EGgaIn-Based Soft Stretchable Sensing System for Touch Localization, Strain Measurement, and CNN-Driven Gesture Recognition

Date of Award

5-1-2025

Degree Name

M.S. in Electrical Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Alexander M. Watson

Abstract

Wearable electronics depend heavily on robust and responsive sensors, yet conventional invasive methods and rigid structures have long limited their potential. While miniaturization has improved sensor integration, these systems often struggle under mechanical stress such as bending or stretching. Recent advances in soft, stretchable materials with high conductivity and self-healing capabilities offer a forward, promising path. Among these, Eutectic Gallium–Indium (EGaIn) stands out due to its high conductivity and minimal resistance change under strain. This thesis presents the design and development of a soft, stretchable sensing system that leverages EGaIn-based electronics for multimodal sensing. Capacitive sensing is used for touch localization, while resistive EGaIn traces enable strain measurement. Also, gesture recognition is implemented using a convolutional neural network (CNN), which classifies touch patterns captured from the sensor surface. The system demonstrates the potential of combining capacitive and resistive sensing to form a highly responsive, deformable interface. Through extensive testing—including uniaxial and biaxial stretch experiments—this work highlights the challenges of two-dimensional strain sensing and the importance of sensor layout in performance.

Keywords

Electrical Engineering, Engineering

Rights Statement

Copyright 2025, author.

Link to Full Text

Share

COinS