arXiv subjects

Muzi Xu

Publications and source records attributed to Muzi Xu.

6 recordsLinked to original sources

arXiv2025

Biomimetic Metamaterial-based Interface for Decoding Heterogeneous Mechanodermal Activity

Human skin acts as a dynamic biomechanical interface that conveys critical physiological and behavioural information through spatiotemporally distributed deformations. Due to the limited capabilities of current sensing technologies, the spatiotemporal diversity of its mechanical cues has remained underutilised to date, preventing these mechanisms from being used to capture and decode the full spectrum of underlying physiological states. In this work, we define this heterogeneous set of mechanical signals as mechanodermal activity (MDA) and introduce the biomimetic metamaterial-based interface (BMMI), an engineered auxetic metamaterial substrate that reproduces the microrelief and mechanoreceptor architecture of natural skin. The BMMI allows selective capture of diverse MDA signals from adjacent skin regions with simultaneous signal amplification and noise suppression, and permits straightforward modulation to accommodate various scenarios. Combined with bespoke algorithms, the wireless BMMI device decodes MDA accurately and robustly for multimodal communication interfaces, unleashing applications in healthcare monitoring and human-machine interaction.

q-bio.NC↗

arXiv2025

Monitoring Nitric Oxide in Trigeminal Neuralgia Rats with a Cerium Single-Atom Nanozyme Electrochemical Biosensor

Trigeminal neuralgia (TN) is the most common neuropathic disorder; however, its pathogenesis remains unclear. A prevailing theory suggests that nitric oxide (NO) may induce nerve compression and irritation via vascular dilation, thereby being responsible for the condition, making real-time detection of generated NO critical. However, traditional evaluations of NO rely on indirect colorimetric or chemiluminescence techniques, which offer limited sensitivity and spatial resolution for its real-time assessment in biological environments. Herein, we reported the development of a highly sensitive NO electrochemical biosensor based cerium single-atom nanozyme (Ce1-CN) with ultrawide linear range from 1.08 nM to 143.9 {\mu}M, and ultralow detection limit of 0.36 nM, which enables efficient and real-time evaluation of NO in TN rats. In-situ attenuated total reflection surface-enhanced infrared spectroscopy combined with density functional theory calculations revealed the high-performance biosensing mechanism, whereby the Ce centers in Ce1-CN nanoenzymes adsorb NO and subsequently react with OH- to form *HNO2. Results demonstrated that NO concentration was associated with TN onset. Following carbamazepine treatment, NO production from nerves decreased, accompanied by an alleviation of pain. These findings indicate that the biosensor serves as a valuable tool for investigating the pathogenesis of TN and guiding subsequent therapeutic strategies.

q-bio.BM↗

arXiv2025

Physiology-informed layered sensing for intelligent human-exoskeleton interaction

Wearable exoskeletons hold transformative promise for restoring mobility across diverse users with muscular weakness or other impairments. However, their translation beyond laboratory environments remains limited by sensing systems that capture movement but not underlying physiology. Here, we present a soft, lightweight smart leg sleeve that achieves anatomically aligned, layered multimodal sensing by integrating textile-based surface electromyography (sEMG) electrodes, ultrasensitive textile strain sensors, and inertial measurement units (IMUs). Each sensing modality targets a distinct physiological layer: IMUs track joint kinematics at the skeletal level, sEMG monitors muscle activation at the muscular level, and strain sensors detect skin deformation at the cutaneous level. Together, these sensors provide real-time perception to support three core objectives: controlling personalized assistance, optimizing user effort, and safeguarding against injury risks. The system is skin-conformal, mechanically compliant, and seamlessly integrated with a custom exoskeleton ($<20$~g total sensor and electronics weight). We demonstrate: (1) accurate ankle joint moment estimation (RMSE = 0.13~Nm/kg), (2) real-time classification of metabolic trends (accuracy = 97.1\%), and (3) injury risk detection within 100~ms (recall = 0.96), all validated on unseen users using a leave-one-subject-out protocol. This work establishes a physiology-aligned sensing architecture that reframes exoskeleton perception from motion tracking to real-time physiological decoding, offering a pathway towards intelligent, adaptive, and personalized wearable robotics.

eess.SY↗

arXiv2024

Wearable intelligent throat enables natural speech in stroke patients with dysarthria

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.

eess.AS↗

arXiv2024

A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life

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.

eess.SP↗

arXiv2023

Ultrasensitive Textile Strain Sensors Redefine Wearable Silent Speech Interfaces with High Machine Learning Efficiency

Our research presents a wearable Silent Speech Interface (SSI) technology that excels in device comfort, time-energy efficiency, and speech decoding accuracy for real-world use. We developed a biocompatible, durable textile choker with an embedded graphene-based strain sensor, capable of accurately detecting subtle throat movements. This sensor, surpassing other strain sensors in sensitivity by 420%, simplifies signal processing compared to traditional voice recognition methods. Our system uses a computationally efficient neural network, specifically a one-dimensional convolutional neural network with residual structures, to decode speech signals. This network is energy and time-efficient, reducing computational load by 90% while achieving 95.25% accuracy for a 20-word lexicon and swiftly adapting to new users and words with minimal samples. This innovation demonstrates a practical, sensitive, and precise wearable SSI suitable for daily communication applications.

eess.AS↗