Xiaohua Wu (吴小华)

Xiaohua Wu (吴小华)

passionate about tactile sensing, advanced manufacturing, and interdisciplinary innovation.

Talks and presentations

Poster: Deep Learning-Assisted Fingerprint-Inspired Flexible Pressure Sensor for Tension Monitoring in Carbon Fiber Production

September 01, 2025

Poster, The 9th Symposium of Flexible & Stretchable Electronics (ISFSE), 2025, Shenzhen, CN

We present a novel fingerprint-inspired flexible pressure sensor for real-time tension monitoring in wide carbon fiber bundle arrays. Carbon fiber production relies on continuous pre-oxidation lines, where thermal contraction induces tension fluctuations that can compromise fiber quality. Conventional tension sensors are bulky and difficult to integrate, making multi-bundle monitoring challenging.

Poster: High-Precision Lower-Limb Motion Capture System Based on Flexible Pressure Sensor Array Insoles

August 01, 2025

Poster, Chinese Society for Optical Engineering, Taiyuan, CN

We present an intelligent insole system based on a flexible textile pressure sensor array for lower-limb motion capture. The system features an exceptional detection range of up to 3770.9 kPa and an ultra-long operational lifespan exceeding 4 million cycles. It achieves human lower-limb joint position prediction with a precision of 3.6 cm, providing a scene-independent, non-intrusive, and motion-unbiased data terminal. This technology serves as a key foundational component for the next-generation human-machine perception fusion systems.

Skin-Inspired, Ultra-Long-Lifetime Fully Textile Flexible Pressure Sensors for Complex Recognition Applications

April 01, 2024

Talk, PhD Academic Salon on Microsystems and Nanoengineering, online

In this talk, we present a skin-inspired all-textile flexible pressure sensor with a signal transmission layer, addressing the challenges of manufacturing complexity and device longevity in conventional sensor arrays. Our design enables lossless acquisition of localized pressure signals using a single sensor, demonstrating the feasibility of performing complex tasks such as handwritten digit input detection. Key advantages of our approach include: