Country：Japan

Country：Japan

Country：Japan

Language：English   Publisher：The Institute of Electronics, Information and Communication Engineers  

<p>This paper focuses on the potential value and future prospects of using virtual reality (VR) technology in online education. In detailing online education and the latest VR technology, we focus on metaverse construction and artificial intelligence (AI) for educational VR use. In particular, we describe a virtual university campus in which on-demand VR lectures are conducted in virtual lecture halls, automated evaluations of student learning and training using machine learning, and the linking of multiple digital campuses.</p>

DOI： 10.1587/transinf.2022eti0001

Web of Science

Scopus

CiNii Research

Language：English   Publisher：Frontiers in Human Neuroscience  

Aphasia is a language disorder that occurs after a stroke and impairs listening, speaking, reading, writing, and calculation skills. Patients with post-stroke aphasia in Japan are increasing due to population aging and the advancement of medical treatment. Opportunities for adequate speech therapy in chronic stroke are limited due to time constraints. Recent studies have reported that intensive speech therapy for a short period of time or continuous speech therapy using high-tech equipment, including speech applications (apps, can improve aphasia even in the chronic stage. However, its underlying mechanism for improving language function and its effect on other cognitive functions remains unclear. In the present study, we investigated whether intensive speech therapy using a newly developed speech support app could improve aphasia and other cognitive functions in patients with chronic stroke. Furthermore, we examined whether it can alter the brain network related to language and other cortical areas. Thus, we conducted a prospective, single-comparison study to examine the effects of a new speech support app on language and cognitive functions and used resting state functional MRI (rs-fMRI) regions of interest (ROI) to ROI analysis to determine changes in the related brain network. Two patients with chronic stroke participated in this study. They used the independent speech therapy system to perform eight sets of 20 randomly presented words/time (taking approximately 20 min), for 8 consecutive weeks. Their language, higher cognitive functions including attention function, and rs-fMRI, were evaluated before and after the rehabilitation intervention using the speech support app. Both patients had improved pronunciation, daily conversational situations, and attention. The rs-fMRI analysis showed increased functional connectivity of brain regions associated with language and attention related areas. Our results show that intensive speech therapy using this speech support app can improve language and attention functions even in the chronic stage of stroke, and may be a useful tool for patients with aphasia. In the future, we will conduct longitudinal studies with larger numbers of patients, which we hope will continue the trends seen in the current study, and provide even stronger evidence for the usefulness of this new speech support app.

DOI： 10.3389/fnhum.2022.870733

Web of Science

Scopus

PubMed

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE  

Students will experience a complex mixture of mental states during discussion, including concentration, confusion, frustration, and boredom, which have been widely acknowledged as crucial components for revealing a student's learning states. In this study, we propose using multimodal data to design an intelligent monitoring agent that can assist teachers in effectively monitoring the multiple mental states of students during discussion. We firstly developed an advanced multi-sensor-based system and applied it in a real university's research lab to collect a multimodal “in-the-wild” teacher-student conversation dataset. Then, we derived a set of proxy features from facial, heart rate, and acoustic modalities and used them to train several supervised learning classifiers with different multimodal fusion approaches single-channellevel, feature-level, and decision-level fusion to recognize students' multiple mental states in conversations. We explored how to design multimodal analytics to augment the ability to recognize different mental states and found that fusing heart rate and acoustic modalities yields better recognize the states of concentration (AUC = 0.842) and confusion (AUC = 0.695), while fusing three modalities yield the best performance in recognizing the states of frustration (AUC = 0.737) and boredom (AUC = 0.810). Our results also explored the possibility of leveraging the advantages of the replacement capabilities between different modalities to provide human teachers with solutions for addressing the challenges with monitoring students in different real-world education environments.

DOI： 10.1109/ACCESS.2021.3054176

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

In recent years, the use of automatic speech recognition (ASR) systems in meetings has been increasing, such as for minutes generation and speaker diarization. The problem is that ASR systems often misrecognize words because there is domain-specific content in meetings. In this paper, we propose a novel method for automatically post-editing ASR results by using presentation slides that meeting participants use and utterances adjacent to a target utterance. We focus on automatic post-editing rather than domain adaptation because of the ease of incorporating external information, and the method can be used for arbitrary speech recognition engines. In experiments, we found that our method can significantly improve the recognition accuracy of domain-specific words (proper nouns). We also found an improvement in the word error rate (WER).

