A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning
The rapid development of big data technology and artificial intelligence has provided a new perspective on sports injury prevention. Although data-driven algorithms have achieved some valuable results in the field of sports injury risk assessment, the lack of sufficient generalization of models and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2023.1174525/full |
| _version_ | 1797387746881306624 |
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| author | Xiaohong Ye Yuanqi Huang Zhanshuang Bai Zhanshuang Bai Yukun Wang |
| author_facet | Xiaohong Ye Yuanqi Huang Zhanshuang Bai Zhanshuang Bai Yukun Wang |
| author_sort | Xiaohong Ye |
| collection | DOAJ |
| description | The rapid development of big data technology and artificial intelligence has provided a new perspective on sports injury prevention. Although data-driven algorithms have achieved some valuable results in the field of sports injury risk assessment, the lack of sufficient generalization of models and the inability to automate feature extraction have made it challenging to deploy research results in the real world. Therefore, this study attempts to build an injury risk prediction model using a combination of time-series image encoding and deep learning algorithms to address this issue better. This study used the time-series image encoding approach for feature construction to represent relationships between values at different moments, including Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP). Deep Convolutional Auto-Encoder (DCAE) learned the image-encoded data for representation to obtain features with good discrimination, and the classifier was performed using Deep Neural Network (DNN). The results from five repeated experiments show that the GASF-DCAE-DNN model is overall better in the training (AUC: 0.985 ± 0.001, Gmean: 0.930 ± 0.007, Sensitivity: 0.997 ± 0.003, Specificity: 0.868 ± 0.013) and test sets (AUC: 0.891 ± 0.026, Gmean: 0.830 ± 0.027, Sensitivity: 0.816 ± 0.039, Specificity: 0.845 ± 0.022), with good discriminative power, robustness, and generalization ability. Compared with the best model reported in the literature, the AUC, Gmean, Sensitivity, and Specificity of the GASF-DCAE-DNN model were higher by 23.9%, 27.5%, 39.7%, and 16.2%, respectively, which confirmed the validity and practicability of the model in injury risk prediction. In addition, differences in injury risk patterns between the training and test sets were identified through shapley additivity interpretation. It was also found that the training volume was an essential factor that affected injury risk prediction. The model proposed in this study provides a powerful injury risk prediction tool for future sports injury prevention practice. |
| first_indexed | 2024-03-08T22:29:32Z |
| format | Article |
| id | doaj.art-9318f6af1d854567a0ad854bc6e08441 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-03-08T22:29:32Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-9318f6af1d854567a0ad854bc6e084412023-12-18T06:42:33ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2023-12-011410.3389/fphys.2023.11745251174525A novel approach for sports injury risk prediction: based on time-series image encoding and deep learningXiaohong Ye0Yuanqi Huang1Zhanshuang Bai2Zhanshuang Bai3Yukun Wang4Chengyi College, Jimei University, Xiamen, ChinaSchool of Physical Education and Sport Science, Fujian Normal University, Fuzhou, ChinaSchool of Physical Education and Sport Science, Fujian Normal University, Fuzhou, ChinaSchool of Tourism and Sports Health, Hezhou University, Hezhou, ChinaInstitute of Sport Business, Loughborough University London, London, United KingdomThe rapid development of big data technology and artificial intelligence has provided a new perspective on sports injury prevention. Although data-driven algorithms have achieved some valuable results in the field of sports injury risk assessment, the lack of sufficient generalization of models and the inability to automate feature extraction have made it challenging to deploy research results in the real world. Therefore, this study attempts to build an injury risk prediction model using a combination of time-series image encoding and deep learning algorithms to address this issue better. This study used the time-series image encoding approach for feature construction to represent relationships between values at different moments, including Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP). Deep Convolutional Auto-Encoder (DCAE) learned the image-encoded data for representation to obtain features with good discrimination, and the classifier was performed using Deep Neural Network (DNN). The results from five repeated experiments show that the GASF-DCAE-DNN model is overall better in the training (AUC: 0.985 ± 0.001, Gmean: 0.930 ± 0.007, Sensitivity: 0.997 ± 0.003, Specificity: 0.868 ± 0.013) and test sets (AUC: 0.891 ± 0.026, Gmean: 0.830 ± 0.027, Sensitivity: 0.816 ± 0.039, Specificity: 0.845 ± 0.022), with good discriminative power, robustness, and generalization ability. Compared with the best model reported in the literature, the AUC, Gmean, Sensitivity, and Specificity of the GASF-DCAE-DNN model were higher by 23.9%, 27.5%, 39.7%, and 16.2%, respectively, which confirmed the validity and practicability of the model in injury risk prediction. In addition, differences in injury risk patterns between the training and test sets were identified through shapley additivity interpretation. It was also found that the training volume was an essential factor that affected injury risk prediction. The model proposed in this study provides a powerful injury risk prediction tool for future sports injury prevention practice.https://www.frontiersin.org/articles/10.3389/fphys.2023.1174525/fullinjury preventiondeep learningtime seriesinjury risk patterninjury risk prediction |
| spellingShingle | Xiaohong Ye Yuanqi Huang Zhanshuang Bai Zhanshuang Bai Yukun Wang A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning Frontiers in Physiology injury prevention deep learning time series injury risk pattern injury risk prediction |
| title | A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning |
| title_full | A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning |
| title_fullStr | A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning |
| title_full_unstemmed | A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning |
| title_short | A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning |
| title_sort | novel approach for sports injury risk prediction based on time series image encoding and deep learning |
| topic | injury prevention deep learning time series injury risk pattern injury risk prediction |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2023.1174525/full |
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