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

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 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

Fluid Data Compression and ROI Detection Using Run Length Method

AbstractIt is difficult to carry out visualization of the large-scale time-varying data directly, even with the supercomputers. Data compression and ROI (Region of Interest) detection are often used to improve efficiency of the visualization of numerical data. It is well known that the Run Length encoding is a good technique to compress the data where the same sequence appeared repeatedly, such as an image with little change, or a set of smooth fluid data. Another advantage of Run Length encoding is that it can be applied to every dimension of data separately. Therefore, the Run Length method can be implemented easily as a parallel processing algorithm. We proposed two different Run Length based methods. When using the Run Length method to compress a data set, its size may increase after the compression if the data does not contain many repeated parts. We only apply the compression for the case that the data can be compressed effectively. By checking the compression ratio, we can detect ROI. The effectiveness and efficiency of the proposed methods are demonstrated through comparing with several existing compression methods using different sets of fluid data

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

Effective Life and Area Based Data Storing and Deployment in Vehicular Ad-Hoc Networks

In vehicular ad-hoc networks (VANETs), store-carry-forward approach may be used for data sharing, where moving vehicles carry and exchange data when they go by each other. In this ap-proach, storage resource in a vehicle is generally limited. Therefore, attributes of data that have to be stored in vehicles are an important factor in order to efficiently distribute desired data. In VA-NETs, there are different types of data which depend on the time and location. Such kind of data cannot be deployed adequately to the requesting vehicles only by popularity-based rule. In this paper, we propose a data distribution method that takes into account the effective life and area in addition to popularity of data. Our extensive simulation results demonstrate drastic improve-ments on acquisition performance of the time and area specific data

Analysis of Cerebral Blood Flow in Imagination of Moving Object

AbstractWe proposed operating HMD by BMI to speed up operation and to enhance the convenience in wearable device equipping HMD. But it is difficult to reflect user's thought direct on operation at this stage. In order to realize it, we need to identify a kind of human's brain signal when human thinks about moving object. So our study verifies whether it is possible to identify by imaging direction in data that we investigate cerebral blood flow changes in using NIRS when subject thinks about moving object. Identification was using neural network. The average of identification ratio of 4 direction in Total-Hb, the average of identification ratio of 4 direction in Oxy-Hb, the average of identification ratio of 4 direction in Deoxy-Hb are 32.25%, 36.0% and 37.0%, respectively. The average of identification ratio of 2 direction, up and down, in Total-Hb, the average of identification ratio of 2 direction, up and down, in Oxy-Hb, the average of identification ratio of 2 direction, up and down, in Deoxy-Hb are 63.5%, 62.0% and 62.5%, respectively. The average of identification ratio of 2 direction, left and right, in Total-Hb, the average of identification ratio of 2 direction, left and right, in Oxy-Hb, the average of identification ratio of 2 direction, left and right, in Deoxy-Hb are 58.0%, 55.5% and 69.0%, respectively. From the results, we show the possibility of identification because identification ratio are higher than chance level