3D animation is created using keyframe based system in 3D animation software such as Blender and Maya. Due to the long time interval and the need of high expertise in 3D animation, motion capture devices were used as an alternative and Microsoft Kinect v2 sensor is one of them. This research analyses the capabilities of the Kinect sensor in producing 3D human model animations using motion capture and keyframe based animation system in reference to a live motion performance. The quality, time interval and cost of both animation results were compared. The experimental result shows that motion capture system with Kinect sensor consumed less time (only 2.6%) and cost (30%) in the long run (10 minutes of animation) compare to keyframe-based system, but it produced lower quality animation. This was due to the lack of body detection accuracy when there is obstruction. Moreover, the sensor's constant assumption that the performer's body faces forward made it unreliable to be used for a wide variety of movements. Furthermore, standard test defined in this research covers most body parts' movements to evaluate other motion capture system.
Quality of learning in the classroom is influenced by many factors. One of them is the academic emotions of the students. The emotion detection in the classroom cannot be done by using sensors attached to the body of the students, because it would disturb the concentration of the students. The proposed solution is by using unobtrusive emotion detection, e.g. by placing video capture equipment, which is not visible at the front of the student's desk. In this study, an RGB - Depth Microsoft Kinect camera is used to record facial expressions by considering the convenience factor of the students, speed of response time, and cost efficiency. A combination of Cohn-Kanade dataset and EURECOM dataset is used as the training set in machine learning with Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithm, with 8 sample of Asian race students (4 male and 4 female students).
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