Signal quality before prediction
Can a mobile system recognize poor illumination, motion, weak optical coupling, and other acquisition failures before producing an estimate that appears more certain than the data allow?
01 / Research focus
Everyday devices as health sensors.
Explore Masud Rabbani’s research on smartphone-based, non-invasive physiological sensing for blood cells, glucose, oxygen saturation, blood pressure, and multi-parameter mHealth.
9 related publications · 1 projectResearch overview
Non-Invasive Health Sensing with Smartphones and AI
My non-invasive health-sensing research investigates whether smartphones and other accessible devices can estimate physiological measures that usually require specialized equipment, contact sensors, or laboratory procedures. The portfolio includes studies of white blood cells, glucose and HbA1c, oxygen saturation, blood pressure, hemoglobin, and multi-parameter monitoring. These projects combine optical sensing, camera video, photoplethysmography, signal-quality controls, and machine learning within mobile-health workflows.
The motivation is access. A measurement that can be collected with a familiar device may support more frequent monitoring, faster screening research, and care outside a centralized laboratory. But accessibility is only useful when measurement quality is visible and uncertainty is handled honestly. Smartphone cameras, illumination, skin-device contact, motion, ambient light, hardware variation, and population differences can all alter the observed signal. For that reason, the research program treats acquisition design and validation as core computational problems—not as details to address after a model is built.
Published work in this area includes a smartphone-based white-blood-cell counter using blue light and a static magnetic field, camera-based remote photoplethysmography for contactless oxygen-saturation monitoring, facial-video analysis for blood-pressure estimation, and quantum-machine-learning experiments for glucose and HbA1c estimation. A broader non-invasive mHealth platform and the dDream program connect individual measurements into a scalable multi-parameter architecture. Together, these studies explore what is technically possible while identifying the evidence still needed before a prototype could influence clinical decisions.
Questions
The problems guiding this research.
Generalization across people and phones
How do models behave across skin characteristics, health conditions, camera sensors, operating systems, and collection environments that were not represented in the original study?
Useful multi-parameter monitoring
Which measurements can share a practical acquisition workflow, and how should a mobile platform communicate trends, confidence, and the need for confirmatory testing?
Research method
From a measurable signal to a defensible result.
- 01
Design the acquisition
Select camera, light, contact, facial-video, or PPG configurations around the physiological mechanism being studied and document the conditions that affect signal quality.
- 02
Control the signal
Detect motion and lighting artifacts, normalize device-dependent measurements, and extract physiological representations before asking a model to predict an outcome.
- 03
Model and benchmark
Compare classical, deep-learning, and quantum-machine-learning approaches against appropriate ground truth rather than reporting one favorable model in isolation.
- 04
Test translation limits
Evaluate repeatability, subgroup performance, device variation, usability, and calibration needs so the boundary between research prototype and clinical tool is explicit.
Journal article · 2026
Toward a Noninvasive mHealth Platform
Nafi Us Sabbir Sabith, Sayed Mashroor Mamun, Masud Rabbani, and Sheikh Iqbal Ahamed. “Toward a Noninvasive mHealth Platform.” Computer 59, no. 1 (2026): 95–107. https://doi.org/10.1109/MC.2025.3600446.
Journal article · 2025
Smartphone based non invasive real time white blood cell counter leveraging blue light and static magnetic field
Nafi Us Sabbir Sabith, Masud Rabbani, Kazi Shafiul Alam, and Sheikh Iqbal Ahamed. “Smartphone based non invasive real time white blood cell counter leveraging blue light and static magnetic field.” Scientific Reports 15, 1594 (2025). https://doi.org/10.1038/s41598-024-81459-y.
Journal article · 2025
Noninvasive estimation of blood glucose and HbA1c using Quantum Machine Learning technique
Parama Sridevi, Masud Rabbani, Md Hasanul Aziz, Paramita Basak Upama, Sayed Mashroor Mamun, Rumi Ahmed Khan, and Sheikh Iqbal Ahamed. “Noninvasive estimation of blood glucose and HbA1c using Quantum Machine Learning technique.” Machine Learning with Applications 19, 100626 (2025). https://doi.org/10.1016/j.mlwa.2025.100626.
