Automated soccer head impact exposure tracking using video and deep learning
Bibliographic record
Abstract
Head impacts are highly prevalent in sports and there is a pressing need to investigate the potential link between head impact exposure and brain injury risk. Wearable impact sensors and manual video analysis have been utilized to collect impact exposure data. However, wearable sensors suffer from high deployment cost and limited accuracy, while manual video analysis is a long and resource-intensive task. Here we develop and apply DeepImpact, a computer vision algorithm to automatically detect soccer headers using soccer game videos. Our data-driven pipeline uses two deep learning networks including an object detection algorithm and temporal shift module to extract visual and temporal features of video segments and classify the segments as header or nonheader events. The networks were trained and validated using a large-scale professional-level soccer video dataset, with labeled ground truth header events. The algorithm achieved 95.3% sensitivity and 96.0% precision in cross-validation, and 92.9% sensitivity and 21.1% precision in an independent test that included videos of five professional soccer games. Video segments identified as headers in the test data set correspond to 3.5 min of total film time, which can be reviewed through additional manual video verification to eliminate false positives. DeepImpact streamlines the process of manual video analysis and can help to collect large-scale soccer head impact exposure datasets for brain injury research. The fully video-based solution is a low-cost alternative for head impact exposure monitoring and may also be expanded to other sports in future work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".