Liquid-Metal-Based Soft Pressure Sensor and Multidirectional Detection by Machine Learning

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.,Liquid-metal-based microchannels are integrated into the dome-shaped structure to create a multidirectional soft pressure sensor. The proposed multidirectional pressure sensor employs machine learning to identify both the direction and magnitude of multidirectional pressures. Real-time machine learning-based detection of the direction and magnitude of multidirectional pressures from the proposed sensor is utilized as a human-machine interface device. image,