Artificial Intelligence Driven Assessment of Daoist Meditation and Breathing Practices Using Wearable IoT Devices

Abstract

The proliferation of wearable Internet of Things (IoT) devices has introduced novel opportunities for continuous and objective monitoring of individuals engaged in meditation and controlled breathing practices. This study presents an artificial intelligence (AI)-driven framework for assessing the physiological and psychological effects associated with Daoist meditation and breathing techniques through real-time multimodal wearable sensor data analysis. The proposed framework integrates multiple physiological and behavioral parameters, including heart-rate variability (HRV), respiratory rate (RR), blood oxygen saturation (SpO₂), skin temperature (ST), electrodermal activity (EDA), sleep patterns, and physical movement (PM). A comprehensive data-processing pipeline is developed, incorporating signal filtering, artefact removal, normalization, multithreaded segmentation, and feature extraction across heterogeneous sensing modalities. Machine-learning (ML) and deep-learning (DL) approaches are employed to model complex relationships between physiological responses and meditative states. The framework supports multiple analytical tasks, including meditation stage classification, breathing pattern recognition, relaxation-level assessment, and longitudinal monitoring of physiological variations. Statistical and computational analyses are performed to evaluate differences among resting conditions, controlled breathing sessions, and distinct meditation stages, with particular emphasis on autonomic nervous system (ANS) activity and stress-related physiological indicators. To enhance model interpretability, explainable artificial intelligence (XAI) techniques are incorporated to identify key physiological features contributing to predictive outcomes. Furthermore, the framework enables real-time personalized feedback by generating adaptive recommendations based on individual biometric characteristics and historical practice patterns. The integration of Daoist contemplative practices with wearable sensing technologies, artificial intelligence, and real-time analytics provides a novel approach for scientifically evaluating traditional mind–body practices. The proposed framework offers potential applications in digital health monitoring, stress management, personalized wellness systems, and the development of intelligent platforms for individualized contemplative practice optimization.