Tracheal intubation for patients with respiratory infectious diseases requires doctors to wear a full set of protective clothing, which takes a certain time. How to protect doctors from infection when facing an emergency operation has become an important issue. The intubation robot may solve this contradiction. To provide visual information for real-time path planning for robotic intubation, this study recovers depth information about the oral environment using the low-cost and widely used endoscopic. Since the oral cavity is small and has less texture, the Shape from Shading (SFS) method may be a good choice for oral depth estimation. This paper proposes the “oral elbow” hypothesis, filters outliers caused by saliva, calculates the 3-D contour map, and highlights the contour map features from different views. Oral images are obtained from a healthy person and a silicon dummy. This work expands the application scenarios of depth estimation to the oral environment; provides depth information for the visual navigation of the intubation surgical robot.

This paper proposes a new technique of modeling driver drowsiness with multiple eyelid movement features based on an information fusion technique - partial least squares regression (PLSR), with which to cope with the problem of strong collinear relations among eyelid movement features and, thus, predicting the tendency of the drowsiness. With a set of electro- oculogram signals measured in an experiment conducted in Sweden, 14 typical eyelid movement features are first extracted. Then, statistical analyses from 20 subjects indicate that the eyelid movement parameters can characterize a driver's degree of drowsiness. The intrinsic quantitative relationships between eyelid movement features and driver drowsiness degree are modeled by PLSR analysis. The developed model provides a framework for integrating multiple sleepiness features together and defining the contribution of each feature to the decision and prediction result. The predictive precision and robustness of the model thus established are validated, which show that it provides a novel way of fusing multifeatures together for enhancing our capability of detecting and predicting the state of drowsiness.