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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.
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1 Sungkyunkwan University (SKKU), Department of Electrical and Computer Engineering, Suwon, Republic of Korea (GRID:grid.264381.a) (ISNI:0000 0001 2181 989X)
2 Sungkyunkwan University (SKKU), School of Chemical Engineering, Suwon, Republic of Korea (GRID:grid.264381.a) (ISNI:0000 0001 2181 989X); Northwestern University, Querrey Simpson Institute for Bioelectronics, Evanston, USA (GRID:grid.16753.36) (ISNI:0000 0001 2299 3507)
3 Northwestern University, Querrey Simpson Institute for Bioelectronics, Evanston, USA (GRID:grid.16753.36) (ISNI:0000 0001 2299 3507)
4 Ajou University, Department of Mechanical Engineering, Suwon, Republic of Korea (GRID:grid.251916.8) (ISNI:0000 0004 0532 3933)
5 Korea University, School of Electrical Engineering, Seoul, Republic of Korea (GRID:grid.222754.4) (ISNI:0000 0001 0840 2678)
6 Sungkyunkwan University (SKKU), School of Chemical Engineering, Suwon, Republic of Korea (GRID:grid.264381.a) (ISNI:0000 0001 2181 989X); Sungkyunkwan University (SKKU), Biomedical Institute for Convergence (BICS), Suwon, Korea (GRID:grid.264381.a) (ISNI:0000 0001 2181 989X)
7 Konkuk University, Department of Organic and Nano System Engineering, Seoul, Republic of Korea (GRID:grid.258676.8) (ISNI:0000 0004 0532 8339)
8 Kyung Hee University, Department of Advanced Materials Engineering for Information and Electronics, Yongin, Republic of Korea (GRID:grid.289247.2) (ISNI:0000 0001 2171 7818)
9 Jeonbuk National University, School of Semiconductor and Chemical Engineering, Jeonju, Republic of Korea (GRID:grid.411545.0) (ISNI:0000 0004 0470 4320)