Adaptive multimodal migraine prediction system

Abstract

An adaptive multimodal migraine prediction system using energy-conscious sensing and personalized learning, comprising: a multimodal sensor module with a variety of physiological sensors for capturing a user's physiological parameters and a variety of environmental sensors for capturing environmental parameters in the user's environment; a signal preprocessing module configured to receive raw sensor signals from the multimodal sensor module and perform artifact removal and noise filtering on the raw sensor signals; a feature extraction module configured to: extract time- and frequency-domain features from the filtered signals, encode sparse event-based features, and transform the raw sensor signals into compact, informative feature vectors optimized for low memory and energy consumption;an edge processing unit connected to the feature extraction module and configured to perform local processing of feature vectors without dependence on cloud infrastructure; a TinyML core processing unit integrated into the edge processing unit and configured to: implement quantized and compressed neural networks for machine learning, perform real-time migraine risk classification or regression, and predict migraine attack windows without cloud dependency; a multimodal fusion module configured to integrate physiological and environmental features using multimodal data fusion and perform the fusion at the feature or decision level;a prediction module integrated into the multimodal fusion module and configured to generate predictions of migraine onset based on interactions between physiological and environmental indicators; an adaptive and incremental learning module integrated into the prediction module and configured to update model parameters locally based on individual user migraine episodes, adapt to user lifestyle changes, learn user-specific trigger patterns, and personalize prediction accuracy for individual users without requiring model retraining;An explainable AI interpretation module connected to the prediction module and configured to translate model outputs into human-readable rules, identify the key risk factors for migraine, generate interpretable migraine risk scores, and ensure transparency regarding the generation of migraine risk alerts; and an alert and feedback module connected to the explainable AI interpretation module and configured to detect a high risk of migraine onset, deliver alerts via at least one of the following channels: smartphone notifications, wearable displays, smartwatch displays, haptic and acoustic signals, and provide recommendations for preventive measures for the user.

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