Masato Soga

An educational tool for basic techniques in beginner's pencil drawing

A picture is one of important research subjects in order to make our life spiritually rich. Most studies on pictures, however, only propose some substitute functions of actual drawing or painting materials. There is no system that evaluates pictures drawn by users and gives advice about them. We propose a learning support system for basic techniques in beginners pencil drawing. The proposed system receives a subject (motif) data set and an image of users sketch and returns advice to the user. The system is composed of the four subsystems: feature extraction of motifs, feature extraction of sketches, error identification, and generation and presentation of advice. We developed and experimented a prototype system limited to treat a basic motif and some principal advice. As a result, the validity of the proposed system was confirmed.

A learning support system for beginners in pencil drawing

A picture is one of important research subjects to make our life spiritually rich. Most studies on pictures, however, only propose some substitute functions of actual drawing or painting materials. There is no system that evaluates pictures drawn by users and gives advice about them. We propose a learning support system of beginners pencil drawing that is the basis of pictures. Our system receives a motif data set and a users sketch image, and returns advice to the user. The processing is composed of the four functions: feature extraction of motifs, feature extraction of sketches, error identification, and generation and presentation of advice. We developed and experimented a prototype system limited to treat a basic motif and principal advice. As a result, the validity of the proposed system was confirmed.

Proposal and development of motion navigator enabling learners to observe expert's motion from expert's viewpoint by augmented reality

We propose a new method to support skill learning for sports or arts, and developed a prototype system. The system simulates experts motion from experts viewpoint with CG animation on learners head mount display (HMD) by using augmented reality. Learners can trace experts motion by using the system. We applied the system to skill learning support for Kyudo (Japanese art of archery), and we evaluated the proposed method.

Development and Evaluation of a System for AR Enabling Realistic Display of Gripping Motions Using Leap Motion Controller

Abstract Augmented Reality (AR) in the traditional systems have a problem that the drawn objects are always displayed in the foreground because 3D models by AR are superimposed later than the picture of the actual world. This paper proposed a system to produce a realistic picture of AR in accordance with every depth. We developed a prototype system to verify the effect of the method. The prototype system was developed by focusing on a human hand. This paper utilized a Leap Motion Controller as a motion capture device to acquire the depth data of the hand and fingers.

Classification by EEG Frequency Distribution in Imagination of Directions

Abstract This paper describes the method for classification of brain state by the measured electroencephalogram (EEG) frequency in directions (up, down, left, and right) imagination. Recently, Brain-Machine Interface (BMI) has been studied in a variety of ways due to the development of brain measurement technology. Therefore, we have used the BMI to identify the human selection of directions. Our method consists of data normalization, principal component analysis and neural network. The maximum value of the identification rate was 46% by using 3 electrodes (F4, F8 and T8) in the previous study. In this study, we improved the learning method of neural network for the improvement of identification rate of brain state. For that purpose, the measurement points of EEG and the number of subjects are increased. As a result, the maximum value of the identification rate was improved.

Development of Finger Motion Skill Learning Support System based on Data Gloves

Abstract Recently, more and more hearing person starts learning sign language. For finger motion skill learning such as sign language, it is important for an expert to give objective advice to a learner, because the learner is not able to find any motion errors in his/her sign language. In other words the learner needs the self-education tool of sign language which gives good objective advice. However, there are few studies about such a system. In this paper, we developed a finger motion skill learning support system using data gloves. This system helps a learner to recognize motion errors intuitively by himself/herself by overlaying skilled persons 3D models with learners 3D models of hands and fingers.

Interactive Learning Environment for Astronomy with Finger Pointing and Augmented Reality

We developed star learning environment with magnetic sensors. This environment has three functions. First function is star name teller. When a learner point out a star with the sensor on his/her finger top, the system tells him/her name of the star and constellation with voice. Then, second function is star navigator. If the learner know the name of a star, but he/she does not know where it is in the real sky, the system shows the path from current pointing position to the target star. Third function is constellation tutor. We developed guidance and diagnosis function of constellationspsila shapes with Wii remote controller.

The Learning Environment for Stars and Constellations in the Real World with Finger Pointing

We developed learning environment for stars and constellations with finger pointing under real night sky.This environment has some functions. First function is star name telling function. When a learner points a finger at a star with the sensor on his/her finger top, the system tells him/her name of the star and constellation with voice. Then, second function is star navigation function. If the learner know the name of a star, but he/she does not know where it is in the real sky, the system shows the path from current pointing position tithe target star. These two basic functions can be used by single learner, but also used collaborative learners. Third function is constellation tutoring function. The system asks questions about stars and constellations to learners. A learner answers the question by pointing at real star, and then the system corrects it. This function can be used for collaborative learners. Fourth function is collaborative focus point indication. Every learner uses each learning environment for his/her own, and every learning environment communicates each other. Every environment indicates the other learnerpsilas pointed star. Learners can know easily where the other learner points a finger at in the sky, and collaborate learning easily.

Proposal and development of reversal motion skill leaning support environment

It is not easy to master reversal motion by himself / herself, because reversal motion uses different muscles that were not used in the dominant motion, and the reversal motion tend to be awkward. Dominant motion means the motion that a learner can play sports naturally with. In this background, we propose reversal motion skill self-learning environment that shows advice based on learners dominant motion. In this study, we supposed that learners can play with dominant motions almost perfectly but can play poorly with reversal motions. The learners try to improve their reversal motion by referencing reversed dominant motion. In this process, when a learner input his/her reversal motion data, then the self-learning environment shows difference between the reversed dominant motion and reversal motion. The environment also tells how to improve the reversal motion.

Development of Motion Navigator II Enabling Integrated Motion Display of Whole Body with Fingers

Abstract In this study, we build a new system “Motion Navigator II”, which displays finger motion integrated with whole body motion with bone CG animation by AR. The function to display whole body motion is inherited from an existing motion skill learning support system “Motion Navigator”. The existing Motion Navigator can’t display fingers’ motion but shows limited variation of motion in precedent research. Therefore we acquired finger motion data using data gloves and improved Motion Navigator integrating the finger motion data with whole body motion data. We verified learning effect of the experimental group using Motion Navigator II in comparison with the control group using conventional video.