A new approach for detecting and analyzing cutaneous reflexes during locomotion
Bibliographic record
Abstract
During human walking, due to their small amplitude, individual cutaneous reflex responses are difficult to detect in surface EMG recordings. In this study, we present a new algorithm to automatically detect individual cutaneous reflex responses and to extract their corresponding onset latency, amplitude, duration, and sign. To discriminate reflex responses from the intrinsic variability of the background EMG, each stimulated cycle is compared with 10 adjacent nonstimulated cycles, looking for consistent differences. In the first 200 ms after stimulation, reflex responses are detected when ≥ 9/10 of these differences are either positive or negative. This approach does not require amplitude thresholds or fixed time windows for reflex detection. To reduce false detections, a postprocessing step selects 50 nonstimulated cycles randomly, processes them through the algorithm as stimulated cycles, and establishes a minimal reflex duration criterion that it then used to validate the detected responses. Validated responses from an entire test session are then reported on a colormap (reflex activity map) from which specific responses can be identified and quantified. The new method was validated in ten participants, three cutaneous nerves, and two protocols (phase modulation and recruitment curves). Compared with the classical method, the new algorithm showed better performance in terms of detection accuracy, specificity, and reliability. Although tested here to evaluate cutaneous reflexes during human walking, the simplicity of this method is such that it could easily be used with other reflexes, signals, and preparations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".