Motion artifact removal from muscle NIR Spectroscopy measurements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Near Infrared Spectroscopy (NIRS) is an optical method used for monitoring local tissue oxygenation and hemodynamics. This method is becoming increasingly popular in clinical and research applications. One important shortcoming of NIRS is an extreme sensitivity to motion artifacts. In this paper, we propose a new algorithm for removing movement artifacts from NIRS signals. We applied wavelet transform and then used the signal representation in the wavelet domain to isolate the artifacts and remove them using statistical testing. We tested this method on both simulated and experimental NIRS data acquired in a leg fracture operation and compared the results with those of median filtering, FIR filtering and wavelet SURE threshold estimation methods. The results show that the method significantly reduces the artifacts without distorting the signal in artifact free regions and outperforms other artifact removal methods.
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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) | 0.001 | 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 it