Liquid-Metal-Based Multichannel Strain Sensor for Sign Language Gesture Classification Using Machine Learning

Liquid metals are highly conductive like metallic materials and have excellent deformability due to their liquid state, making them rather promising for flexible and stretchable wearable sensors. However, patterning liquid metals on soft substrates has been a challenge due to high surface tension. In this paper, a new method is proposed to overcome the difficulties in fabricating liquid-state strain sensors. The method involves adding nickel powder particles to the liquid metal to maintain the liquid metal's fluidity while lowering the surface tension so that the liquid metal can be easily patterned on a soft substrate using magnets. With the addition of 12 wt % nickel powder (40 μm) to the liquid metal, a gauge factor of 5.17 can be achieved at 300% strain. In addition, by the integration of multiple strain sensors in a smart glove to monitor 14 joints of the human hand, 10 sign language gestures can be recognized by comparing the results of five different machine learning models, among which the quadratic discriminative analysis model can be accomplished with an accuracy rate of 100%. The magnetically patterned nickel-containing liquid-metal strain sensors proposed in this study have a wide range of applications in intelligent soft robots and human-machine interfaces.