Multi-Object Recognition and Motion Detection Based on Flexible Pressure Sensor Array and Deep Learning

With ongoing technological advancements, artificial tactile systems have become a prominent area of research, aiming to replicate human tactile capabilities and enabling machines and devices to interact with their environments. Achieving effective artificial tactile sensing relies on the integration of high-performance pressure sensors, precise signal acquisition, robust transmission, and rapid data processing. In this study, we developed a sensor array system based on flexible pressure sensors designed to recognize objects of varying shapes and sizes. The system comprises a multi-channel acquisition circuit and a signal transmission circuit and employs a convolutional neural network (CNN) to classify distinct signal patterns. In a test on an individual, the test results demonstrate that the system achieves a high recognition accuracy of 99.60% across two sphere sizes, three cylinder sizes, a cone, and a rectangular prism. In a group of eight people, it can achieve a recognition accuracy of 93.75%. Furthermore, we applied this sensor array system in an experimental setting involving a ball-throwing action, and it effectively recognized four distinct stages: empty hand, holding the ball, throwing, and catching. In repeated tests by other individuals, it was also able to clearly distinguish each stage. The development of artificial tactile systems allows robots to engage with their environments in a more nuanced and precise manner, enabling complex tasks such as surgical procedures, enhancing the interactive experience of wearable devices, and increasing immersion in virtual reality (VR) and augmented reality (AR). When integrated with deep learning, artificial tactile sensing shows significant potential for creating more intelligent and efficient applications.