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

AbstractAugmented 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

Development of a Typing Skill Learning Environment with Diagnosis and Advice on Fingering Errors

AbstractExisting application software for touch typing training cannot diagnose fingering errors. Given this fact, we developed a skill learning environment for touch typing training that can diagnose fingering errors by recognizing fingers with color markers using image recognition technique. This study developed two systems: a learning support environment for an experimental group and a learning environment for a control group. We evaluated the effect of the learning environment that can diagnose fingering errors for the experimental group, by comparison with the other learning environment for the control group

Classification of Haptic Tasks based on Electroencephalogram Frequency Analysis

AbstractIn recent years, it is difficult to inherit high level sensory skill, because the number of experts is not so much or the experts are too busy to teach their skill to the beginners. Therefore, many learners do the experiential learning through visual and haptic digital teaching materials. In such a system, however, it is difficult to evaluate whether the learner could recognize the sensation and obtain the sensory skill. In the paper, we investigate whether the biological signal such as EEG can be used for the evaluation of the haptic task skill level

Classification by EEG Frequency Distribution in Imagination of Directions

AbstractThis 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

Error Visualization for Pencil Drawing with Three-Dimensional Model

初心者のための鉛筆デッサンの基礎は，対象を見えたまま正確に描きとる写実であり，獲得が難しい技能である．初心者は自己の写実の誤りに気づくことができない，あるいはデッサン画全体に漠然とした違和感を感じることができても，具体的に写実の何が誤りかを判断できず，同じ誤りを繰り返す．反復練習を指導する教師は，写実の誤りを理解させるため，描く対象と異なった立体物をイメージさせるたとえ（比喩）を用いる．学習者は比喩説明を聞く過程で徐々に，明らかに異なるイメージの写実と感じるようになり，ついに自己の写実の誤りに気づくと考えられる．本論文は学習者の鉛筆デッサン画像に含まれる写実の誤りを顕在化した三次元モデルを構築する手法を検討する．本手法は誤った写実による1 枚の画像から，誤りを映し出し，かつ直感的に不自然さを感じさせる三次元モデルを構築する．写実の8 種の誤りを対象に，デッサン画像の誤りを特徴づける15個の特徴量を用いて三次元モデルのスケーリング変換を定義した．三次元モデルを表示する顕在化ツールを実装した．学習者が本ツールを用いることで，鉛筆デッサンに含まれる写実の誤りに気づきやすくなると期待できる．本研究の一部は文部科学省科学研究補助金基盤研究（B）（2）（課題番号：163000369）による