DOI： 10.1007/978-3-030-83527-9_28

Scopus

Language：Japanese   Publisher：Communications in Computer and Information Science  

Learning activities, especially face-to-face conversational coaching may invite students experience a set of learning-centered mental states including concentration, confusion, frustration, and boredom, those mental sates have been widely used as vital proxies for inferring their learning processes and are closely linked with learning outcomes. Recognizing students’ learning-centered mental states, particularly effectively detecting negative mental states such as confusion and frustration in teacher-student conversation could help teacher effectively monitor students’ learning situations in order to direct personalized and adaptive coaching resources to maximum students’ learning outcome. Most of research focused on analyzing students’ mental states using univariate modality when they completing pre-designed tasks in a computer related environment. It is still an open question on how to effectively measure students’ multiple mental states when they interacting with human teacher in coach-led conversations from various aspects. To achieve this goal, in this work, we developed an advanced multi-sensor-based system to record multi-modal conversational data of student-teacher conversations generated in a real university lab. We then attempt to derive a series of interpretable features from multiple perspectives including facial and physiological (heart rate) to characterize students’ multiple mental states. A set of supervised classifiers were built based on those features with different modality fusion methods to recognize multiple mental states of students. Our results have provided the experimental evidence to validate the outstanding predictive ability of our proposed features and the possibility of using multimodal data to recognize students’ multiple mental states in ‘in-the-wild’ student-teacher conversation.

DOI： 10.1007/978-3-030-86439-2_24

Web of Science

Scopus

CiNii Research

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Fuji Technology Press Ltd.  

To solve the problem of autonomously navigating multiple destinations, which is one of the tasks in the Tsukuba Challenge 2019, this paper proposes a method for automatically generating the optimal travel route based on costs associated with routes. In the proposed method, the route information is generated by playing back the acquired driving data to perform self-localization, and the self-localization log is stored. In addition, the image group of road surfaces is acquired fromthe driving data. The costs of routes are generated based on texture analysis of the road surface image group and analysis of the self-localization log. The cost-added route information is generated by combining the costs calculated by the two methods, and by assigning the combined costs to the route. The minimum-cost multidestination route is generated by conducting a route search using cost-added route information. Then, we evaluated the proposed method by comparing it with the method of generating the route using only the distance cost. The results confirmed that the proposed method generates travel routes that account for safety when the autonomous wheelchair is being driven.

DOI： 10.20965/jrm.2020.p1121

Language：English   Publishing type：Research paper (scientific journal)  

The aim of this paper is to digitize the environments in which humans live, at low cost, and reconstruct highly accurate three-dimensional environments that are based on those in the real world. This three-dimensional content can be used such as for virtual reality environments and three-dimensional maps for automatic driving systems.
In general, however, a three-dimensional environment must be carefully reconstructed by manually moving the sensors used to first scan the real environment on which the three-dimensional one is based. This is done so that every corner of an entire area can be measured, but time and costs increase as the area expands. Therefore, a system that creates three-dimensional content that is based on real-world large-scale buildings at low cost is proposed. This involves automatically scanning the indoors with a mobile robot that uses low-cost sensors and generating 3D point clouds.
When the robot reaches an appropriate measurement position, it collects the three-dimensional data of shapes observable from that position by using a 3D sensor and 360-degree panoramic camera. The problem of determining an appropriate measurement position is called the “next best view problem," and it is difficult to solve in a complicated indoor environment. To deal with this problem, a deep reinforcement learning method is employed. It combines reinforcement learning, with which an autonomous agent learns strategies for selecting behavior, and deep learning done using a neural network. As a result, 3D point cloud data can be generated with better quality than the conventional rule-based approach.

