Esmaeili Sani, Mohsen. (1401). Deep Learning Approach for Detection of Head Injuries in Football from Spatial-Temporal Features in Video Data. , 2(3), 25-44. doi: 10.22089/jehs.2024.14528.1068
Mohsen Esmaeili Sani. "Deep Learning Approach for Detection of Head Injuries in Football from Spatial-Temporal Features in Video Data". , 2, 3, 1401, 25-44. doi: 10.22089/jehs.2024.14528.1068
Esmaeili Sani, Mohsen. (1401). 'Deep Learning Approach for Detection of Head Injuries in Football from Spatial-Temporal Features in Video Data', , 2(3), pp. 25-44. doi: 10.22089/jehs.2024.14528.1068
Esmaeili Sani, Mohsen. Deep Learning Approach for Detection of Head Injuries in Football from Spatial-Temporal Features in Video Data. , 1401; 2(3): 25-44. doi: 10.22089/jehs.2024.14528.1068

Deep Learning Approach for Detection of Head Injuries in Football from Spatial-Temporal Features in Video Data

Journal of Exercise and Health Science
دوره 2، شماره 3، شهریور 2022، صفحه 25-44    اصل مقاله (605.72 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22089/jehs.2024.14528.1068
نویسنده
Mohsen Esmaeili Sani*
PhD student in sports management, Mazandaran University-Babolsar, Iran.
چکیده
Objective: The aim of this study is to propose a deep learning approach for detecting head injuries in football video data using spatial-temporal features.
Methods: The proposed method employs ResNet-50 architecture and the Temporal Shift Module (TSM) for feature learning and classification. The algorithm is trained with a publicly available soccer video dataset labeled with annotated head injuries. The evaluation of the proposed method is done on a test set that includes 500 football videos, and the evaluation criteria used include overall accuracy, precision, recall, and F1 score.
Results: The proposed algorithm achieves an overall accuracy of 0.986 in detecting head injuries in the test set, which is a significant improvement compared to previous studies in the same field.
Conclusions: The proposed method provides a promising approach for head impact event detection using spatio-temporal features, which could have important implications for sports and medical industries. However, the model requires a large amount of annotated data for training, and future research could focus on addressing limitations such as developing more efficient training methods and incorporating other techniques to identify head injuries outside the camera's field of view.
کلیدواژه‌ها
Brain Concussion؛ Traumatic Brain Injury؛ Machine Learning؛ Neural Networks (Computer)؛ Video Recording
مراجع

1. Allison, M. A., Kang, Y. S., Bolte, J. H., Maltese, M. R., & Arbogast, K. B. (2014).
Validation of a helmet-based system to measure head impact biomechanics in ice
hockey. Medicine and science in sports and exercise, 46(1), 115-123.
2. Beaudouin, F., Demmerle, D., Fuhr, C., Tröß, T., & Meyer, T. (2021). Head impact
situations in professional football (soccer). Sports medicine international
open, 5(02), E37-E44.
3. Beckwith, J. G., Greenwald, R. M., Chu, J. J., Crisco, J. J., Rowson, S., Duma, S. M.,
... & Collins, M. W. (2013). Head impact exposure sustained by football players on
days of diagnosed concussion. Medicine and science in sports and exercise, 45(4),
737.
4. Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016, September). Simple
online and realtime tracking. In 2016 IEEE international conference on image
processing (ICIP) (pp. 3464-3468). IEEE.
5. Bourdet, N., Deck, C., Trog, A., Meyer, F., Noblet, V., & Willinger, R. (2021,
September). Deep learning methods applied to the assessment of brain injury risk. In
Proceedings of International Research Conference on the Biomechanics of Impacts.
6. Caccese, J. B., Lamond, L. C., Buckley, T. A., & Kaminski, T. W. (2016). Reducing
purposeful headers from goal kicks and punts may reduce cumulative exposure to
head acceleration. Research in sports medicine, 24(4), 407-415.
7. Campbell, K. R., Marshall, S. W., Luck, J. F., Pinton, G. F., Stitzel, J. D., Boone, J.
S., ... & Mihalik, J. P. (2020). Head impact telemetry system’s video-based impact
detection and location accuracy. Medicine and science in sports and exercise, 52(10),
2198.
8. Chrisman, S. P., Ebel, B. E., Stein, E., Lowry, S. J., & Rivara, F. P. (2019). Head
impact exposure in youth soccer and variation by age and sex. Clinical journal of
sport medicine, 29(1), 3-10.
9. Deliege, A., Cioppa, A., Giancola, S., Seikavandi, M. J., Dueholm, J. V., Nasrollahi,
K., ... & Van Droogenbroeck, M. (2021). Soccernet-v2: A dataset and benchmarks
for holistic understanding of broadcast soccer videos. In Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4508-
4519).
10. Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S.,
Saenko, K., & Darrell, T. (2015). Long-term recurrent convolutional networks for
visual recognition and description. In Proceedings of the IEEE conference on
computer vision and pattern recognition (pp. 2625-2634).
11. Fafula, A., & Peterson, R. (2015). Predicting Global Economic Activity with Media
Analytics. Available on Research Gate.
12. Fanton, M., Wu, L., & Camarillo, D. (2020). Comment on “Frequency and magnitude
of game-related head impacts in male contact sports athletes: a systematic review and
meta-analysis”. Sports medicine, 50(4), 841-842.
 
