受賞国：日本国

受賞国：日本国

受賞国：日本国

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：2023 20th International Conference on Ubiquitous Robots, UR 2023  

Wheelchairs are an important means of transportation for people with disabilities. However, because wheelchairs move on wheels, it is impossible for wheelchair users to move along paths such as stairs that cannot be covered by wheels, thus imposing significant limitations on their mobility. Although some wheelchairs are now available with crawler mechanisms, they cannot handle spiral staircases, in which the length of the tread differs between the inside and outside of the staircase. This paper proposes a quadruped wheelchair that can be used on all types of stairs. The proposed quadruped wheelchair is a fusion of a quadruped robot and a wheelchair, and has the potential to achieve high mobility on both plain and uneven terrain. On the other hand, it is very complicated to carry a person on the wheelchair. In this paper, we show that a quadruped wheelchair with a person on board can ascend and descend stairs using a simulator and reinforcement learning, which is a milestone toward the realization of a quadruped wheelchair.

DOI： 10.1109/UR57808.2023.10202377

Web of Science

Scopus

記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Presence: Teleoperators and Virtual Environments  

In this paper, a method for 3D human body tracking using multiple cameras and an automatic evaluation method using machine learning are developed to construct a virtual reality (VR) dance self-training system for fast-moving hip-hop dance.Dancers’ movement data are input as time-series data of temporal changes in joint point positions and rotations and are categorized into instructional items that are frequently pointed out by coaches as areas for improvement in actual dance lessons. For automatic dance evaluation, contrastive learning is used to obtain better expression vectors with less data. As a result, the accuracy when using contrastive learning was 0.79, a significant improvement from 0.65 without contrastive learning. In addition, since each dance is modeled by a coach, the accuracy was slightly improved to 0.84 by using, as input, the difference between the expression vectors of the model’s and the user’s movement data. Eight subjects used the VR dance training system, and results of a questionnaire survey confirmed that the system is effective.

DOI： 10.1162/pres_a_00383

Web of Science

Scopus

記述言語：英語   出版者・発行元：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

Open Access

Web of Science

Scopus

PubMed

記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Applied Sciences (Switzerland)  

In building-scale VR, where the entire interior of a large-scale building is a virtual space that users can walk around in, it is very important to handle movable objects that actually exist in the real world and not in the virtual space. We propose a mechanism to dynamically detect such objects (that are not embedded in the virtual space) in advance, and then generate a sound when one is hit with a virtual stick. Moreover, in a large indoor virtual environment, there may be multiple users at the same time, and their presence may be perceived by hearing, as well as by sight, e.g., by hearing sounds such as footsteps. We, therefore, use a GAN deep learning generation system to generate the impact sound from any object. First, in order to visually display a real-world object in virtual space, its 3D data is generated using an RGB-D camera and saved, along with its position information. At the same time, we take the image of the object and break it down into parts, estimate its material, generate the sound, and associate the sound with that part. When a VR user hits the object virtually (e.g., hits it with a virtual stick), a sound is generated. We demonstrate that users can judge the material from the sound, thus confirming the effectiveness of the proposed method.

DOI： 10.3390/app11167546

DOI： 10.3390/app11167546

Open Access

Web of Science

Scopus

担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：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

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：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

Web of Science

Scopus

記述言語：日本語   出版者・発行元：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

担当区分：最終著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：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

記述言語：英語   掲載種別：研究論文（学術雑誌）  

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

記述言語：英語   掲載種別：研究論文（学術雑誌）  

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.

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

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

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

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。

記述言語：英語   掲載種別：研究論文（学術雑誌）  

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.

