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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
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