 
41 Journal of Exercise and Health Science, Vol. 02, No. 03, Summer 2022
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License
13. Forsyth, D., & Ponce, J. (2012). Computer vision: A modern approach. Always
learning.
14. Gabler, L. F., Huddleston, S. H., Dau, N. Z., Lessley, D. J., Arbogast, K. B.,
Thompson, X., ... & Crandall, J. R. (2020). On-field performance of an instrumented
mouthguard for detecting head impacts in American football. Annals of biomedical
engineering, 48, 2599-2612.
15. Ghazi, K., Wu, S., Zhao, W., & Ji, S. (2021). Instantaneous whole-brain strain
estimation in dynamic head impact. Journal of Neurotrauma, 38(8), 1023-1035.
16. Jiang, Y., Cui, K., Chen, L., Wang, C., & Xu, C. (2020, October). Soccerdb: A large-
scale database for comprehensive video understanding. In Proceedings of the 3rd
International Workshop on Multimedia Content Analysis in Sports (pp. 1-8).
17. Jocher, G., Stoken, A., Borovec, J., Chaurasia, A., Changyu, L., Laughing, A. V., ...
& Ingham, F. (2021). ultralytics/yolov5: v5. 0-YOLOv5-P6 1280 models AWS
Supervise. ly and YouTube integrations. Zenodo, 11.
18. King, D., Hume, P., Gissane, C., Brughelli, M., & Clark, T. (2016). The influence of
head impact threshold for reporting data in contact and collision sports: systematic
review and original data analysis. Sports medicine, 46(2), 151-169.
19. Kuo, C., Wu, L., Loza, J., Senif, D., Anderson, S. C., & Camarillo, D. B. (2018).
Comparison of video-based and sensor-based head impact exposure. PloS one, 13(6),
e0199238.
20. Kontos, A. P., Dolese, A., Elbin Iii, R. J., Covassin, T., & Warren, B. L. (2011).
Relationship of soccer heading to computerized neurocognitive performance and
symptoms among female and male youth soccer players. Brain Injury, 25(12), 1234-
1241.
21. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L.
(2014). Large-scale video classification with convolutional neural networks.
In Proceedings of the IEEE conference on Computer Vision and Pattern
Recognition (pp. 1725-1732). Karpathy, A., Toderici, G., Shetty, S., Leung, T.,
Sukthankar, R., & Fei-Fei, L. (2014). Large-scale video classification with
convolutional neural networks. In Proceedings of the IEEE conference on Computer
Vision and Pattern Recognition (pp. 1725-1732).
22. Gu, Y., Wang, Q., & Qin, X. (2021, October). Real-time streaming perception system
for autonomous driving. In 2021 China Automation Congress (CAC) (pp. 5239-
5244). IEEE.
23. Li, Z., Gavrilyuk, K., Gavves, E., Jain, M., & Snoek, C. G. (2018). Videolstm
convolves, attends and flows for action recognition. Computer Vision and Image
Understanding, 166, 41-50.
24. Lin, T. Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., ... & Dollár,
P. (2015). Microsoft COCO: common objects in context. arXiv. arXiv preprint
arXiv:1405.0312, 21.
25. Lin, J., Gan, C., & Han, S. (2019). Tsm: Temporal shift module for efficient video
understanding. In Proceedings of the IEEE/CVF International Conference on
Computer Vision (pp. 7083-7093).