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

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

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

DOI： 10.3169/itej.66.J461

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

プロジェクトミーティングのように継続的に行われるミーティングをよりよいものにするためには，過去の話し合いの内容を思い出しながら話し合うことが重要である．本研究では，相互に協調して動作する情報的に拡張されたボードとタブレット型デバイスを用いた新たなミーティング支援システムについて述べる．本システムを用いることで，日常的なミーティングの内容が検索・要約など高度利用が可能なコンテンツとしてクラウドに記録・蓄積され，タブレット型デバイスからいつでも閲覧できる．さらに，過去のコンテンツの一部を進行中のミーティングに引用して話し合うことができる．

担当区分：筆頭著者   記述言語：日本語  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

記述言語：英語  

記述言語：英語   掲載種別：研究論文（学術雑誌）  

我々は，映像シーンについて議論するために掲示板システムやブログツールなどを利用するコミュニティの活動から映像コンテンツの意味を獲得する仕組みについて述べる．これらのコミュニティ活動にはビデオ応用に役立つ情報が含まれているので，それらをアノテーションとして獲得する．我々はSynvieと呼ばれるWebベースのビデオ共有とアノテーションのためのシステムを開発した．また，映像シーンに関連付けられたユーザコメントやシーンを引用したブログエントリーを獲得するために，システムの評価を目的とした公開実験を実施した．我々は，また，本実験で獲得したアノテーションに基づく，タグを用いたビデオシーン検索システムを開発した．

記述言語：英語  

本研究では、ディスカッションオントロジーの構築を目指し、メタデータを付与し構造化した会議情報の解析を行う。会議情報を構造化するために、Webブラウザベースのツールを用い実世界の会議に関する情報を獲得する。そして構造化された会議情報から、議論の中核となる発言がどのような意図で発されたか、機械学習を用いた判別ルールから推定し、議論構造の抽出を行う。抽出した議論構造から効率的に知識共有が可能な議論を行うための方法論の確立を目指す。

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

記述言語：日本語   掲載種別：研究論文（学術雑誌）  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

記述言語：日本語

記述言語：日本語

開催年月日： 2019年11月

記述言語：英語   会議種別：口頭発表（一般）  

国名：日本国  

Our university is currently developing an advanced physical-digital learning environment that can train students to enhance their presentation and discussion skills. The environment guarantees an efficient presentation and discussion among users with state-of-the-art technologies such as touch panel digital poster panels. It includes an automatic evaluation system that efficiently records, analyses, and evaluates the presenter's presentation and discussion skills. We also have developed a digital poster authoring tool, a novel tool for creating interactive digital posters displayed on the digital poster panels. The environment also allows the students for effective self-training of presentation by authoring digital posters and getting feedbacks of automatic evaluation of presentation. We call such education promoted by the AI technologies AI-Powered Education.

開催年月日： 2019年9月

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Vienna   国名：オーストリア共和国  

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.

開催年月日： 2019年3月

記述言語：英語   会議種別：口頭発表（一般）  

開催地：San Jose, CA, USA   国名：アメリカ合衆国  

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.

開催年月日： 2019年1月

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Thessaloniki, Greece   国名：ギリシャ共和国  

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.

開催年月日： 2018年3月

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Portugal   国名：ポルトガル共和国  

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.

開催年月日： 2018年1月

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Hawaii, USA   国名：アメリカ合衆国  

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.

開催年月日： 2017年11月

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Vancouver, Canada   国名：カナダ  

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.

開催年月日： 2016年12月

記述言語：英語   会議種別：口頭発表（基調）  

開催地：Taipei, Taiwan   国名：台湾  

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.

開催年月日： 2016年11月

記述言語：英語   会議種別：口頭発表（招待・特別）  

開催地：Qingdao, China   国名：中華人民共和国  

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.

開催年月日： 2015年11月

記述言語：英語   会議種別：口頭発表（一般）  

国名：ポルトガル共和国  

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.

開催年月日： 2015年11月

記述言語：英語   会議種別：口頭発表（一般）  

国名：ポルトガル共和国  

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.

開催年月日： 2014年7月

記述言語：英語   会議種別：口頭発表（一般）  

国名：中華人民共和国  

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.

