Inertial measurement units for the detection of the effects of simulated leg length inequalities
Bibliographic record
Abstract
BACKGROUND: Leg length inequalities (LLI) are a common condition that can be associated with detrimental effects like low back pain and osteoarthritis. Inertial measurement units (IMUs) offer the chance to analyze daily activities outside a laboratory. Analyzing the kinematic effects of (simulated) LLI on the musculoskeletal apparatus using IMUs will show their potentiality to improve the comprehension of LLI. METHODS: Twenty healthy participants with simulated LLI of 0-4 cm were analyzed while walking with an inertial sensor system (MyoMotion). Statistical evaluation of the peak anatomical angles of the spine and legs were performed using repeated measurement (RM) ANOVA or their non-parametric test versions (Friedman test). RESULTS: Lumbar lateral flexion and pelvic obliquity increased during the stance phase of the elongated leg and decreased during its swing phase. The longer limb was functionally shortened by higher hip and knee flexion, higher hip adduction, dorsiflexion, and lower ankle adduction. Finally, the shorter leg was lengthened by higher hip and knee extension, hip abduction, ankle plantarflexion, and decreased hip adduction. CONCLUSION: We found differing compensation strategies between the different joints, movement planes, gait phases, and amounts of inequality. Overall the shorter leg is lengthened and the longer leg is shortened during walking, to retain the upright posture of the trunk. IMUs were helpful and precise in the detection of anatomical joint angles and for the analysis of the effects of LLI.
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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 | 0.000 |
| 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) | 0.004 | 0.001 |
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".