Biomedical applications of wireless continuous wave near infrared spectroscopy
Bibliographic record
Abstract
Progress in research applications of near infrared spectroscopy (NIRS) and growing clinical interest have led to significant improvements in hardware and software. Amongst continuous wave systems the use of light emitting diodes, incorporation of spatially resolved optical geometry, algorithm refinement, development of portable systems, and wireless telemetry led first to portable NIRS instruments, then wearable systems, and now miniaturized self-contained devices. Measurement of absolute tissue oxygen saturation in both muscle and brain, and mapping of event related cortical hemodynamic responses using functional NIRS (fNIRS) have added specific measurement modalities. Wireless wearable systems and self-contained devices capable of measuring such modalities in addition to providing conventional monitoring of trends in oxygenated and deoxygenated haemoglobin concentration from baseline have increased the scope of research, expanded the population readily monitored, and opened new clinical avenues for applications involving NIRS. This review explores the range of biomedical applications reported using wireless continuous wave (CW) NIRS and fNIRS systems, summarizes key elements in the specification of available devices, and outlines potential future directions for clinical use of wireless NIRS technologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".