Wentian Yi

Chenyu Tang, Wentian Yi, Muzi Xu et al.

In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device-skin coupling quality in electrophysiological sleep monitoring systems hinder the comfort and reliability of night wearing. Here, we report a washable, skin-compatible smart garment sleep monitoring system that captures local skin strain signals under weak device-skin coupling conditions without positioning or skin preparation requirements. A printed textile-based strain sensor array responds to strain from 0.1% to 10% with a gauge factor as high as 100 and shows independence to extrinsic motion artefacts via strain-isolating printed pattern design. Through reversible starching treatment, ink penetration depth during direct printing on garments is controlled to achieve batch-to-batch performance variation < 10%. Coupled with deep learning, explainable artificial intelligence (XAI), and transfer learning data processing, the smart garment is capable of classifying six sleep states with an accuracy of 98.6%, maintaining excellent explainability (classification with low bias) and generalization (95% accuracy on new users with few-shot learning less than 15 samples per class) in practical applications, paving the way for next-generation daily sleep healthcare management.

Chenyu Tang, Wentian Yi, Edoardo Occhipinti et al.

A human body digital twin (DT) is a virtual representation of an individual's physiological state, created using real-time data from sensors and medical test devices, with the purpose of simulating, predicting, and optimizing health outcomes through advanced analytics and simulations. The human body DT has the potential to revolutionize healthcare and wellness, but its responsible and effective implementation requires consideration of various factors. This article presents a comprehensive overview of the current status and future prospects of the human body DT and proposes a five-level roadmap for its development. The roadmap covers the development of various components, such as wearable devices, data collection, data analysis, and decision-making systems. The article also highlights the necessary support, security, cost, and ethical considerations that must be addressed in order to ensure responsible and effective implementation of the human body DT. The proposed roadmap provides a framework for guiding future development and offers a unique perspective on the future of the human body DT, facilitating new interdisciplinary research and innovative solutions in this rapidly evolving field.

Chenyu Tang, Shuo Gao, Cong Li et al.

Wearable silent speech systems hold significant potential for restoring communication in patients with speech impairments. However, seamless, coherent speech remains elusive, and clinical efficacy is still unproven. Here, we present an AI-driven intelligent throat (IT) system that integrates throat muscle vibrations and carotid pulse signal sensors with large language model (LLM) processing to enable fluent, emotionally expressive communication. The system utilizes ultrasensitive textile strain sensors to capture high-quality signals from the neck area and supports token-level processing for real-time, continuous speech decoding, enabling seamless, delay-free communication. In tests with five stroke patients with dysarthria, IT's LLM agents intelligently corrected token errors and enriched sentence-level emotional and logical coherence, achieving low error rates (4.2% word error rate, 2.9% sentence error rate) and a 55% increase in user satisfaction. This work establishes a portable, intuitive communication platform for patients with dysarthria with the potential to be applied broadly across different neurological conditions and in multi-language support systems.