Characterization of a Packaged Triboelectric Harvester Under Simulated Gait Loading for Total Knee Replacement
Bibliographic record
Abstract
Load sensing total knee replacement (TKR) implants are useful tools for monitoring prosthesis health and providing quantitative data to support patient claims of pain or instability. However, powering such devices throughout the entire life of the knee replacement is a challenge, and self-powered telemetryviaenergy harvesting is an attractive solution. In this article, we implemented vertical contact mode triboelectric energy harvesters inside a knee implant package to generate the power required for embedded digitization and communications circuitry. The harvesters produce small-scale electric power from physiologically relevant loads transmitted through the knee. Experiments were performed on a joint motion simulator with an instrumented package prototype between the polyethylene bearing and tibial tray. The amplitude and the pattern of the power output varied with the input loadings. Under sinusoidal loading the maximum apparent power harvested was around 7$\mu$W at (50–2000)N whereas, under vertical compressive gait loading the harvesters generated around 10$\mu$W at average human knee loads of (151–1950)N and 20$\mu$W when the maximum applied load was increased by 25%. Full six degrees-of-freedom gait load/motions at 0.67 Hz produced 50% less power due to the slower loading rate. The results show the potential of developing a triboelectric energy harvesting-based self-powered instrumented knee implant for long-termin vivoknee joint force measurement.
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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 | 0.000 |
| 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".