A heart beat rate detection framework using multiple nanofiber sensor signals
Bibliographic record
Abstract
Although electrocardiogram (ECG) is one standard way for monitoring heart beat rate, there are of great interests in exploring other types of biophysical signals. A novel type of nanofiber (NF) sensor signals, as a potential alternative choice to ECG signals for heart beat monitoring, are investigated in this paper. To get the heart beat signal, three nano sensors are deployed at the wrist. However, detecting the heart beat rate (HBR) directly from the raw data is challenging because the signals of interest are masked by different types of noise. To address this concern, a two-step framework based on ensemble empirical mode decomposition (EEMD) and multiset canonical correlation analysis (MCCA) is proposed to extract the interesting signals. Further, a specific HBR detection method is presented based on peak detection and peak filtering. We apply the proposed framework to the real data collected from one subject performing 8 tasks, and the results demonstrate its effectiveness and potential in real applications.
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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 | 0.000 |
| 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".