Journal article · 2025
PulseSight: A novel method for contactless oxygen saturation (SpO2) monitoring using smartphone cameras, remote photoplethysmography and machine learning
Arefin, Kazi Zawad, Kazi Shafiul Alam, Sayed Mashroor Mamun, Nafi Us Sabbir Sabith, Masud Rabbani, Parama Sridevi, and Sheikh Iqbal Ahamed. "PulseSight: A novel method for contactless oxygen saturation (SpO2) monitoring using smartphone cameras, remote photoplethysmography and machine learning." Smart Health (2025): 100542. https://doi.org/10.1016/j.smhl.2025.100542.
Journal article · 2024
UbiHeart: A novel approach for non-invasive blood pressure monitoring through real-time facial video
Alam, Kazi Shafiul, Sayed Mashroor Mamun, Masud Rabbani, Parama Sridevi, and Sheikh Iqbal Ahamed. "UbiHeart: A novel approach for non-invasive blood pressure monitoring through real-time facial video." Smart Health (2024): 100473. https://doi.org/10.1016/j.smhl.2024.100473.
Conference paper · 2026
dDream: A Smartphone-Based Comprehensive and Scalable Multi-Parameter Physiological Monitoring Platform
Mamun, Sayed Mashroor, Kazi Shafiul Alam, Nafi Us Sabbir Sabith, Kazi Zawad Arefin, Masud Rabbani, and Sheikh Iqbal Ahamed. "dDream: A Smartphone-Based Comprehensive and Scalable Multi-Parameter Physiological Monitoring Platform." In 2026 IEEE 50th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 1-10. IEEE, 2026. https://doi.org/10.1109/COMPSAC69091.2026.00457.
Conference paper · 2025
A Survey on Non-Invasive Computing: Neurological-Hematological Framework for Early Infection and Stroke Detection with Future Directions
Masud Rabbani, Nafi Us Sabbir Sabith, and Sheikh Iqbal Ahamed. “A Survey on Non-Invasive Computing: Neurological-Hematological Framework for Early Infection and Stroke Detection with Future Directions.” 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), 824–833 (2025). https://doi.org/10.1109/COMPSAC65507.2025.00110.
Conference paper · 2024
Towards a Multi-model Comparative Study for Non-invasive Hemoglobin Level Prediction from Fingertip Video
Sabith, Nafi Us Sabbir, Parama Sridevi, Kazi Zawad Arefin, Masud Rabbani, and Sheikh Iqbal Ahamed. "Towards a Multi-model Comparative Study for Non-invasive Hemoglobin Level Prediction from Fingertip Video." In 2024 IEEE International Conference on Digital Health (ICDH), pp. 172-180. IEEE, 2024. https://doi.org/10.1109/ICDH62654.2024.00038.
Conference paper · 2022
Towards a Survey on Universal Human Vital Signs with prototype for Detection and Record Electronically Acceptable Medical-data (dDream)
Masud Rabbani, Kazi Shafiul Alam, Lin He, Shiyu Tian, Mohammad Syam, Iysa Iqbal, Anushka Kolli, Hansika Kolli, Syeda Shefa, Bipasha Sobhani, Paramita Basak Upama, and Sheikh Iqbal Ahamed. “Towards a Survey on Universal Human Vital Signs with prototype for Detection and Record Electronically Acceptable Medical-data (dDream).” 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 502–511 (2022). https://doi.org/10.1109/COMPSAC54236.2022.00094.
Responsible translation
Promising research still needs careful validation.
The long-term goal is not to replace established clinical testing with an unverified phone estimate. It is to determine where accessible sensing can support research, screening, longitudinal context, or a prompt for confirmatory care. A useful system must make failed acquisition and uncertainty as visible as a successful result.
Moving from a promising study to trustworthy use requires prospective evaluation, diverse participants, comparisons against recognized reference measurements, external replication, privacy protection, and regulatory review where appropriate. These requirements guide how the work is described and which claims are intentionally avoided.
Continue exploring