Biomimetic Biomechanical Sensors for Intelligent Wearable Interfaces

This thesis presents the contributions to the field of biomimetic biomechanical sensing by developing nature-inspired wearable sensor technologies that emulate and extend the complex structure and functions of biological tissues such as human skin and fingers. Human skin operates as a dynamic biomechanical interface, encoding rich physiological and behavioural information through spatiotemporally distributed deformations such as throat vibrations, arterial pulses, respiratory rhythms, and tactile manipulation. Capturing these subtle signals with high fidelity can unlock transformative applications in silent communication, healthcare monitoring, and immersive human-machine interaction. However, current biomechanical sensors remain limited in sensitivity within small strain range, omnidirectional responsiveness, spatiotemporal selectivity, and computational efficiency. Conventional devices mainly measure uniaxial, single-modality strain without targeted amplification or noise suppression, preventing effective transduction of the complex, multiaxial signals inherent to natural human motions and vibrations. To address these limitations, this thesis introduces a unified framework that combines material innovation, structural biomimicry, and deep learning classification. First, ordered cracks nanomaterial-based strain sensors integrated with textile substrates are developed for ultrasensitive detection of throat vibrations, enabling efficient silent speech decoding with lightweight neural networks. Second, isotropic omnidirectional strain and shear sensors inspired by human fingerprints are designed to achieve hypersensitive 360° strain recognition and directional force detection, supporting tactile perception and healthcare applications. Third, a metamaterial-based biomimetic interface is proposed to capture and decode mechanodermal activity (MDA), a heterogeneous set of biomechanical cues distributed across the skin, thereby facilitating selective signal amplification, noise suppression, multimodal acquisition, and robust decoding. These sensor platforms are integrated with computationally efficient deep learning models to enable real-time, adaptive, and context-aware interaction between humans and machines. The thesis demonstrates how biomimetic biomechanical sensors can serve as the foundation for next-generation intelligent wearable interfaces, and establishes a new paradigm for wearable systems in various biomedical applications and human-machine interactions.

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2025

Author dc:creator

• Xu, Muzi

Advisor dc:contributor.advisor

• Occhipinti, Luigi

• Biomimetic
• Biomechanical Sensors
• Wearable Interfaces

Licence

• https://www.repository.cam.ac.uk/bitstreams/3947dc44-06ad-4128-8cf2-27376ed56b9c/download
• http://purl.org/NET/rdflicense/allrightsreserved

Language dc:language

eng

Author Identifier

0000-0001-6381-9863

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/395465