Non-Invasive Lactate Monitoring System Using Wearable Chipless Microwave Sensors With Enhanced Sensitivity and Zero Power Consumption
Bibliographic record
Abstract
Monitoring lactate levels is an established method for determining hyperlactatemia in critically ill patients and assessing aerobic fitness. It is a widely used gold-standard technique in both professional and serious amateur sports. Non-invasive real-time lactate monitoring offers significant advantages over the current technology of finger-prick blood sampling. Possible candidate technology for developing non-invasive real-time lactate monitoring should be highly sensitive, flexible, and capable of real-time monitoring of lactate levels in interstitial fluid or within specific working muscle groups depending on the type of sport. Herein we describe a planar, flexible, passive, chipless tag resonator that is electromagnetically coupled to a reader placed in proximity to the lactate sensor tag. The tag resonator is a thin metallic tracing that can be taped on the skin. The resonance frequency of the tag fluctuates proportionately with changing lactate concentrations in a solution mimicking human interstitial fluid with very high sensitivity. The spectrum of the tag is reflected in the spectrum of the reader, which is a planar microwave resonator designed at a different frequency. The reader could be embedded in a cellphone or an application-specific wearable device for data communication and processing. The tag can accurately and reproducibly measure lactate concentrations in the range of 1 to 10 mM, which is in the physiological range of lactate observed at rest and during intense physical activity. Furthermore, the chrematistics of this technology will allow monitoring of lactate in specific working muscle groups.
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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 | 0.001 |
| 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".