Toward Self-Powered Load Imbalance Detection for Instrumented Knee Implants Using Quadrant Triboelectric Energy Harvesters
Bibliographic record
Abstract
In this study, we proposed a triboelectric nanogenerator (TENG) as a pressure sensor to measure the load imbalance on the tibial tray. To detect the load imbalance, we proposed a segmented quadrant design. The TENG pressure sensors with various micropatterns, including pyramid, cylindrical, and bar patterns, are utilized to measure the axial forces with different sensitivity in different quadrants of the tibial tray. The functionality of the instrumented implant is examined through experimental testing on the package, evaluating its sensing capabilities and power harvesting. The relationship between each quadrant output and the axial force is determined, which enables characterizing the self-powered sensor performance. This relationship is then used to find the center of pressure (CoP), which is an important parameter for implant design. The detection of a shift in the CoP can be an early indication of loosening, which is one of the major causes of knee implant failure. In addition, we investigated the apparent power captured in resistance loads by applying a sinusoidal cyclic loading to the package harvester. Under an average walking load of 2200N, each quadrant of the harvester-package prototype produces an apparent power of approximately 5$\mu $W at 1Hz and 10$\mu $W at 2Hz.
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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 | 0.000 | 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".