Exploration of Inertial Sensor-Derived Kinematic Predictors for Dynamic Balance Assessment in the Active Adult
Bibliographic record
Abstract
Purpose: The modified Star Excursion Balance Test (mSEBT) is a clinical tool for dynamic balance assessment. While researchers have examined individual joint kinematic predictors of dynamic balance performance, limited data exist on body and joint sway during the test execution. Further investigation of kinematic predictors’ influence on dynamic balance is needed to improve assessment methodologies. This study aimed to examine the relationship between the center of mass and lower limb kinematics as predictors of mSEBT performance. Patients and Methods: Twenty-seven participants with no history of lower limb joint instability were recruited for this study. The inertial sensors were positioned on the non-dominant leg: trunk, thigh, shank, and foot. The participants completed the mSEBT barefoot following standardized practice trials with three test trials per direction. The reach distance and lower limb kinematic data were recorded. Spearman rank’s correlation and stepwise multiple regression analyses identified key predictors of dynamic balance performance. Results: Ankle dorsiflexion was a strong predictor of normalized reach distance in the anterior direction (r² = 0.34, p < 0.001). Body center of mass displacement was the strongest predictor of posteromedial and posterolateral reach (r² = 0.55, p < 0.001; r 2 = 0.57, p = < 0.001, respectively). The combined influence of the body center of mass and hip flexion accounted for 65% of the variance in the posterior reach assessments. Conclusion: This study highlights the key biomechanical factors that influence dynamic balance, focusing on the interaction between joint mobility and segmental control. Ankle dorsiflexion is critical for anterior balance, whereas hip flexion and body center of mass displacement are essential for posterior balance. Keywords: postural balance, accelerometry, lower extremity, biomechanical phenomena
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".