開催年月日： 2013年9月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：鳥取大学   国名：日本国  

開催年月日： 2013年9月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：鳥取大学   国名：日本国  

開催年月日： 2013年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東北大学   国名：日本国  

開催年月日： 2013年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東北大学   国名：日本国  

開催年月日： 2013年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東北大学   国名：日本国  

開催年月日： 2013年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東北大学   国名：日本国  

開催年月日： 2013年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東北大学   国名：日本国  

開催年月日： 2013年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東北大学   国名：日本国  

開催年月日： 2013年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東北大学   国名：日本国  

開催年月日： 2013年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東北大学   国名：日本国  

開催年月日： 2013年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東北大学   国名：日本国  

開催年月日： 2013年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東北大学   国名：日本国  

開催年月日： 2013年1月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：多摩美術大学   国名：日本国  

開催年月日： 2012年5月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：明治大学   国名：日本国  

複雑な建物内の形状を直感的に表現するためには立体的な地図が適している．しかし，3次元地図の作成には非常に高いコストがかかり，屋内の状況が頻繁に変化する場合などの運用が困難である．そこで，自律走行可能な小型無人移動体（SUVと呼称する）を用いて屋内の様々なデータを自動的に収集し，そのデータに基づいて3次元地図の生成を行う仕組みを実現した．
SUVはRGB-Dカメラを用いて屋内の3次元形状データを取得し，レーザーレンジセンサーを用いて自己位置推定のための2次元環境地図を生成する．
さらに，3次元地図をWeb上で利用可能にし，ユーザーが自由に地図にアノテーションを施せる仕組みを開発した．
アノテーションを含む3次元地図とSUVを用いることで，直感的で対話的なビューと位置依存の詳細な情報に基づく高度なナビゲーションが実現できる．

開催年月日： 2011年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東京工業大学 大岡山キャンパス   国名：日本国  

開催年月日： 2011年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東京工業大学 大岡山キャンパス   国名：日本国  

開催年月日： 2011年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東京工業大学 大岡山キャンパス   国名：日本国  

開催年月日： 2011年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東京工業大学 大岡山キャンパス   国名：日本国  

開催年月日： 2011年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東京工業大学 大岡山キャンパス   国名：日本国  

開催年月日： 2011年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東京工業大学 大岡山キャンパス   国名：日本国  

開催年月日： 2011年3月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：東京工業大学 大岡山キャンパス   国名：日本国  

開催年月日： 2010年11月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：国立情報学研究所   国名：日本国  

Webにおける高度な情報共有や知能ロボットなど、人工知能研究の成果に対する人々の期待は少なくないにも関わらず、知能の原理がどこまで解明され、どのような問題が未解決なのか、あまり明確にはなっていない。
そこで、知能システムは、知能の原理に関する仮説検証の手段である、という考えをより徹底することを提案する。
それによって、検証された（あるいは検証されつつある）仮説集ができる。
知能システムの実現の仕方はさまざまでも、それが前提としている原理がシンプルで、それに関する仮説が検証されていることで、知能システムの設計思想やそれが実装する知能の妥当性が説明できるだろう。
本稿では、知能システムを構築する上で、今後重要になると思われる、リアル（実世界性）、ソーシャル（社会性）、マテリアル（物質性・道具性）の3つの観点について述べ、知能システムの展望について述べる。

開催年月日： 2010年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2010年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2010年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2010年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2010年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2010年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2010年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2010年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2010年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2010年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2009年8月

記述言語：英語   会議種別：口頭発表（一般）  

開催年月日： 2009年8月

記述言語：英語   会議種別：口頭発表（一般）  

開催年月日： 2009年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

情報端末の発展と小型化に伴い、多くの人が携帯電話などの情報端末を持ち歩くようになった。このため、場所や時間、状況に応じたさまざまな情報サービスを享受することができるようになった。しかし、これらの情報端末は小型ゆえにセンサ類などの機能や情報処理能力が乏しく、周囲の状況を即座に把握できず、依然として使用者の入力に依存する面が大きいなどの欠点がある。そこで我々は、移動体、周囲の状況を取得するセンサ類、そして情報端末の3つを統合して情報処理と人間の物理的な移動を連動させるという考えのもと、AT(Attentive To

開催年月日： 2009年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

少人数のグループにおいて日々行われるカジュアルなミーティングは、知的活動の中心に位置し、知的活動の成果を左右する。
カジュアルミーティングを支援するためのシステムは様々なものが提案されているが、議論中・議論後に過去のミーティングに遡って、議論を検索・引用などを行う一般的な仕組みは実現されていない。
本論文では、少人数のグループによる知的活動の記録と検索、そして引用や要約などの再利用を促進するための、複数ディスプレイ環境に対応し、ホワイトボードとプレゼンテーションソフトの利点を併せ持つ、新たなカジ

