Multimodal Electronic Skin Integrating Discrete Wavelet Transform and Deep Learning for Accurate Tactile Perception

ABSTRACT Electronic skin, as a key technology for enabling robots and wearable devices to perceive the external environment and achieve intelligent interaction, faces the core challenge of developing multimodal and highly sensitive comprehensive tactile sensing. In this work, we propose a biomimetic, multimodal, and flexible electronic skin that provides high‐sensitivity discrimination and reliable detection of both pressure and temperature signals. Through the synergistic regulation of the conductive hydrogel and the strain‐sensitive layer, the sensor exhibits excellent performance, with a pressure sensitivity of 1063 kPa − 1 , a detection limit of 1.16 Pa, and a response time of under 30 ms; the temperature sensing range spans 0°C–80°C, offering a broad‐spectrum detection capability. Signals are processed using a discrete wavelet transform (DWT) for multiscale feature extraction, and high‐dimensional feature fusion and classification are achieved by combining support vector machines (SVM) with deep neural networks (DNN), demonstrating the feasibility and potential of the system for distinguishing samples with different surface texture characteristics, with an average recognition accuracy of 93%. The system integrates multimodal detection capabilities for pressure, temperature, and material recognition, demonstrating its broad application potential in robotic tactile perception, wearable health monitoring, and human‐machine interaction.