Faculty Profiles - ENOKIBORI Yu

Papers - ENOKIBORI Yu

Division display 1 - 20 of about 164 ／ All the affair displays >>

rTsfNet: a DNN Model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition Reviewed International journal

Yu Enokibori

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Vol. 8 ( 4 ) page： 1 - 16 2024.12

Integrated Utilization of IMU-based Human Activity Recognition Datasets Across Varied Configurations using MIG HAR Dataset Reviewed International journal

Akihisa Tsukamoto, Kenji Mase, Yu Enokibori

International Journal of Activity and Behavior Computing Vol. 2024 ( 2 ) page： 1 - 21 2024.6

匠の技能解析に向けたラベル付き大規模データ収集システムの作業現場導入に試みるボトムアップアプローチ Invited

榎掘優, 間瀬健二

システム制御情報学会学会誌「システム／制御／情報」 Vol. 67 ( 2 ) page： 72 - 77 2023.2

BLEビーコンを用いた車椅子の自走・介助および活動量の推定手法 Reviewed

大鐘勇輝，榎堀優，梶克彦

情報処理学会論文誌 Vol. 64 ( 1 ) page： 145 - 157 2023.1

Joint Position Estimation for Body Pressure Images during Sleep: An Extension for CPM Using Body Area and Posture Estimation Mashups Reviewed

Kei Iwase, Yu Enokibori, Naoto Yoshida, Kenji Mase

Journal of Information Processing Vol. 29 page： 620 - 629 2021.10

Educational Support System for Pressure Ulcer Prevention using Textile Pressure Sensor Reviewed

Ryosuke Onose, Yu Enokibori, Yuko Harasawa, Kenji Mase

IPSJ Journal page： 746 - 755 2020.3

Difficulty Control Method for Change Detection Task with Daily Common Objects Reviewed

Yu Enokibori, Takeshi Hidukuri, Kenji Mase

The IEICE Transactions on Information and Systems Vol. J103-D ( 3 ) page： 111 - 119 2020.3

e-Coaching Technologies Based on Personal Lifelog Invited Reviewed

Kenji Mase, Takatsugu Hirayama, Jun-ya Morita, Yu Enokibori

IPSJ Transactions on Computers and Education Vol. 5 ( 2 ) page： 1 - 11 2019.6

Data Augmentation to Build High Performance DNN for In-bed Posture Classification Reviewed International journal

Yu Enokibori, Kenji Mase

Journal of Information Processing Vol. 26 page： 718 - 727 2018.10

A Garment-type Pressure Sensor for Pressure-ulcer Care Use: To Measure Pressures on Human Skin Reviewed

Ryosuke Onose, Yu Enokibori, Kenji Mase

Journal of Information Processing Society of Japan Vol. 59 ( 10 ) page： 1827 - 1836 2018.10

Personal viewpoint navigation based on object trajectory distribution for multi-view videos Reviewed International journal

Xueting Wang, Kensho Hara, Yu Enokibori, Takatsugu Hirayama, Kenji Mase

IEICE Transactions on Information and Systems Vol. E101D ( 1 ) page： 193 - 204 2018.1

Authorship：Lead author Language：English Publishing type：Research paper (scientific journal) Publisher：Institute of Electronics, Information and Communication, Engineers, IEICE

Multi-camera videos with abundant information and high flexibility are useful in a wide range of applications, such as surveillance systems, web lectures, news broadcasting, concerts and sports viewing. Viewers can enjoy an enhanced viewing experience by choosing their own viewpoint through viewing interfaces. However, some viewers may feel annoyed by the need for continual manual viewpoint selection, especially when the number of selectable viewpoints is relatively large. In order to solve this issue, we propose an automatic viewpoint navigation method designed especially for sports. This method focuses on a viewer’s personal preference for viewpoint selection, instead of common and professional editing rules. We assume that different trajectory distributions of viewing objects cause a difference in the viewpoint selection according to personal preference. We learn the relationship between the viewer’s personal viewpoint-selection tendency and the spatio-temporal game context represented by the objects trajectories. We compare three methods based on Gaussian mixture model, SVM with a general histogram and SVM with a bag-of-words to seek the best learning scheme for this relationship. The performance of the proposed methods are evaluated by assessing the degree of similarity between the selected viewpoints and the viewers’ edited records.

