Robust hydrogel sensors for unsupervised learning enabled sign‐to‐verbal translation

Abstract

Abstract Highly stretchable and robust strain sensors are rapidly emerging as promising candidates for a diverse of wearable electronics. The main challenge for the practical application of wearable electronics is the energy consumption and device aging. Energy consumption mainly depends on the conductivity of the sensor, and it is a key factor in determining device aging. Here, we design a liquid metal (LM)‐embedded hydrogel as a sensing material to overcome the barrier of energy consumption and device aging of wearable electronics. The sensing material simultaneously exhibits high conductivity (up to 22 S m−1), low elastic modulus (23 kPa), and ultrahigh stretchability (1500%) with excellent robustness (consistent performance against 12 000 mechanical cycling). A motion monitoring system is composed of intrinsically soft LM‐embedded hydrogel as sensing material, a microcontroller, signal‐processing circuits, Bluetooth transceiver, and self‐organizing map developed software for the visualization of multi‐dimensional data. This system integrating multiple functions including signal conditioning, processing, and wireless transmission achieves monitor hand gesture as well as sign‐to‐verbal translation. This approach provides an ideal strategy for deaf‐mute communicating with normal people and broadens the application of wearable electronics.

Keywords

• hydrogel
• liquid metal
• sign‐to‐verbal translation
• strain sensor
• unsupervised learning