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.

对表皮电子传感器的持续研究旨在开发能够在人体皮肤中重现关键多模式反应的复杂平台，并能够感知各种外部刺激，如压力、剪切、扭转和触摸。此类应用程序的开发利用算法解释来分析复杂的刺激形状、大小和表皮的各种模量，需要附加传感器的多个复杂方程。在本实验中，我们将硅压敏电阻与定制的深度学习数据过程集成在一起，以促进对各种刺激的精确评估和评估，而无需如此复杂。凭借超越传统香草深度回归模型的能力，定制的回归和分类模型能够预测外力的大小、表皮硬度和物体形状，平均平均绝对误差百分比和准确度分别<15%和96.9%。深度学习辅助传感器的技术能力以及随之而来的准确数据处理为未来的感知电子系统提供了重要基础。