To help student nurses learn to transfer patients from a bed to a wheelchair, this paper proposes a system for automatic skill evaluation in nurses' training for this task. Multiple Kinect sensors were employed, in conjunction with colored markers attached to the trainee's and patient's clothing and to the wheelchair, in order to measure both participants' postures as they interacted closely during the transfer and to assess the correctness of the trainee's movements and use of equipment. The measurement method involved identifying body joints, and features of the wheelchair, via the colors of the attached markers and calculating their 3D positions by combining color and depth data from two sensors. We first developed an automatic segmentation method to convert a continuous recording of the patient transfer process into discrete steps, by extracting from the raw sensor data the defining features of the movements of both participants during each stage of the transfer. Next, a checklist of 20 evaluation items was defined in order to evaluate the trainee nurses' skills in performing the patient transfer. The items were divided into two types, and two corresponding methods were proposed for classifying trainee performance as correct or incorrect. One method was based on whether the participants' relevant body parts were positioned in a predefined spatial range that was considered ‘correct’ in terms of safety and efficacy (e.g., feet placed appropriately for balance). The second method was based on quantitative indexes and thresholds for parameters describing the participants' postures and movements, as determined by a Bayesian minimum-error method. A prototype system was constructed and experiments were performed to assess the proposed approach. The evaluation of nurses' patient transfer skills was performed successfully and automatically. The automatic evaluation results were compared with evaluation by human teachers and achieved an accuracy exceeding 80%.

In order to display complicated virtual spaces in real time, such as spaces consisting of a dynamic natural scenery, we earlier proposed a method for simplifying the shape data of 3-D trees whereby the amount of shape data is efficiently reduced. The method generates tree shapes based on a fractal model according to the required level of details (LOD). By using a texture-mapping technique, we experimentally showed that our method can display 3-D tree images with allowable image quality in real time. However, methods for controlling the LOD of 3-D tree shapes in virtual spaces have yet to be discussed. In this paper, quantitative evaluations were made on the effect of a data simplification method employing such visual properties as resolution difference between the central vision and peripheral vision. Results showed that it is possible to display a complicated scene containing many trees in real time by controlling the LOD of tree shapes in the virtual space considering such visual properties. Furthermore, so that reality can be added to the virtual space, we consider that it is important to display the natural sways of wind-blown trees and plants in real time. Therefore, we propose a method for generating sway data for simplified tree shape data based on a simple physical model, in which each branch is connected to several other branches by springs, and also a new texture-mapping technique for rendering simplified tree shapes, making it appear as if the shapes have a high LOD. Finally, we show some examples of images of trees generated in real time by using our method, in which many trees exist and sway due to wind.