DOI： 10.15377/2409-9694.2019.06.2

Language：English   Publishing type：Research paper (scientific journal)  

Whether a discussion is executed effectively depends on the completion level of the question-and-answer segments (Q&A segments) generated during the discussion. Relevance of answers could be used as a clue for evaluating the Q&A segments' completion degree. In this study, we argue that discussion participants' heart rate (HR) and its variability (HRV), which have recently received increased attention for being a crucial indicator in cognitive task performance evaluation, can be used to predict participants' answer-relevance in Q&A segments of discussions. To validate our argument, we propose an intelligent system that acquires and visualizes the HR data with the help of a non-invasive device, e.g. an Apple Watch, for measuring and recording the HR data of participants which is being updated in real-time. We also developed a web-based human-scoring method for evaluating answer-relevance of Q&A segments and question-difficulty level. A total of 17 real lab-seminar-style discussion experiments were conducted, during which the Q&A segments and the HR of participants were recorded using our proposed system. We then experimented with three machine-learning classifiers, i.e. logistic regression, support vector machine, and random forest, to predict answer-relevance of Q&A segments using the extracted HR and HRV features. Area Under the ROC Curve (AUC) was used to evaluate classifier accuracy using leave-one-student-out cross validation. We achieved an AUC= 0.76 for logistic regression classifier, an AUC=0.77 for SVM classifier, and an AUC=0.79 for random forest classifier. We examined possibilities of using participants' HR data to predict their answer-statements' relevance in Q&A segments of discussions, which provides evidence of the potential utility of the presented tools in scaling-up analysis of this type to a large number of subjects and in implementing these tools to evaluate and improve discussion outcomes in higher education environment.

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

A method is presented that extends the real world into all buildings. This building-scale virtual reality (VR) method differs from augmented reality (AR) in that it uses automatically generated 3D point cloud maps of building interiors. It treats an entire indoor area a pose tracking area by using data collected using an RGB-D camera mounted on a VR headset and using deep learning to build a model from the data. It modifies the VR space in accordance with its intended usage by using segmentation and replacement of the 3D point clouds. This is difficult to do with AR but is essential if VR is to be used for actual real-world applications, such as disaster simulation including simulation of fires and flooding in buildings. 3D pose tracking in the building-scale VR is more accurate than conventional RGB-D simultaneous localization and mapping.

DOI： 10.4018/IJMDEM.2019010101

Language：Japanese   Publishing type：Research paper (scientific journal)  

われわれは今やさまざまな電子音に囲まれて生活しており、それらを検出・認識し情報処理を行うことはMaker文化の発展デジタルゲーム拡張およびゲーミフィケーションの構成のうえで有意義であるが、エンドユーザプログラマが個々の検出・認識対象について教師付き学習により認識器を構築するのは現状ではまだ容易ではない。そこで再生ごとの音響的変動が小さいという電子音の特徴を活かし、Dynamic Time Warpingなどの伝統的なテンプレートベースのパターンマッチングアルゴリズムにより電子音の検出・認識を行うJavaScriptライブラリを実装する。基礎的な性能評価を行うとともに、さまざまなユースケースを例示することでその有用性を示す。

Language：English   Publishing type：Research paper (scientific journal)  

There must be as many concrete indicators as possible in education, which will become signposts. People will not be confident about their learning and will become confused with tenuous instruction. It is necessary to clarify what they can do and what kinds of abilities they can improve. This paper describes a case of evidence-based education that acquires educational data from students' study activities and not only uses the data to enable instructors to check the students' levels of understanding but also improve their levels of performance. Our previous research called discussion mining was specifically used to collect various data on meetings (statements and their relationships, presentation materials such as slides, audio and video, and participants' evaluations of statements). This paper focuses on student presentations and discussions in laboratory seminars that are closely related to their research activities in writing their theses. We propose a system that supports tasks to be achieved in research activities and a machine-learning method to make the system sustainable for long-term operation by automatically extracting essential tasks. We conducted participant-based experiments that involved students and computer-simulation-based experiments to evaluate how efficiently our proposed machine-learning method updated the task extraction model. We confirmed from the participant-based experiments that informing responsible students of tasks that were automatically extracted on the system we developed improved their awareness of the tasks. Here, we also explain improvements in extraction accuracy and reductions in labeling costs with our method and how we confirmed its effectiveness through computer simulations.