DOI： 10.1587/transinf.2017EDP7122

Scopus

Why Did False Rumors Diffuse after the 2011 Earthquake off the Pacific Coast of Tohoku? Impact Analysis of the Network Structure Reviewed International journal

Shohei Usui, Fujio Toriumi, Takatsugu Hirayama, Yu Enokibori, Kenji Mase

ELECTRONICS AND COMMUNICATIONS IN JAPAN Vol. 98 ( 9 ) page： 1 - 13 2015.9

Authorship：Lead author Language：English Publishing type：Research paper (scientific journal) Publisher：WILEY-BLACKWELL

As a steady new network communications tool, social media have reached global proportions. This phenomenon has had an impact on societies all over the world. Above all, people provide information to Twitter and Facebook on a daily basis. As a result, vast amounts of data exist on Twitter and Facebook, and we can expect to gather useful information from these services. Twitter was changed by the 2011 Tohoku Earthquake. Further, Twitter greatly contributed to the diffusion of information. For example, many users checked on the safety of their friends or family. However, numerous false rumors were spread, probably due to the source of information being unclear. To prepare for future disasters, we must analyze the diffusion of information through social media as soon as possible. In this paper we analyze how the diffusion of information on Twitter has been influenced by structural changes in the network that are caused by communication among users. As a result, just after the 2011 earthquake in the Tohoku region in Japan, it became easier for information to spread in the network. However, this means that misinformation too can spread. We also found that a few users believed misinformation despite being corrected. (C) 2015 Wiley Periodicals, Inc.

DOI： 10.1002/ecj.11687

Web of Science

Posture-Change Adaptation for Torso-girths based Respiratory Volume Estimation Reviewed

Yu Enokibori, Yoshu Ito, Takatsugu Hirayama, Kenji Mase

Journal of the Society of Biomechanisms Japan Vol. 22 page： 203 - 211 2014

Dwarfstar : Towards an Optimised Framework for Lifelog Analysis Reviewed

Tkaaki Myojo, Tomonori Matsui, Yu Enokibori, Nobuhiko Nishio

The IEICE transactions on information and systems (Japanese edition) Vol. 96 ( 5 ) page： 1267 - 1278 2013

Reproduction of expert's subjective skill-level evaluation with wearable acceleration and gyroscope sensors on metal-filing Reviewed

Enokibori Yu, Masa Kenji

Transactions of the Japanese Society for Artificial Intelligence Vol. 28 ( 4 ) page： 391 - 399 2013

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

On-demand, skill-level self-checks are required to establish effective training, and this is the same for metalfiling. However, such a training system has not been established yet for metal-filing because the current skill-level check depends on subjective skill-level estimation by experts. Such subjective skill-level estimation is composed of various complex viewpoints, and its estimation mechanisms cannot be represented in language because they are generated and supported by the experience of experts. That is why sensor-based, on-demand systems cannot faithfully imitate or replace the current skill-level checks of metal-filing. To solve this problem, we analyze the relationships among the subjective skill-level estimation mechanisms of experts and metal-filing mechanics structures. Our analysis yielded three simple viewpoints and related measures: class 2 lever-like movement measure (L), upper body rigidity measure (R), and pre-acceleration measure (A). Surveys of experts also yielded another viewpoint and a related measure: stability measure (S). These four measures successfully reproduced the subjective skill-level estimation of experts (adjusted-R&lt
sup&gt
2&lt
/sup&gt
= 0.90,p &lt
0.1,N = 10). The coefficients for the measures, which suggest that A is the main factor of the subjective skill-level estimation of experts, also suggest that effective training must emphasize these points in this order: A&gt
L&gt
R. S's coefficient suggests that the skill-level scores of experts are reduced by 69% when the filings of learners fail. In addition, since these four measures can be calculated with three small wearable hybrid sensors, they can be implemented on scalable wearable sensor-based skill-training systems. In future works, we will implement one such system, integrate it into a skill-training center's teaching plan, and assess how much the system improved the learning speeds of the students.

DOI： 10.1527/tjsai.28.391

Scopus

PeerPool: DNS Query-based Networking for Inter-Smart Environment Communication Reviewed

Yu Enokibori, Etsushi Nakano, Ismail Arai, Nobuhiko Nishio

IPSJ Transactions on Advanced Computing System Vol. 2 ( 4 ) page： 37 - 47 2009

A Virtualization of Smart Environment for Flexible Structure Reconfiguration Reviewed

Yu Enokibori, Nobuhiko Nishio

Journal of Information Processing Society of Japan Vol. 49 ( 1 ) page： 58 - 68 2008

Unified-usage-behavior Based KANSEI Sharing Among Who Have Similar KANSEI Background Reviewed International journal

Yu Enokibori, Jun Yamagami, Naoto Yoshida, Tomoko Yonezawa, and Kenji Mase

The 2024 International Conference on Kansei Engineering and Emotion Research (KEER2024) 2024.11

Controlling Age and Similarity to User's Face for Trustworthiness of Interactive Agent: Generative Faces and Acceptability Reviewed International journal

Tomoko Yonezawa, Shinji Iwata, Naoto Yoshida, Kenji Mase, and Yu Enokibori

The 2024 International Conference on Kansei Engineering and Emotion Research (KEER2024) 2024.11

Database Construction of Facial Expressions of Elderly Japanese and Evaluation Based on FACS Reviewed International journal

Naoto Yoshida, Hiroto Murakami, Tomoko Yonezawa, Kenji Mase, and Yu Enokibori

The 2024 International Conference on Kansei Engineering and Emotion Research (KEER2024) 2024.11

　 PREV - NEXT 　

<ENOKIBORI Yu>

Papers - ENOKIBORI Yu