With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user’s intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system’s real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system’s ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses’ algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management.

A new filter named the maximum likelihood-based iterated divided difference filter (MLIDDF) is developed to improve the low state estimation accuracy of nonlinear state estimation due to large initial estimation errors and nonlinearity of measurement equations. The MLIDDF algorithm is derivative-free and implemented only by calculating the functional evaluations. The MLIDDF algorithm involves the use of the iteration measurement update and the current measurement, and the iteration termination criterion based on maximum likelihood is introduced in the measurement update step, so the MLIDDF is guaranteed to produce a sequence estimate that moves up the maximum likelihood surface. In a simulation, its performance is compared against that of the unscented Kalman filter (UKF), divided difference filter (DDF), iterated unscented Kalman filter (IUKF) and iterated divided difference filter (IDDF) both using a traditional iteration strategy. Simulation results demonstrate that the accumulated mean-square root error for the MLIDDF algorithm in position is reduced by 63% compared to that of UKF and DDF algorithms, and by 7% compared to that of IUKF and IDDF algorithms. The new algorithm thus has better state estimation accuracy and a fast convergence rate.

Pilots’ loss of situational awareness is one of the human factors affecting aviation safety. Numerous studies have shown that pilot perception errors are one of the main reasons for a lack of situational awareness without a proper system to detect these errors. The main objective of this study is to examine the changes in pilots’ eye movements during various flight tasks from the perspective of visual awareness. The pilot’s gaze rule scanning strategy is mined through cSPADE, while a hidden semi-Markov model-based model is used to detect the pilot’s visuoperceptual state, linking the correlation between the hidden state and time. The performance of the proposed algorithm is then compared with that of the hidden Markov model (HMM), and the more flexible hidden semi-Markov model (HSMM) is shown to have an accuracy of 93.55%.

Improving the efficiency of human-computer interaction is one of the critical goals of intelligent aircraft cockpit research. The gaze interaction control method can vastly reduce the manual operation of operators and improve the intellectual level of human-computer interaction. Eye-tracking is the basis of sight interaction, so the performance of eye-tracking will directly affect the outcome of gaze interaction. This paper presents an eye-tracking method suitable for human-computer interaction in an aircraft cockpit, which can now estimate the gaze position of operators on multiple screens based on face images. We use a multi-camera system to capture facial images, so that operators are not limited by the angle of head rotation. To improve the accuracy of gaze estimation, we have constructed a hybrid network. One branch uses the transformer framework to extract the global features of the face images; the other branch uses a convolutional neural network structure to extract the local features of the face images. Finally, the extracted features of the two branches are fused for eye-tracking. The experimental results show that the proposed method not only solves the problem of limited head movement for operators but also improves the accuracy of gaze estimation. In addition, our method has a capture rate of more than 80% for targets of different sizes, which is better than the other compared models.