factors in clinical data and proposing sports protocols. Scientific Reports, 12(1), 18330.
  • Fathollahi Parvaneh, O., Ameri, S., & Sajjadi, S. N. (2023). Designing a green management model for sports Facilities with Emphasis on Sustainable Development. Strategic Studies on Youth and Sports, 22(60), 289-316.
  • Harifi, T., & Montazer, M. (2017). Application of nanotechnology in sports clothing and flooring for enhanced sport activities, performance, efficiency and comfort: a review. Journal of Industrial Textiles, 46(5), 1147-1169.
  • Huang, C., & Jiang, L. (2021). Data monitoring and sports injury prediction model based on embedded system and machine learning algorithm. Microprocessors and Microsystems, 81, 103654.
  • Huang, H., & Wen, S. (2022). Markov model-based sports training risk prediction model design and its training control. Journal of Sensors, 2022.
  • Jiang, D. (2022). Risk Management of Sports Venues and Olympic Sports Cooperation Spirit under Complex Environment. Journal of Environmental and Public Health, 2022.
  • Kampakis, S. (2016). Predictive modelling of football injuries. arXiv preprint arXiv:1609.07480.
  • Landset, S., Bergeron, M. F., & Khoshgoftaar, T. M. (2017). Using Weather and Playing Surface to Predict the Occurrence of Injury in Major League Soccer Games: A Case Study. Paper presented at the 2017 IEEE International Conference on Information Reuse and Integration (IRI).
  • Leventer, L., Eek, F., Hofstetter, S., & Lames, M. (2016). Injury patterns among elite football players: a media-based analysis over 6 seasons with emphasis on playing position. International journal of sports medicine, 898-908.
  • López-Valenciano, A., Ayala, F., Puerta, J. M., Croix, M. D. S., Vera-García, F., Hernández-Sánchez, S., . . . Myer, G. (2018). A preventive model for muscle injuries: a novel approach based on learning algorithms. Medicine and science in sports and exercise, 50(5), 915.
  • Lövdal, S. S., Den Hartigh, R. J., & Azzopardi, G. (2021). Injury prediction in competitive runners with machine learning. International Journal of Sports Physiology and Performance, 16(10), 1522-1531.
  • Majumdar, A., Bakirov, R., Hodges, D., Scott, S., & Rees, T. (2022). Machine learning for understanding and predicting injuries in soccer. Sports Medicine-Open, 8(1).
  • Meng, L., & Qiao, E. (2023). Analysis and design of dual-feature fusion neural network for sports injury estimation model. Neural Computing and Applications, 35(20), 14627-14639.
  • Moustakidis, S., Siouras, A., Vassis, K., Misiris, I., Papageorgiou, E., & Tsaopoulos, D. (2022). Prediction of Injuries in CrossFit Training: A Machine Learning Perspective. Algorithms, 15(3), 77.
  • Nozari, H., & Sadeghi, M. E. (2021). Artificial intelligence and Machine Learning for Real-world problems (A survey). International Journal of Innovation in Engineering, 1(3), 38-47.
  • Organization, W. H. (2022). Towards a global guidance framework for the responsible use of life sciences: summary report of consultations on the principles, gaps and challenges of biorisk management, May 2022. Retrieved from
  • Osisanwo, F., Akinsola, J., Awodele, O., Hinmikaiye, J., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3), 128-138.
  • Rault, T., Bouabdallah, A., Challal, Y., & Marin, F. (2017). A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications. Pervasive and Mobile Computing, 37, 23-44.
  • Rodas, G., Osaba, L., Arteta, D., Pruna, R., Fernández, D., & Lucia, A. (2019). Genomic prediction of tendinopathy risk in elite team sports. International Journal of Sports Physiology and Performance, 15(4), 489-495.
  • Rossi, A., Pappalardo, L., & Cintia, P. (2021). A narrative review for a machine learning application in sports: an example based on injury forecasting in soccer. Sports, 10(1), 5.
  • Rossi, A., Pappalardo, L., Cintia, P., Iaia, F. M., Fernández, J., & Medina, D. (2018). Effective injury forecasting in soccer with GPS training data and machine learning. PloS one, 13(7), e0201264.
  • Ruddy, J. D., Shield, A. J., Maniar, N., Williams, M. D., Duhig, S. J., Timmins, R. G., . . . Opar, D. A. (2018). Predictive modeling of hamstring strain injuries in elite Australian footballers. Medicine & Science in Sports & Exercise, 50(5), 906-914.
  • Sharma, B. (2016). A focus on reliability in developmental research through Cronbach’s Alpha among medical, dental and paramedical professionals. Asian Pacific Journal of Health Sciences, 3(4), 271-278.
  • Shen, H. (2021). Prediction simulation of sports injury based on embedded system and neural network. Microprocessors and Microsystems, 82, 103900.
  • Theron, G. F. (2020). The use of data mining for predicting injuries in professional football players.
  • Van Eetvelde, H., Mendonça, L. D., Ley, C., Seil, R., & Tischer, T. (2021). Machine learning methods in sport injury prediction and prevention: a systematic review. Journal of experimental orthopaedics, 8, 1-15.
  • Wang, S., & Lyu, B. (2022). Evidence-based sports medicine to prevent knee joint injury in triple jump. Revista Brasileira de Medicina do Esporte, 28, 195-198.
  • Yang, S. X., Cheng, S., & Su, D. L. (2022). Sports injury and stressor-related disorder in competitive athletes: a systematic review and a new framework. Burns & Trauma, 10, tkac017. 
  • Dolatyari, E., Shahlaei Bagheri, J., Ghafouri, F., & keshkar, S. (2024). Comparison of the Refereeing Structure of Iranian football and selected Asian, Oceanic and European continents. Research in Sport Management and Marketing, (), -. doi: 10.22098/rsmm.2024.14684.1325
آمار
تعداد مشاهده مقاله: 84
تعداد دریافت فایل اصل مقاله: 43


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب