In a camera-based engagement level recognition, a face is an important factor because cues mainly come from a face, which is affected from a distance between a camera and a user. In this paper, we present an automatic engagement level recognition method showing stable performance regardless of a distance between a camera and a user. We show a detailed process about getting a distance-invariant cue and compare its performance with and without the process. We also adopt a temporal pyramid structure to extract temporal statistical feature and present a voting method for an engagement level estimation. We show the results and the analysis using the database acquired in the real environment.
In this paper, we present an affect recognition system for measuring the engagement level of children using the Kinect while performing a multiple intelligence test on a computer. First of all, we recorded 12 children while solving the test and manually created a ground truth data for the engagement levels of each child. For a feature extraction, Kinect for Windows SDK provides support for a user segmentation and skeleton tracking so that we can get 3D joint positions of an upper-body skeleton of a child. After analyzing movement of children, the engagement level of children’s responses is classified into two classes: High or Low. We present the classification results using the proposed features and identify the significant features in measuring the engagement.
Recently, many studies show that an indoor horse riding exercise has a positive effect on promoting health and diet. However, if a rider has an incorrect posture, it will be the cause of back pain. In spite of this problem, there is only few research on analyzing rider’s posture. Therefore, the purpose of this study is to estimate a rider pose from a depth image using the Asus’s Xtion sensor in real time. In the experiments, we show the performance of our pose estimation algorithm in order to comparing the results between our joint estimation algorithm and ground truth data.