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

Our university is currently developing an advanced physical-digital learning environment that can train students to enhance their discussion and presentation skills. The environment guarantees an efficient discussion among users with state-of-the-art technologies such as touch panel discussion tables, digital posters, and an interactive wall-sized whiteboard. It includes a data mining system that efficiently records, summarizes, and annotates discussions held inside our facility. We also developed a digital poster authoring tool, a novel tool for creating interactive digital posters displayed using our digital poster presentation system. Evaluation results show the efficiency of using our facilities: the data mining system and the digital poster authoring tool. In addition, our physical-digital learning environment will be further enhanced with a vision system that will detect interactions with the digital poster presentation system and the different discussion tools enabling a more automated skill evaluation and discussion mining.

DOI： doi:10.1186/s40561-015-0011-1

Language：Japanese   Publishing type：Research paper (scientific journal)  

Reading as many technical documents as possible is important to improve our research. When we read these documents, we can understand their contents more easily by referring to related resources such as images, audio clips, and videos. Videos contain a variety of helpful information, and facilitate our understanding of technical documents. We propose a method to define video scenes and document elements, and to annotate them with additional information such as relationships. Based on these annotations and relationships, we developed a support system that uses videos and helps readers understand technical documents. We performed some experiments to confirm whether the system was usable.

DOI： 10.3169/itej.66.J461

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

In order to facilitate more efficient meetings held continuously, like project meetings, it is important to consider about past discussions and recall them in the current discussion. In this paper, we describe about a novel meeting support system consists with digital whiteboards and tablet devices which designed to be work cooperatively. With using the system, meeting results are saved as board contents which is usable to realize advanced application such as topic search or generating multi-meeting summary in content cloud server. Users are able to download contents from content cloud server and view them whenever they want with tablet device. Moreover, users are able to quote a part of past board content to the board which is using in a current meetings.

Authorship：Lead author   Language：Japanese  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：English  

Language：English   Publishing type：Research paper (scientific journal)  

We describe a mechanism for acquiring the semantics of video contents from the activities of Web communities that use a bulletin-board system and Weblog tools to discuss video scenes. Since these community activities include information valuable for video applications, we extract it as annotations. We developed a Web-based video sharing and annotation system called Synvie. We also performed an open experiment to acquire annotation data such as user comments on video scenes and scene-quoted Weblog entries and to evaluate our system. We have also developed a tag-based system for retrieving video scenes that is based on annotations accumulated in this open experiment.

Language：English  

Discussion mining is a preliminary study on gathering knowledge based on the content of face-to-face discussion meetings. To extract knowledge from discussion content, we have to analyze not only the surface arguments, but also semantic information such as a statement's intention and the discussion flow during meetings. We require a discussion ontology for this information. This discussion ontology forms the basis of our discussion methodology and requires semantic relations between elements in meetings. We must clarify these semantic relations to build the discussion ontology. We therefore generated discussion content and analyzed meeting metadata to build the ontology.

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)  

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

Language：Japanese

Language：Japanese

Event date： 2019.9

Language：English   Presentation type：Oral presentation (general)  

Venue：Vienna   Country：Austria  

In this paper, we propose a system for improving the discussion skills of participants in a meeting by automatically evaluating statements in the meeting and effectively feeding back the results of the evaluation to them. To evaluate the skills automatically, the system uses both acoustic features and linguistic features of statements. It evaluates the way a person speaks, such as their voice size, on the basis of the acoustic features, and it also evaluates the contents of a statement, such as the consistency of context, on the basis of linguistic features. These features can be obtained from meeting minutes. Since it is difficult to evaluate the semantic contents of statements such as the consistency of context, we build a machine learning model that uses the features of minutes such as speaker attributes and the relationship of statements. In addition, we argue that participants' heart rate (HR) data can be used to effectively evaluate their cognitive performance, specifically the performance in a discussion that consists of several Q&A segments (question-and-answer pairs). We collect HR data during a discussion in real time and generate machine-learning models for evaluation. We confirmed that the proposed method is effective for evaluating the discussion skills of meeting participants.

