Epidermal piezoresistive structure with deep learning-assisted data translation
Changrok So, Jong Uk Kim, Haiwen Luan, Sang Uk Park, Hyochan Kim, Seungyong Han, Doyoung Kim, Changhwan Shin, Tae‐il Kim, Wi Hyoung Lee, Yoonseok Park, Keun Heo, Hyoung Won Baac, Jong Hwan Ko, Sang Min Won
Abstract
Abstract Continued research on the epidermal electronic sensor aims to develop sophisticated platforms that reproduce key multimodal responses in human skin, with the ability to sense various external stimuli, such as pressure, shear, torsion, and touch. The development of such applications utilizes algorithmic interpretations to analyze the complex stimulus shape, magnitude, and various moduli of the epidermis, requiring multiple complex equations for the attached sensor. In this experiment, we integrate silicon piezoresistors with a customized deep learning data process to facilitate in the precise evaluation and assessment of various stimuli without the need for such complexities. With the ability to surpass conventional vanilla deep regression models, the customized regression and classification model is capable of predicting the magnitude of the external force, epidermal hardness and object shape with an average mean absolute percentage error and accuracy of <15 and 96.9%, respectively. The technical ability of the deep learning-aided sensor and the consequent accurate data process provide important foundations for the future sensory electronic system.