 
 
Soltanirad: Detecting Head Injuries in Football Using Deep… 42
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License
26. Lipton, M. L., Kim, N., Zimmerman, M. E., Kim, M., Stewart, W. F., Branch, C. A.,
& Lipton, R. B. (2013). Soccer heading is associated with white matter
microstructural and cognitive abnormalities. Radiology, 268(3), 850.
27. Lipton, Z. C., Elkan, C., & Narayanaswamy, B. (2014). Thresholding classifiers to
maximize F1 score. arXiv preprint arXiv:1402.1892.
28. Hanlon, E. M., & Bir, C. A. (2012). Real-time head acceleration measurement in girls'
youth soccer. Medicine and science in sports and exercise, 44(6), 1102-1108.
29. Haran, F. J., Tierney, R., Wright, W. G., Keshner, E., & Silter, M. (2013). Acute
changes in postural control after soccer heading. International journal of sports
medicine, 34(04), 350-354.
30. Hasija, V., & Takhounts, E. G. (2022). Deep learning methodology for predicting
time history of head angular kinematics from simulated crash videos. Scientific
Reports, 12(1), 6526.
31. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image
recognition. In Proceedings of the IEEE conference on computer vision and pattern
recognition (pp. 770-778).
32. Martin, Z., Hendricks, S., & Patel, A. (2021). Automated tackle injury risk assessment
in contact-based sports-a rugby union example. In Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition (pp. 4594-4603).
33. Miller, L. E., Pinkerton, E. K., Fabian, K. C., Wu, L. C., Espeland, M. A., Lamond,
L. C., ... & Urban, J. E. (2020). Characterizing head impact exposure in youth female
soccer with a custom-instrumented mouthpiece. Research in Sports Medicine, 28(1),
55-71.
34. Montenigro, P. H., Alosco, M. L., Martin, B. M., Daneshvar, D. H., Mez, J., Chaisson,
C. E., ... & Tripodis, Y. (2017). Cumulative head impact exposure predicts later-life
depression, apathy, executive dysfunction, and cognitive impairment in former high
school and college football players. Journal of neurotrauma, 34(2), 328-340.
35. Nevins, D., Hildenbrand, K., Kensrud, J., Vasavada, A., & Smith, L. (2016). Field
evaluation of a small form-factor head impact sensor for use in soccer. Procedia
engineering, 147, 186-190.
36. O'Connor, K. L., Rowson, S., Duma, S. M., & Broglio, S. P. (2017). Head-impact–
measurement devices: a systematic review. Journal of athletic training, 52(3), 206-
227.
37. Patton, D. A., Huber, C. M., Jain, D., Myers, R. K., McDonald, C. C., Margulies, S.
S., ... & Arbogast, K. B. (2020). Head impact sensor studies in sports: a systematic
review of exposure confirmation methods. Annals of biomedical engineering, 48(11),
2497-2507.
38. Press, J. N., & Rowson, S. (2017). Quantifying head impact exposure in collegiate
women's soccer. Clinical journal of sport medicine, 27(2), 104-110.
39. Raymond, S. J., Cecchi, N. J., Alizadeh, H. V., Callan, A. A., Rice, E., Liu, Y., ... &
Camarillo, D. B. (2022). Physics-informed machine learning improves detection of
head impacts. Annals of biomedical engineering, 50(11), 1534-1545.

