ArticleBiosensors2022
A Smartphone-Based Biosensor for Non-Invasive Monitoring of Total Hemoglobin Concentration in Humans with High Accuracy.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Applications of Machine Learning in Noninvasive Anemia Diagnosis: A Systematic Review Based on Cross-Industry Standard Process for Data Mining.Health science reports · 2026Review
- Optical Biosensors-Principles of Operation and Applications.Micromachines · 2026Review
- An approach to machine learning-based non-invasive hemoglobin estimation using multi-wavelength PPG signal features.Frontiers in physiology · 2026Article
- Smartphone-based biosensing: a review of optical imaging, microfluidic integration, and AI-enhanced analysis.Mikrochimica acta · 2025Review
- Ensemble Extreme Learning Machine Method for Hemoglobin Estimation Based on PhotoPlethysmoGraphic Signals.Sensors (Basel, Switzerland) · 2024Article
- Label-Free Biosensor.Biosensors · 2023Article
- A calibration method for smartphone camera photophlethysmography.Frontiers in digital health · 2023Article
- IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review.Healthcare (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In this paper, we propose a smartphone-based biosensor for detecting human total hemoglobin concentration in vivo with high accuracy. Compared to the existing biosensors used to measure hemoglobin concentration, the smartphone-based sensor utilizes the camera, memory, and computing power of the phone. Thus, the cost is largely reduced. Compared to existing smartphone-based sensors, we developed a highly integrated multi-wavelength LED module and a specially designed phone fixture to reduce spatial errors and motion artifacts, respectively. In addition, we embedded a new algorithm into our smartphone-based sensor to improve the measurement accuracy; an L*a*b* color space transformation and the "a" parameter were used to perform the final quantification. We collected 24 blood samples from normal and anemic populations. The adjusted
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.