The integration of non-invasive wearable sensing technologies in healthcare monitoring has revolutionized our approach to cardiovascular and sleep healthcare, as well as artificial intelligence, and their convergence has created unprecedented opportunities transforming our understanding of cardiovascular and sleep physiology. As these technologies rapidly evolve beyond traditional clinical settings into everyday life with unprecedented precision and accessibility, they offer transformative opportunities for early detection, continuous monitoring, and personalized interventions in cardiovascular diseases and sleep disorders, and enable large-scale population monitoring, novel digital biomarker discovery, and AI-driven predictive analytics. The TST recognizes the critical importance of this world-wide emerging field and invites submissions to this special issue dedicated to exploring the frontier advances and transformative applications of non-invasive wearable sensing technologies paired with artificial intelligence  in reshaping cardiovascular and sleep research.

Scope of Topics

This special issue focuses on four major directions within non-invasive wearable sensing for cardiovascular and sleep medicine, each encompassing a range of specific topics:

Sensing Technologies and Frontier Advances

• Next-generation sensor technologies for non-obtrusive physiological monitoring
• Advanced sensor development and miniaturization for cardiovascular and sleep monitoring
• Multi-modal sensing approaches combining complementary physiological measurements
• Validation studies comparing wearable sensors against gold-standard clinical measures
• Sensor fusion methodologies for comprehensive physiological assessment
• Energy-efficient sensing architectures for extended monitoring durations
• Validation methodologies across diverse populations and real-world conditions
• Systems for simultaneous monitoring of cardiac, vascular, neural, and respiratory parameters

Biophysical Mechanisms, Modeling and Validations

• Mathematical and data-driven approaches for disease mechanism elucidation
• Digital biomarker development and validation for cardiovascular and sleep disorders
• Multi-modal sensing and modeling combining complementary biophysical measurements
• Biophysical frameworks for understanding sleep-disordered patterns and their cardiovascular impacts
• Theoretical foundations for cardio-cerebral coupling during sleep apnea episodes
• Mathematical-physical principles to quantify cardiac-autonomic coupling and sleep disorder
• Novel interpretations of biological signals through non-linear dynamics and Partial Differential Equations
• Theoretical frameworks for understanding physiological synchronization across systems

Artificial Intelligence and Data-driven Frameworks

• Deep learning approaches for feature extraction from wearable sensor data streams
• Transfer learning techniques to leverage knowledge across different sensing modalities
• Deep learning models for detecting subtle physiological coupling phenomena
• Explainable AI frameworks for transparent cardiovascular and sleep parameter estimation
• Unsupervised anomaly detection in longitudinal physiological monitoring
• Federated learning approaches for privacy-preserving model development
• Time-series analysis methods for circadian rhythm and sleep architecture characterization
• Generative models for synthetic data augmentation and rare event simulation

Paradigm-Shifting Digital Biomarkers and Transformative Applications

• Large-scale public datasets for algorithm development and validation
• Population-level studies using wearable technologies revealing novel physiological interaction patterns
• Digital biomarker discovery through multi-dimensional physiological signal analysis
• Early warning systems for cardiovascular events based on continuous monitoring
• Sleep quality assessment and disorder detection in natural environments
• Longitudinal tracking of cardiovascular and sleep health metrics
• Personalized feedback mechanisms based on individual baselines and trends
• Open-source platforms for collaborative algorithm development and benchmarking

Submission Guidelines

Authors should prepare papers in accordance with the format requirements of Tsinghua Science and Technology, with reference to the Instruction given at  https://www.sciopen.com/journal/1007-0214, and submit the complete manuscript through the online manuscript submission system at https://mc03.manuscriptcentral.com/tst with manuscript type as “Special Issue on Breaking Boundaries: Non-invasive Wearable Sensing Technologies Reshaping Cardiovascular and Sleep Healthcare: Frontier Advances and Transformative Applications”.

Important Dates

Deadline for submissions: March 31, 2026

Guest Editors

Kai Xing

School of Computer Science, University of Science and Technology of China, China

kxing@ustc.edu.cn

Hui Yang

School of Life Sciences, Northwestern Polytechnical University, China

kittyyh@nwpu.edu.cn

Hanqiang Deng

School of Medicine, Yale University, USA

hanqiang.deng@yale.edu

Yewei Wang

Gangarosa Department of Environmental Health, Emory University, USA

yewei.wang@emory.edu

Jing Zhou

Department of Physiology and Pathophysiology, Peking University Health Science Center, China

jzhou@bjmu.edu.cn

Zonglai Jiang

School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China

zljiang@sjtu.edu.cn

Guixue Wang

Bioengineering College, Chongqing University, China

wanggx@cqu.edu.cn

Yuan Bian

Emergency Medicine, Qilu Hospital of Shandong University, China

Bianyuan9307@msn.com