Deep-learning-assisted printed liquid metal sensory system for wearable applications and boxing training

Abstract

Liquid metal (LM) exhibits a distinct combination of high electrical conductivity comparable to that of metals and exceptional deformability derived from its liquid state, thus it is considered a promising material for high-performance soft electronics. However, rapid patterning LM to achieve a sensory system with high sensitivity remains a challenge, mainly attributed to the poor rheological property and wettability. Here, we report a rheological modification strategy of LM and strain redistribution mechanics to simultaneously simplify the scalable manufacturing process and significantly enhance the sensitivity of LM sensors. By incorporating SiO2 particles into LM, the modulus, yield stress, and viscosity of the LM-SiO2 composite are drastically enhanced, enabling 3D printability on soft materials for stretchable electronics. The sensors based on printed LM-SiO2 composite show excellent mechanical flexibility, robustness, strain, and pressure sensing performances. Such sensors are integrated onto different locations of the human body for wearable applications. Furthermore, by integrating onto a tactile glove, the synergistic effect of strain and pressure sensing can decode the clenching posture and hitting strength in boxing training. When assisted by a deep-learning algorithm, this tactile glove can achieve recognition of the technical execution of boxing punches, such as jab, swing, uppercut, and combination punches, with 90.5% accuracy. This integrated multifunctional sensory system can find wide applications in smart sport-training, intelligent soft robotics, and human-machine interfaces.