Reconfigurable Point-of-Care System for Hemoglobin Estimation From Photoplethysmogram
Bibliographic record
Abstract
Hemoglobin is an integral part of blood, and its abnormality indicates various diseases. Different noninvasive methods are developed to predict the concentration of hemoglobin. Previous studies verified the potential of photoplethysmogram (PPG) signals in estimating the health parameter. Although different hardware tools have been used to develop digital systems over the years, they lack the reconfigurability feature needed to develop a point-of-care (POC) system. In this study, a field programmable gate array (FPGA)-based reconfigurable hardware system, including preprocessor, memory and control, feature extractor and classifier subsystems, is designed targeting Zynq 7000 Zedboard. The system utilizes six features extracted from the PPG signals collected using DCM08 PPG sensor and linear regression classifier model for prediction. PPG signals based on four different wavelengths of light are tested, and the best result has been achieved with infrared light having a wavelength of 940 nm, which will help to design PPG sensors for wearable and medical devices. The mean absolute error with this wavelength is 2.55 g/L with an error rate of 1.78%. The power consumption analysis validates the designed system to be a low-power device. The designed processor can be used as a POC system, and due to its reconfigurable advantage, the system can be further improved by adding other health parameter predictions and disease detection.
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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".