A biomechanical investigation of the surface stress of a synthetic femur using infrared thermography validated by strain gauge measurements
Bibliographic record
Abstract
As the North American population ages, there will be a massive increase in musculoskeletal impairments because these problems are most common in the elderly. A very common condition is osteoporosis, which can result in fractures. Therefore, the need for improved orthopaedic fracture repair implants is vital. Currently, the two main approaches in studying orthopaedic implants are strain gauge measurements and finite element modelling. This study introduces and validates a relatively new, non-destructive approach in analysing stress patterns in a biomechanics application. Lock-in infrared (IR) thermography calibrated with strain gauges was used to investigate the stress and strain patterns of a synthetic femur under dynamic loading. The femur was instrumented with strain gauges and tested using axial average forces of 1500N, 1800N, and 2100N at an adduction angle of 7 degrees to simulate the single-legged stance phase of walking. Three dimensional surface stress maps were obtained using an IR thermography versus strain gauge data with a Pearson correlation of R² = 0.99 and a slope ranging from 0.99 to 1.08, based on thermoelastic coefficient (Km) ranging from 1.067 x 10⁻⁵/MPa to 1.16 x 10⁻⁵/MPa, for the line of best fit. IR thermography detected bone peak stresses on the superior-posterior side of the femoral neck of 91.2MPa (at 1500 N), 96.0Mpa (at 1800 N), and 103.5MPa (at 2100 N). There was strong correlation between IR measured stresses and force along the anterior (R² = 0.87 to 0.99), posterior (R² = 0.81 to 0.99) and lateral (R² = 0.89 to 0.99) surface. This is the first study to provide an experimentally validated three dimensional stress map of a synthetic femur using IR thermography.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".