Infrared Gesture Recognition System Based on Near-Sensor Computing

Intelligent systems have brought convenience to contemporary society. However, latency and poor-efficiency have been urgent problems for intelligence systems. Here, an infrared (IR) intelligent system fusing a non-contact IR thermopile sensor array fabricated by microelectromechanical system technology and an artificial neural network (ANN) algorithm is proposed, with the characteristics of high-efficiency and low-latency. The system is based on a designed near-sensor computing architecture, which can realize computing directly on edge devices without sending data to the cloud, resulting in reduced redundant data and decreased latency. The responsivity of the sensors affects the weight accuracy and computing speed of the ANN algorithm and further influences the accuracy and efficiency of the system. Transfer of graphene oxide (GO) material to the IR thermopile sensors with a proposed location transfer method is suggested to enhance the responsivity. The responsivity of the IR thermopiles with GO is up to 705.1 V/W, which is enhanced by 85.9% compared to that without GO. The system is applied to gesture recognition to study practicality. The recognition accuracy of the system with GO is 100%. This work provides an effective idea for studying a high-accuracy IR intelligent sensing system.