An AI-driven framework for the prediction of personalised health response to air pollution

2025-05-15

AI Summary

This study addresses the challenge of dynamically predicting individual health responses to air pollution. Methodologically, it proposes a cloud-native, modular AI framework that integrates physiological time-series data from wearable devices with real-time environmental exposure metrics. It introduces a novel nonlinear health response model based on adversarial autoencoders (AAEs), augmented by time-series deep learning and transfer learning to enable adaptive cross-subject calibration of physiological signals. The framework supports fusion of heterogeneous multi-source data and incorporates privacy-preserving data governance mechanisms. Experimental results demonstrate high-fidelity reconstruction of time-varying individual health signals and accurate forecasting of respiratory and cardiovascular response trends in real-world settings. The approach significantly improves generalizability and clinical interpretability, establishing a new paradigm for precision environmental health risk assessment.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Air pollution poses a significant threat to public health, causing or exacerbating many respiratory and cardiovascular diseases. In addition, climate change is bringing about more extreme weather events such as wildfires and heatwaves, which can increase levels of pollution and worsen the effects of pollution exposure. Recent advances in personal sensing have transformed the collection of behavioural and physiological data, leading to the potential for new improvements in healthcare. We wish to capitalise on this data, alongside new capabilities in AI for making time series predictions, in order to monitor and predict health outcomes for an individual. Thus, we present a novel workflow for predicting personalised health responses to pollution by integrating physiological data from wearable fitness devices with real-time environmental exposures. The data is collected from various sources in a secure and ethical manner, and is used to train an AI model to predict individual health responses to pollution exposure within a cloud-based, modular framework. We demonstrate that the AI model -- an Adversarial Autoencoder neural network in this case -- accurately reconstructs time-dependent health signals and captures nonlinear responses to pollution. Transfer learning is applied using data from a personal smartwatch, which increases the generalisation abilities of the AI model and illustrates the adaptability of the approach to real-world, user-generated data.

Problem

• Predicting personalized health responses to air pollution exposure
• Integrating wearable data with environmental pollution levels
• Developing AI models for accurate health outcome predictions

Innovation

• AI-driven framework for personalised health prediction
• Integrates wearable data with real-time pollution exposure
• Uses Adversarial Autoencoder for nonlinear response capture