Event date： 2019.3

Language：English   Presentation type：Oral presentation (general)  

Venue：San Jose, CA, USA   Country：United States  

We propose building-scale virtual reality (VR), a real-world extension method different from augmented reality (AR), that uses automatically generated indoor 3D point cloud maps. To make the entire indoor area a pose tracking area, we attach an RGB-D camera to a VR headset, collect data with it, and use deep learning to build a model learned from the data. This method is more accurate than the conventional RGB-D SLAM method. Furthermore, to modify the VR space according to the purpose, segmentation and replacement of the 3D point cloud are performed. This is hard to do in AR, but it is essential technology for VR to be used in actual real-world work. We describe a disaster simulation including virtual evacuation drills and virtual work environments as application examples.

Event date： 2019.1

Language：English   Presentation type：Oral presentation (general)  

Venue：Thessaloniki, Greece   Country：Greece  

This paper introduces DensePoint, a densely sampled and annotated point cloud dataset containing over 10,000 single objects across 16 categories, by merging different kind of information from two existing datasets. Each point cloud in DensePoint contains 40,000 points, and each point is associated with two sorts of information: RGB value and part annotation. In addition, we propose a method for point cloud colorization by utilizing Generative Adversarial Networks (GANs). The network makes it possible to generate colours for point clouds of single objects by only giving the point cloud itself. Experiments on DensePoint show that there exist clear boundaries in point clouds between different parts of an object, suggesting that the proposed network is able to generate reasonably good colours. Our dataset is publicly available on the project page.

Event date： 2018.3

Language：English   Presentation type：Oral presentation (general)  

Venue：Portugal   Country：Portugal  

Heart rate variability (HRV) has recently seen a surge in interest regarding the evaluation of cognitive performance as it always be used to measure the autonomic nervous system function. In this study, we argue that a presenters' HRV data can be used to effectively evaluate their cognitive performance, specifically presenters' performance of discussion which consists of several Q&A segments (question and answer pairs) compared with using traditional natural language processing (NLP) such as semantic analysis. To confirm this, we used a non-invasive device, i.e., Apple Watch, to collect real-time updated HR data of presenters during discussions in our lab-seminar environment, their HRV data were analyzed based on Q&A segments, and three machine-learning models were generated for evaluation: logistic regression, support vector machine, and random forest. We also discuss the meaningful HRV features (metrics). Comparative experiments were conducted involving semantic data of Q&A statements alone and a combination of HRV and semantic data. The HRV data of presenters resulted in effective evaluation of discussion performance compared with using only semantic data. The combination of these two types of data could improve the discussion performance evaluation ability to some extent.

Event date： 2018.1

Language：English   Presentation type：Oral presentation (general)  

Venue：Hawaii, USA   Country：United States  

In this paper, we present high-probability statements mentioning future tasks during meetings that should lead to innovations and facilitate creative discussions. We also propose a creative activity support system that should help users to discover and execute essential tasks.

Event date： 2017.11

Language：English   Presentation type：Oral presentation (general)  

Venue：Vancouver, Canada   Country：Canada  

In this paper, we explain how to implement Building-Scale VR and its applications to disaster simulations. It is useful to express disaster situations by reconstructing actual buildings into virtual space and enable users in the building to experience such situations beforehand to learn how to properly act during a disaster.

Event date： 2016.12

Language：English   Presentation type：Oral presentation (keynote)  

Venue：Taipei, Taiwan   Country：Taiwan, Province of China  

Our university is currently developing an advanced physical-digital learning environment that can train students to enhance their discussion and presentation skills. The environment guarantees an efficient discussion among users with state-of-the-art technologies such as touch panel discussion tables, digital posters, and an interactive wall-sized whiteboard. It includes a data mining system that efficiently records, summarizes, and annotates discussions held in our facility. We also have developed a digital poster authoring tool, a novel tool for creating interactive digital posters displayed using our digital poster presentation system. Evaluation results show the efficiency of using our facilities: the data mining system and the digital poster authoring tool. In addition, our physical-digital learning environment will be further enhanced with some automated recognition systems that can detect interactions with the digital poster presentation system and the different discussion tools enabling a more automated skill evaluation and knowledge discovery from discussion and presentation records.

