Agility in Handball: Position- and Age-Specific Insights in Performance and Kinematics Using Proximity and Wearable Inertial Sensors
Bibliographic record
Abstract
Handball is a dynamic team sport characterized by high agility requirements, which feature complex motor-cognitive demands. The ability to meet these demands is critical for performance in handball but remains under-represented in research. Existing studies highlight that cognitive demands can strongly interfere with motor behavior, particularly in dynamic sport-specific movement tasks. Furthermore, high motor-cognitive load is associated with risk of lower limb injury. Therefore, to gain insight in the mechanisms between movement and performance dynamics in the presence of cognitive demands, this study investigated the performance of elite handball players in a novel planned and reactive agility task. Four FitLight proximity sensors (FitLight Corp, Aurora, ON, Canada) recorded execution time. Nine Noraxon Myomotion wearable inertial sensors (Noraxon U.S.A. Inc., Scottsdale, AZ, USA) tracked the motion of the players' trunk, pelvis, and lower extremities at 200 Hz. Execution time and kinematics were compared between adult and youth players and between different playing positions. Adult players demonstrated faster performance than youth players and exhibited differences in hip and knee flexion, potentially reflecting variations in acceleration and deceleration strategies. Backcourt players and wings demonstrated faster performance compared to pivots, who showed distinct patterns of hip, knee, and ankle flexion, possibly due to differences in body composition. These findings highlight the influence of motor and cognitive demands on agility performance and offer valuable insights into age- and position-specific differences among elite handball players. Furthermore, these findings support the use of wearable inertial sensors for the purpose of athlete evaluation. Future research should explore the implementation into athlete monitoring.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".