Development Design and Signal Processing Algorithm Optimization of Traditional Chinese Medicine Pulse Acquisition System Based on CP301 Sensor
Bibliographic record
Abstract
Based on the "Three Parts and Nine Symptoms" pulse diagnosis in TCM, a wristband pulse signal acquisition device with adjustable pressure is designed. It uses soft PVDF sensors for comfort and adjustable Cun-Guan-Chi positions for simulating TCM pulse diagnosis. To address weak, interference-prone signals, impedance conversion, bandpass filtering, and amplification circuits are integrated. A six-channel digital acquisition system and PC-based interface are developed for signal processing. An improved EMD algorithm removes pseudo-baselines, and dimensionality reduction is achieved by extracting features. Pulse signals generated by the Nektar 1D model are classified using SOM and decision tree algorithms, with SOM showing higher accuracy. The hardware includes optimized PVDF sensors, two-stage amplification, 50Hz notch filters, and fourth-order bandpass filters, with FPGA-based six-channel acquisition. The software, developed on LabVIEW, manages initialization, data acquisition, storage, and calibration. While objective signal acquisition is achieved, hardware optimization, portability, and signal processing need improvement. Enhancing the TCM pulse diagnosis feature database will further promote objectivity in TCM pulse diagnosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".