Liquid‐Metal‐Based Soft Pressure Sensor and Multidirectional Detection by Machine Learning
Osman Gul, Jeongnam Kim, Kyuyoung Kim, Hye Jin Kim, Inkyu Park
Abstract
Abstract Electronic skin (e‐skin) is an emerging technology with promising applications in various fields, including human–machine interfaces, prosthetics, and robotics. Soft and flexible sensors are vital components for the e‐skin that can mimic human skin's sensing capabilities. Among soft sensors, liquid‐metal‐based sensors have gained attention owing to their unique properties, such as high electrical conductivity, stretchability, and elasticity. Herein, a novel approach is presented that enables multidirectional pressure sensing with a machine‐learning approach from the transient response of the liquid‐metal‐based soft pressure sensor for the e‐skins. In this study, a soft sensor is developed that utilizes liquid metal and has an array of microchannels on a dome‐shaped structure to detect pressures from multiple directions. The transient response from six microchannels of the sensor is used as the input for a convolutional neural network (CNN) to predict the direction (classification accuracy of 99.1%) and magnitude (regression error of 20.13%) of the applied pressures in real time. Finally, a potential application of the developed liquid‐metal‐based soft sensor as a human–machine interface device is demonstrated by using it to control an RC model car through multidirectional predictions (pressure direction and magnitude) through machine learning in real time.