Neuromuscular Performance and Injury Risk Assessment Using Fusion of Multimodal Biophysical and Cognitive Data
Bibliographic record
Abstract
Athletes rely on rationally bounded decisions of coaches and sports physicians to optimize performance, improve well-being, and reduce risk of injuries. These decisions are subjective or require costly tests that are not necessarily predictive of in-game performance or cannot predict risk of injury. This paper presents an approach to remedy this shortcoming by providing coaches and sports medicine teams with reliable tools for objective, quantitative assessment of in-field performance and risk of injury. The proposed method uses advanced physiological signal processing, data driven modelling, and multi-modal data fusion techniques applied to data recorded from unobtrusive wearable sensors in tasks and conditions that closely resemble those observed in the field during training or even a game. We postulate that the required data for this prediction task include joint kinematics from inertial measurement units or accelerometers, muscle surface electromyography, ground reaction force, electrocardiography, heart rate and heart rate variability, oxygen saturation, respiration rate, and pupillometry data. The required analysis methods include physiological signal processing, feature extraction, and data-driven modeling techniques to estimate neuromuscular properties, identify joint and leg stiffness, and assess cognitive performance from pupillometry and heart rate variability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".