Correlation of Biomechanical Variables of Lower Extremity Movement During Functional Tests and Tasks in Youth League Football Players: Cross-Sectional Correlation Study
Bibliographic record
Abstract
Background: Football is the most widely played sport globally but carries a high risk of lower limb injuries, particularly among youth athletes. Real-time biomechanical monitoring can play a critical role in injury prevention. However, traditional lab-based systems are often complex and impractical for field use. Recent advances in wearable technology, such as inertial sensors and smart socks, provide more accessible solutions for movement analysis. The DAid smart sock system is a promising tool, but further evidence is needed to support its use in lower extremity functional assessments. Objective: This correlational study aimed to investigate the correlation between lower limb joint angles, muscle activity, and plantar pressure distribution during the "Single Leg Squat" (SLS) and its variations in youth football players, using wireless wearable sensors in a field-based setting. Methods: In total, 32 youth football players (16 male and 16 female individuals; mean age 14.6, SD 0.5 years) performed SLS movements while wearing the NOTCH inertial motion sensors, DAid smart socks (plantar pressure), and PLUX muscleBAN EMG system. Spearman correlation was used to explore relationships between hip, knee, and ankle joint kinematics; muscle activity (gluteus medius, gluteus maximus [GMx], vastus lateralis, and biceps femoris); and changes in center of pressure (COP) on the plantar surface. Results: A strong positive correlation was found between hip adduction and medial foot pressure (COP1X: ρ=0.785, P<.001). Knee flexion was strongly correlated with gluteus medius (ρ=0.809) and GMx (ρ=0.841) muscle activity. Hip internal rotation also showed moderate to strong correlations with both COP variables (COP1Y: ρ=0.585) and GMx activation (ρ=0.477). Significant gender-specific differences were identified: male individuals showed stronger correlations between joint angles and muscle activation, while female individuals demonstrated a strong correlation between knee flexion and overall plantar pressure (COP2W: ρ=0.818). Several moderate correlations (0.35<ρ<0.47) further confirmed interactions between joint movement, muscle activity, and plantar pressure. Conclusions: The findings support the feasibility and utility of wireless wearable sensors-including inertial measurement units and smart socks-for in-field biomechanical analysis in youth football players. The study confirmed key relationships between joint mechanics and plantar pressure distribution, suggesting their relevance in injury risk screening. The DAid smart sock system, in particular, demonstrated reliable performance for assessing medial-lateral loading patterns associated with hip and knee movement. These insights may help guide neuromuscular training and individualized injury prevention strategies in young athletes.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".