Event date： 2016.11

Language：English   Presentation type：Oral presentation (invited, special)  

Venue：Qingdao, China   Country：China  

Our university is currently developing an advanced physical-digital learning environment that can train students to enhance their discussion and presentation skills. The environment guarantees an efficient discussion among users with state-of-the-art technologies such as touch panel discussion tables, digital posters, and an interactive wall-sized whiteboard. It includes a data mining system that efficiently records, summarizes, and annotates discussions held inside our facility. We also developed a digital poster authoring tool, a novel tool for creating interactive digital posters displayed using our digital poster presentation system. Evaluation results show the efficiency of using our facilities: the data mining system and the digital poster authoring tool. In addition, our physical-digital learning environment will be further enhanced with some automated recognition systems that can detect interactions with the digital poster presentation system and the different discussion tools enabling a more automated skill evaluation and knowledge discovery from discussion and presentation records.

Event date： 2015.11

Language：English   Presentation type：Oral presentation (general)  

Country：Portugal  

We previously developed a discussion mining system that records face-to-face meetings in detail, analyzes their content, and conducts knowledge discovery. Looking back on past discussion content by browsing documents, such as minutes, is an effective means for conducting future activities. In meetings at which some research topics are regularly discussed, such as seminars in laboratories, the presenters are required to discuss future issues by checking urgent matters from the discussion records. We call statements including advice or requests proposed at previous meetings "task statements" and propose a method for automatically extracting them. With this method, based on certain semantic attributes and linguistic characteristics of statements, a probabilistic model is created using the maximum entropy method. A statement is judged whether it is a task statement according to its probability. A seminar-based experiment validated the effectiveness of the proposed extraction method.

Event date： 2015.11

Language：English   Presentation type：Oral presentation (general)  

Country：Portugal  

We have developed a new idea creation support tool in which (1) each idea is represented by a tree structure, (2) the idea is automatically evaluated on the basis of the tree structure so that the relative advantages among several alternative ideas is found, (3) the ideas are presented in a poster format, and (4) the ideas are shared by multiple users so that the ideas can be quoted and expanded upon by individual users. In this work, we explain the mechanisms of this tool, including the evaluation and poster conversion of ideas and collaborative idea creation, and briefly discuss our plan for the future.

Event date： 2014.7

Language：English   Presentation type：Oral presentation (general)  

Country：China  

Our university is currently developing an advanced physical-digital learning environment that can train the students with better discussion and presentation skills. The environment guarantees an efficient discussion among users with state-of-the-art technologies such as touch panel discussion tables and posters. It includes a data mining system that efficiently records, summarizes, and annotates the discussion. It will be further enhanced by using a vision system to facilitate the interactions enabling a more automated discussion mining.

Event date： 2013.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：鳥取大学   Country：Japan  

Event date： 2013.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：鳥取大学   Country：Japan  

Event date： 2013.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東北大学   Country：Japan  

Event date： 2013.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東北大学   Country：Japan  

Event date： 2013.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東北大学   Country：Japan  

Event date： 2013.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東北大学   Country：Japan  

Event date： 2013.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東北大学   Country：Japan  

Event date： 2013.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東北大学   Country：Japan  

Event date： 2013.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東北大学   Country：Japan  

Event date： 2013.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東北大学   Country：Japan  

Event date： 2013.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東北大学   Country：Japan  

Event date： 2013.3

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東北大学   Country：Japan  

Event date： 2013.1

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：多摩美術大学   Country：Japan  

Event date： 2012.5

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

While 3D maps are useful to visualize complicated shapes of objects inside buildings, it is very costly to create and maintain them.
We developed a mechanism to automatically generate indoor 3D maps by using a Small Unmanned Vehicle (SUV, for short).
SUV is capable of autonomous run and collection of 3D data captured by an RGB-D (RGB and Depth) image sensor.
SUV also has a function of generation of a 2D environmental map for localization of current position.
Futhermore, we also developed a Web-based system for social annotation to automatically-generated indoor 3D maps.
Using annotated 3D maps and SUV, we can realize highly informative indoor navigations based on intuitive and interactive views and location-aware information.

