Development of a Wireless Telemetry Sensor Device to Measure Load and Deformation in Orthopaedic Applications
Bibliographic record
Abstract
Due to sensor size and supporting circuitry, in-vivo load and deformation measurements are currently restricted to applications within larger orthopaedic implants. The objective of this study is to repurpose a commercially available low-power, miniature, wireless, telemetric, tire-pressure sensor (FXTH87) to measure load and deformation for future use in orthopaedic and biomedical applications. The capacitive transducer membrane was modified, and compressive deformation was applied to the transducer to determine the sensor signal value and the internal resistive force. The sensor package was embedded within a deformable enclosure to illustrate potential applications of the sensor for monitoring load. To reach the maximum output signal value, sensors required compressive deformation of 350 ± 24 µm. The output signal value of the sensor was an effective predictor of the applied load on a calibrated plastic strain member, over a range of 35 N. The FXTH87 sensor can effectively sense and transmit load-induced deformations. The sensor does not have a limit on loads it can measure, as long as deformation resulting from the applied load does not exceed 350 µm. The proposed device presents a sensitive and precise means to monitor deformation and load within small-scale, deformable enclosures.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".