Deep learning-enabled self-powered bimodal flexible sensor for intelligent access control

In the era of digital transformation characterized by the deep integration of artificial intelligence and the Internet of Things, human-machine interaction systems have become ubiquitous in smart architecture and urban security. As the primary security interface, intelligent access control systems face unprecedented challenges. Traditional biometric technologies, such as facial recognition and fingerprint scanning, not only rely heavily on external power sources but also encounter critical limitations regarding privacy risks and environmental sensitivity. To address these issues, this study develops a self-powered bimodal sensor based on a single-electrode triboelectric nanogenerator, providing a low-power, high-security, and multidimensional sensing solution. The core of the sensor lies in its sophisticated functional structural design, featuring a polydimethylsiloxane triboelectric layer patterned with a micro-pyramid array, integrated with a copper foil electrode and a polyethylene terephthalate substrate. The device leverages the coupled effects of contact electrification and electrostatic induction to convert mechanical stimuli into characteristic electrical signals rich in material electronegativity and human kinetic information. To extract latent features from these complex waveforms, a deep learning framework based on a convolutional neural network is implemented to analyze and decouple the signals. Experimental results demonstrate that the system exhibits superior recognition performance in complex environments: in single-dimensional tasks, the accuracies for material identification and user authentication reach 99.83% and 98.88%, respectively. Even under a challenging scenario, the system maintains a high recognition accuracy of 96.15%. This work provides a robust technological foundation for future smart security, flexible electronic skins, and personalized healthcare monitoring.