Implantable Biosensors for Real-time Strain and Pressure Monitoring
Bibliographic record
Abstract
Implantable biosensors were developed for real-time monitoring of pressure and strain in the human body. The sensor, which was wireless and passive, consisted of a soft magnetic material and a permanent magnet. When exposed to a low frequency AC magnetic field, the soft magnetic material generated secondary magnetic fields that also included the higher-order harmonic modes. Parameters of interest were determined by measuring the changes in the pattern of these higher-order harmonic fields, which was achieved by changing the intensity of a DC magnetic field generated by a permanent magnet. The DC magnetic field, or the biasing field, was altered by changing the separation distance between the soft magnetic material and the permanent magnet. For pressure monitoring, the permanent magnet was placed on the membrane of an airtight chamber. Changes in the ambient pressure deflected the membrane, altering the separation distance between the two magnetic elements and thus the higher-order harmonic fields. Similarly, the soft magnetic material and the permanent magnet were separated by a flexible substrate in the stress/strain sensor. Compressive and tensile forces flexed the substrate, changing the separation distance between the two elements and the higher-order harmonic fields. In the current study, both stress/strain and pressure sensors were fabricated and characterized. Good stability, linearity and repeatability of the sensors were demonstrated. This passive and wireless sensor technology may be useful for long term detection of physical quantities within the human body as a part of treatment assessment, disease diagnosis, or detection of biomedical implant failures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".