A smart knee implant using triboelectric energy harvesters
Bibliographic record
Abstract
Although the number of total knee replacement (TKR) surgeries is growing rapidly, functionality and pain-reduction outcomes remain unsatisfactory for many patients. Continual monitoring of knee loads after surgery offers the potential to improve surgical procedures and implant designs. The goal of this study is to characterize a triboelectric energy harvester under body loads and to design compatible frontend electronics to digitize the load data. The harvester prototype would be placed between the tibial component and polyethylene bearing of a TKR implant. The harvester generates power from the compressive load. To examine the harvester output and the feasibility of powering a digitization circuitry, a triboelectric energy harvester prototype is fabricated and tested. An axial tibiofemoral load profile from normal walking (gait) is approximated as a 1 Hz sine wave signal and is applied to the harvester. Because the root mean square of voltages generated via this phenomenon is proportional to the applied load, the device can be simultaneously employed for energy harvesting and load sensing. With an approximated knee cyclic load of 2.3 kN at 1 Hz, the harvester generated output voltage of 18 V rms, and an average power of 6 μ W at the optimal resistance of 58 MΩ. The harvested signal is rectified through a negative voltage converter rectifier and regulated through a linear-dropout regulator with a combined efficiency of 71%. The output of the regulator is used to charge a supercapacitor. The energy stored in the supercapacitor is used for low resolution sensing of the load through a peak detector and analog-to-digital converter. According to our analysis, sensing the load several times a day is feasible by relying only on harvested power. The results found from this work demonstrate that triboelectric energy harvesting is a promising technique for self-powering load sensors inside knee implants.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".