 
 
43 Journal of Exercise and Health Science, Vol. 02, No. 03, Summer 2022
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License
40. Rezaei, A., & Wu, L. C. (2022). Automated soccer head impact exposure tracking
using video and deep learning. Scientific reports, 12(1), 9282.
41. Rico-González, M., Pino-Ortega, J., Méndez, A., Clemente, F., & Baca, A. (2023).
Machine learning application in soccer: a systematic review. Biology of sport, 40(1),
249-263.
42. Rodrigues, A. C., Lasmar, R. P., & Caramelli, P. (2016). Effects of soccer heading on
brain structure and function. Frontiers in neurology, 7, 38.
43. Sanchez, E. J., Gabler, L. F., Good, A. B., Funk, J. R., Crandall, J. R., & Panzer, M.
B. (2019). A reanalysis of football impact reconstructions for head kinematics and
finite element modeling. Clinical biomechanics, 64, 82-89.
44. Sandmo, S. B., Gooijers, J., Seer, C., Kaufmann, D., Bahr, R., Pasternak, O., ... &
Koerte, I. K. (2021). Evaluating the validity of self-report as a method for quantifying
heading exposure in male youth soccer. Research in sports medicine, 29(5), 427-439.
45. Siegmund, G. P., Guskiewicz, K. M., Marshall, S. W., DeMarco, A. L., & Bonin, S.
J. (2016). Laboratory validation of two wearable sensor systems for measuring head
impact severity in football players. Annals of biomedical engineering, 44(4), 1257-
1274.
46. Stemper, B. D., Shah, A. S., Harezlak, J., Rowson, S., Mihalik, J. P., Duma, S. M., ...
& CARE Consortium Investigators. (2019). Comparison of head impact exposure
between concussed football athletes and matched controls: evidence for a possible
second mechanism of sport-related concussion. Annals of biomedical
engineering, 47, 2057-2072.
47. Stewart, W. F., Kim, N., Ifrah, C. S., Lipton, R. B., Bachrach, T. A., Zimmerman, M.
E., ... & Lipton, M. L. (2017). Symptoms from repeated intentional and unintentional
head impact in soccer players. Neurology, 88(9), 901-908.
48. Stephens, R., Rutherford, A., Potter, D., & Fernie, G. (2010). Neuropsychological
consequence of soccer play in adolescent UK school team soccer players. The Journal
of neuropsychiatry and clinical neurosciences, 22(3), 295-303.
49. Takhounts, E. G., Eppinger, R. H., Campbell, J. Q., Tannous, R. E., Power, E. D., &
Shook, L. S. (2003). On the development of the SIMon finite element head model
(No. 2003-22-0007). SAE Technical Paper.
50. Thomas, G., Gade, R., Moeslund, T. B., Carr, P., & Hilton, A. (2017). Computer
vision for sports: Current applications and research topics. Computer Vision and
Image Understanding, 159, 3-18.
51. Wang, T., Kenny, R., & Wu, L. C. (2021). Head impact sensor triggering bias
introduced by linear acceleration thresholding. Annals of biomedical
engineering, 49(12), 3189-3199.
52. Wang, X., & Gupta, A. (2018). Videos as space-time region graphs. In Proceedings
of the European conference on computer vision (ECCV) (pp. 399-417).
53. Wu, S., Zhao, W., Ghazi, K., & Ji, S. (2019). Convolutional neural network for
efficient estimation of regional brain strains. Scientific reports, 9(1), 17326.

 
 
Soltanirad: Detecting Head Injuries in Football Using Deep… 44
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License
54. Wu, L. C., Nangia, V., Bui, K., Hammoor, B., Kurt, M., Hernandez, F., ... &
Camarillo, D. B. (2016). In vivo evaluation of wearable head impact sensors. Annals
of biomedical engineering, 44(4), 1234-1245.
55. Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., &
Toderici, G. (2015). Beyond short snippets: Deep networks for video classification.
In Proceedings of the IEEE conference on computer vision and pattern
recognition (pp. 4694-4702).
56. Zhan, X., Liu, Y., Raymond, S. J., Alizadeh, H. V., Domel, A. G., Gevaert, O., ... &
Camarillo, D. B. (2020). Deep learning head model for real-time estimation of entire
brain deformation in concussion. arXiv preprint arXiv:2010.08527.
57. Zhan, X., Liu, Y., Raymond, S. J., Alizadeh, H. V., Domel, A. G., Gevaert, O., ... &
Camarillo, D. B. (2021). Rapid estimation of entire brain strain using deep learning
models. IEEE Transactions on Biomedical Engineering, 68(11), 3424-3434.
58. Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., ... & He, Q. (2020). A
comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43-76

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