Event date： 2011.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2011.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2011.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2011.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2011.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2011.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2011.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2010.11

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

While research results of artificial intelligence have been looked forward to be available, for example, in Web-based advanced information systems and intelligent robotics, previous AI researches have not sufficiently shown that how many principles of intelligence are already explained or are yet to be solved.
Therefore we should make sure that intelligent computing systems are means to verify hypotheses on principles of intelligence.
Being always aware of this point, we can acquire series of verified or being verified hypotheses.
Based on these hypotheses, we can explain design principles and appropriateness of our developed systems with some sort of intelligent mechanisms.
In this article, I describe three perspectives of guideline for developing and deploying intelligent computing systems; they are real, social, and material.

Event date： 2010.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2010.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2010.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2010.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2010.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2010.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2010.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2010.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2010.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2010.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2009.8

Language：English   Presentation type：Oral presentation (general)  

Event date： 2009.8

Language：English   Presentation type：Oral presentation (general)  

Event date： 2009.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2009.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Ordinary casual meetings held by small groups are one of the most important knowledge activities in an organization, and the organization's performance deeply depends on such meetings. There are many systems that help facilitate casual meetings, but there are no common way to assist in reusing content from previous meetings during a current meeting. We developed a new casual meeting system called the TimeMachineBoard that uses multiple displays to enable users to retrieve and quote discussions f

Event date： 2009.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

We have been developing an IT-powered electric personal vehicle called the AT. AT stands for Attentive Townvehicle, which is a human-centered and context-aware vehicle that makes people's urban lives more comfortable and enjoyable.
AT is capable of omnidirectional movement, obstacle and collision avoidance, and autonomous travel to user specified destinations in indoors.
In this paper, we present AT's fundamental mechanism and techniques for moving obstacle avoidance.

Event date： 2008.12

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

A personal intelligent vehicle that is a main topic of this research project is an example product of a new research field called Mountable Computing, which supports not only anytime/anywhere computing functionalities but also computer-augmented physical movement and transportation activities. Mountable Computing contributes to augmentation of human body (physical movement abilities) and intelligence (sensing, remembering, and other cognitive abilities). We have been also studying physical and i

Event date： 2008.8

Language：English   Presentation type：Oral presentation (general)  

We have developed a system to support knowledge activity. Knowledge activity isactivity in which people continuously create ideas about themes and developthem into knowledge. First, the system generates minutes (linked to video andaudio of discussions) and metadata (on significant discussions). Then, userstag the minutes in order to identify certain parts contemplate the discussioncontent and write notes derived from the minutes with the system for use asdiscussion content. The system converts i

Event date： 2008.3

Language：Japanese  

Event date： 2008.3

Language：Japanese  

Event date： 2008.3

Language：Japanese  

Event date： 2008.3

Language：Japanese  

Event date： 2008.3

Language：Japanese  

Event date： 2008.3

Language：Japanese  

Event date： 2008.3

Language：Japanese  

Event date： 2008.3

Language：Japanese  

Event date： 2008.3

Language：Japanese  

Event date： 2008.3

Language：Japanese  

Event date： 2008.2

Language：Japanese  

Event date： 2007.6

Language：Japanese  

Event date： 2007.6

Language：Japanese  

Event date： 2007.6

Language：Japanese  

Event date： 2007.6

Language：Japanese  

Event date： 2007.6

Language：Japanese  

Event date： 2007.6

Language：Japanese  

Event date： 2007.6

Language：Japanese  

Event date： 2007.6

Language：Japanese  

Event date： 2007

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2007

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2007

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2007

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2006

Language：Japanese  

Country：Japan  

Event date： 2005

Language：English   Presentation type：Oral presentation (general)  

Event date： 2005

Language：Japanese  

Country：Japan  

Event date： 2005

Language：English   Presentation type：Oral presentation (general)  

Event date： 2004

Language：English   Presentation type：Poster presentation  

Country：Japan  

Event date： 2004

Language：English   Presentation type：Poster presentation  

Country：Japan  

Event date： 2004

Language：English   Presentation type：Poster presentation  

Country：Japan  

Event date： 2003

Language：Japanese  

Country：Japan  

Event date： 2002

Language：English   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2002

Language：English   Presentation type：Oral presentation (general)  

Event date： 2001

Language：English   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 1998

Language：English  

Event date： 1995

Language：English  

Event date： 1992

Language：English