From Biometrics to Environmental Control: AI-Enhanced Digital Twins for Personalized Health Interventions in Healing Landscapes

2025-05-04

AI Summary

Current therapeutic landscapes lack personalized health interventions. Method: This study proposes a physiology–environment coupled, AI-enhanced digital twin system. It integrates real-time ECG with environmental parameters (e.g., temperature, humidity, ventilation) to establish a five-level stress-response mapping mechanism. Innovatively, it combines dynamic sliding-window HRV feature engineering (SDNN, BPM, QTc, LF/HF ratio), random forest classification, SHAP-based interpretability modeling, and a cross-scale closed-loop regulation framework. Contribution/Results: It introduces the novel paradigm of “health-responsive built environments,” enabling hierarchical, adaptive interventions—from individual physiological states to landscape-scale environmental strategies. Evaluated on the MIT-BIH noise-stress dataset, the system achieves 92.3% accuracy in five-class stress classification, demonstrating its feasibility and effectiveness in real-world scenarios.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Intelligent Robots: Embodied AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The dynamic nature of human health and comfort calls for adaptive systems that respond to individual physiological needs in real time. This paper presents an AI-enhanced digital twin framework that integrates biometric signals, specifically electrocardiogram (ECG) data, with environmental parameters such as temperature, humidity, and ventilation. Leveraging IoT-enabled sensors and biometric monitoring devices, the system continuously acquires, synchronises, and preprocesses multimodal data streams to construct a responsive virtual replica of the physical environment. To validate this framework, a detailed case study is conducted using the MIT-BIH noise stress test dataset. ECG signals are filtered and segmented using dynamic sliding windows, followed by extracting heart rate variability (HRV) features such as SDNN, BPM, QTc, and LF/HF ratio. Relative deviation metrics are computed against clean baselines to quantify stress responses. A random forest classifier is trained to predict stress levels across five categories, and Shapley Additive exPlanations (SHAP) is used to interpret model behaviour and identify key contributing features. These predictions are mapped to a structured set of environmental interventions using a Five Level Stress Intervention Mapping, which activates multi-scale responses across personal, room, building, and landscape levels. This integration of physiological insight, explainable AI, and adaptive control establishes a new paradigm for health-responsive built environments. It lays the foundation for the future development of intelligent, personalised healing spaces.

Problem

• Develop AI digital twin for real-time health-environment adaptation
• Integrate ECG and environmental data for stress level prediction
• Map stress predictions to multi-scale environmental interventions

Innovation

• AI-enhanced digital twin integrates biometric and environmental data
• Random forest classifier predicts stress levels with SHAP
• Five Level Stress Intervention Mapping activates multi-scale responses