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Deep multi-modal features based spatio-temporal video regression for non-invasive hemoglobin estimation.

Ata Güneş¹, Ufuk Bal², Selami Beyhan³

¹Department of Electrical and Electronics Engineering, Faculty of Engineering, Izmir Democracy University, İzmir, Turkey.

Medical & Biological Engineering & Computing

|July 2, 2026

Summary

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This summary is machine-generated.

Non-invasive hemoglobin estimation from facial videos offers a promising alternative to blood tests. This machine learning approach uses facial video analysis for accurate anemia screening and remote health monitoring.

Area of Science:

Biomedical Engineering
Artificial Intelligence in Healthcare
Medical Diagnostics

Background:

Hemoglobin concentration is crucial for diagnosing anemia and monitoring health.
Conventional blood sampling is invasive, limiting accessibility and patient comfort.
Non-invasive methods using machine learning are emerging for point-of-care and remote healthcare.

Purpose of the Study:

To develop and validate a non-invasive method for estimating hemoglobin levels from facial videos.
To explore multi-modal feature extraction and ensemble learning for improved accuracy.
To assess the system's potential for anemia screening and remote patient monitoring.

Main Methods:

A dataset of 260 participants was used.
Features were extracted using pre-trained convolutional neural networks (MobileNetV2, ResNet152), remote photoplethysmography (rPPG) signals, and color statistics.

Keywords:

Anemia diagnosis Computer vision Deep learning Hemoglobin concentration estimation Machine learning Non-invasive measurement Remote photoplethysmography Video regression

Related Experiment Videos

Machine learning models including XGBoost, Random Forest, and Stacking Regressor were employed for hemoglobin estimation.

Main Results:

The Stacking Regressor achieved the best performance (MAE: 0.7754 g/dL, R²: 0.5852).
Combining ResNet152 features with XGBoost yielded comparable results (MAE: 0.6635 g/dL, R²: 0.4977).
Multi-modal feature strategies significantly outperformed single-modality approaches.

Conclusions:

The proposed video-based hemoglobin estimation system achieves clinically relevant accuracy.
The system demonstrates potential for anemia screening and remote patient monitoring.
This non-invasive approach shows promise compared to existing literature methods and point-of-care instruments.