Self-Adhesive, Anti-Freezing MXene-Based Hydrogel Strain Sensor for Motion Monitoring and Handwriting Recognition with Deep Learning

Abstract

Flexible strain sensors based on self-adhesive, high-tensile, super-sensitive conductive hydrogels have promising application in human–computer interaction and motion monitoring. Traditional strain sensors have difficulty in balancing mechanical strength, detection function, and sensitivity, which brings challenges to their practical applications. In this work, the double network hydrogel composed of polyacrylamide (PAM) and sodium alginate (SA) was prepared, and MXene and sucrose were used as conductive materials and network reinforcing materials, respectively. Sucrose can effectively enhance the mechanical performance of the hydrogels and improve the ability to withstand harsh conditions. The hydrogel strain sensor has excellent tensile properties (strain >2500%), high sensitivity with a gauge factor of 3.76 at 1400% strain, reliable repeatability, self-adhesion, and anti-freezing ability. Highly sensitive hydrogels can be assembled into motion detection sensors that can distinguish between various strong or subtle movements of the human body, such as joint flexion and throat vibration. In addition, the sensor can be applied in handwriting recognition of English letters by using the fully convolutional network (FCN) algorithm and achieved the high accuracy of 98.1% for handwriting recognition. The as-prepared hydrogel strain sensor has broad prospect in motion detection and human–machine interaction, which provides great potential application of flexible wearable devices.

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