Deep learning-enabled real-time personal handwriting electronic skin with dynamic thermoregulating ability

The rapid rise of the Internet of things (IoT) have brought the progress of electronic skin (e-skin). E-skin is used to imitate or even surpass the functions of human skin. Thermoregulating is one of the crucial functions of human skin, it is significant to develop a universal way to realize e-skin thermoregulating. Here, inspired by the sweat gland structure in human skin, we report a simple method for achieving dynamic thermoregulating, attributing to the temperature of microencapsulated paraffin remains unchanged when phase change occurs. Combining with the principle of triboelectric nanogenerator, a deep learning model is employed to recognize the output signals of handwriting different letters on ME-skin, and the recognition accuracy reaches 98.13%. Finally, real-time recognition and display of handwritings are successfully implemented by the ME-skin, which provides a general solution for thermoregulating e-skin and application direction for e-skin in the field of IoT.

Shengxin Xiang, Jiafeng Tang, Lei Yang, Yanjie Guo, Zhibin Zhao, Weiqiang Zhang