開催年月日： 2009年3月

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

近年の情報端末の小型化・高性能化に伴い、我々は常に情報端末を持ち歩くようになった。さらに、小型情報端末は人間の置かれた状況を認識し、それに応じた情報提供をするようになった。しかし小型情報端末では、情報処理結果を直接的に人間の行動に反映させることができない。そこで我々は、乗り物を情報端末化して情報処理と人間の物理的な移動とを連動させるという考え方のもと、AT~(Attentive Townvehicle)と呼ばれる個人用知的移動体を開発している。
これまでにATを用いて、ユーザーの指定する目的地に自動的に移動する仕組みについ

開催年月日： 2008年12月

記述言語：日本語   会議種別：口頭発表（招待・特別）  

国名：日本国  

近年、自動車同士の衝突事故を回避するための仕組みが開発・実用化されているが、歩行者や個人用の低速移動体の事故防止のための仕組みは十分に研究が行われていない。
本研究では、自動車などの高速移動体と協調して個人を事故から守るために、柔軟な回避運動を考慮し全方位に移動可能な個人用知的移動体を開発した。
いわゆるモバイル／ユビキタス・コンピューティングが、情報機器を携帯したり、周囲の物理的環境に埋め込むことによって情報環境を遍在化し、人間活動を支援することを目指しているのに対し、われわれの手法は個人に適

開催年月日： 2008年8月

記述言語：英語   会議種別：口頭発表（一般）  

開催年月日： 2008年3月

記述言語：日本語  

開催年月日： 2008年3月

記述言語：日本語  

開催年月日： 2008年3月

記述言語：日本語  

開催年月日： 2008年3月

記述言語：日本語  

開催年月日： 2008年3月

記述言語：日本語  

開催年月日： 2008年3月

記述言語：日本語  

開催年月日： 2008年3月

記述言語：日本語  

開催年月日： 2008年3月

記述言語：日本語  

開催年月日： 2008年3月

記述言語：日本語  

開催年月日： 2008年3月

記述言語：日本語  

開催年月日： 2008年2月

記述言語：日本語  

開催年月日： 2007年6月

記述言語：日本語  

開催年月日： 2007年6月

記述言語：日本語  

開催年月日： 2007年6月

記述言語：日本語  

開催年月日： 2007年6月

記述言語：日本語  

開催年月日： 2007年6月

記述言語：日本語  

開催年月日： 2007年6月

記述言語：日本語  

開催年月日： 2007年6月

記述言語：日本語  

開催年月日： 2007年6月

記述言語：日本語  

開催年月日： 2007年

記述言語：日本語   会議種別：口頭発表（招待・特別）  

国名：日本国  

開催年月日： 2007年

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2007年

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2007年

記述言語：日本語   会議種別：口頭発表（一般）  

国名：日本国  

開催年月日： 2006年

記述言語：日本語  

国名：日本国  

開催年月日： 2005年

記述言語：英語   会議種別：口頭発表（一般）  

開催年月日： 2005年

記述言語：日本語  

国名：日本国  

開催年月日： 2005年

記述言語：英語   会議種別：口頭発表（一般）  

開催年月日： 2004年

記述言語：英語   会議種別：ポスター発表  

国名：日本国  

開催年月日： 2004年

記述言語：英語   会議種別：ポスター発表  

国名：日本国  

開催年月日： 2004年

記述言語：英語   会議種別：ポスター発表  

国名：日本国  

開催年月日： 2003年

記述言語：日本語  

国名：日本国  

開催年月日： 2002年

記述言語：英語   会議種別：口頭発表（招待・特別）  

国名：日本国  

開催年月日： 2002年

記述言語：英語   会議種別：口頭発表（一般）  

開催年月日： 2001年

記述言語：英語   会議種別：口頭発表（招待・特別）  

国名：日本国  

開催年月日： 1998年

記述言語：英語  

開催年月日： 1995年

記述言語：英語  

開催年月日： 1992年

記述言語：英語