Adaptive Edge-Federated AI Framework for Contactless Menstrual Health Prediction Using Multimodal Physiological...

Area of Science:

• Biomedical Engineering
• Artificial Intelligence
• Digital Health

Background:

• Traditional menstrual tracking methods are often invasive, error-prone, and raise privacy concerns due to centralized data storage.
• Contact-based sensors and manual logs lack real-time accuracy and personalization capabilities.
• Existing digital health solutions struggle to balance predictive accuracy with user data privacy.

Purpose of the Study:

• To introduce an adaptive edge-federated artificial intelligence (AI) framework for accurate, real-time, and privacy-preserving menstrual health prediction.
• To overcome the limitations of traditional tracking methods by employing contactless biosensing and multimodal physiological intelligence.
• To develop a decentralized system that enhances menstrual health management while safeguarding sensitive reproductive health data.

Main Methods:

• Integration of radar-based respiration sensing, photoplethysmography (PPG), and LiDAR-assisted microvascular mapping for non-invasive physiological signal monitoring.
• Utilizing an adaptive edge-learning engine for local analysis and personalization of multimodal biosignals without raw data transfer.
• Implementing a federated optimization mechanism for secure, decentralized model training, ensuring data remains on user devices.

Main Results:

• Demonstrated accurate, real-time menstrual health prediction through contactless, multimodal biosensing.
• Achieved privacy preservation by analyzing data locally and training models in a decentralized manner.
• Enhanced prediction accuracy, particularly for individuals with irregular menstrual cycles, through adaptive AI intelligence.

Conclusions:

• The proposed adaptive edge-federated AI framework offers a next-generation, non-invasive approach to menstrual health management.
• This intelligent system combines edge intelligence, federated learning, and multimodal biosensing for a discreet and trustworthy reproductive health solution.
• The framework successfully addresses accuracy, latency, and privacy concerns, paving the way for advanced digital women's health technologies.