Biomechanical stress maps of an artificial femur obtained using a new infrared thermography technique validated by strain gages
Bibliographic record
Abstract
Femurs are the heaviest, longest, and strongest long bones in the human body and are routinely subjected to cyclic forces. Strain gages are commonly employed to experimentally validate finite element models of the femur in order to generate 3D stresses, yet there is little information on a relatively new infrared (IR) thermography technique now available for biomechanics applications. In this study, IR thermography validated with strain gages was used to measure the principal stresses in the artificial femur model from Sawbones (Vashon, WA, USA) increasingly being used for biomechanical research. The femur was instrumented with rosette strain gages and mechanically tested using average axial cyclic forces of 1500 N, 1800 N, and 2100 N, representing 3 times body weight for a 50 kg, 60 kg, and 70 kg person. The femur was oriented at 7° of adduction to simulate the single-legged stance phase of walking. Stress maps were also obtained using an IR thermography camera. Results showed good agreement of IR thermography vs. strain gage data with a correlation of R(2)=0.99 and a slope=1.08 for the straight line of best fit. IR thermography detected the highest principal stresses on the superior-posterior side of the neck, which yielded compressive values of -91.2 MPa (at 1500 N), -96.0 MPa (at 1800 N), and -103.5 MPa (at 2100 N). There was excellent correlation between IR thermography principal stress vs. axial cyclic force at 6 locations on the femur on the lateral (R(2)=0.89-0.99), anterior (R(2)=0.87-0.99), and posterior (R(2)=0.81-0.99) sides. This study shows IR thermography's potential for future biomechanical applications.
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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 | 0.000 |
| 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".