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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, MBE (Joint) |
2023-03-15 13:50 |
Tokyo | The Univ. of Electro-Communications (Primary: On-site, Secondary: Online) |
Comparison of classification accuracy by frequency band restriction on emotion recognition from EEG Raiki Yamane, Shin'ichiro Kanoh (SIT)  NC2022-115 |
Accuracy of emotion classification in deep learning when frequency band restriction is used as a preprocessing method fo... [more] | NC2022-115 pp.127-132 |
| HCGSYMPO (2nd) |
2022-12-14 - 2022-12-16 |
Kagawa | Onsite (Sunport Takamatsu) and Online (Primary: On-site, Secondary: Online) |
Motion Classification of Cleaning Tasks by Machine Learning Using Short-Time Motion and Biometric Data Yuya Itagaki, Kazuki Maeda, Yuya Okazaki, Yuko Horita (Univ of Toyama) |
Moderate exercise is considered to be one of the most important factors for health promotion, and cleaning work is attra... [more] | |
| HCGSYMPO (2nd) |
2022-12-14 - 2022-12-16 |
Kagawa | Onsite (Sunport Takamatsu) and Online (Primary: On-site, Secondary: Online) |
Motion similarity classification of Radio Gymnastics by using information of 3D skeletal features Ryohei Yamazaki, Shigeru Akamatsu (Hosei Univ.) |
The purpose of this study is to examine effective features for classification of movements like human evaluation based o... [more] | |
| HCGSYMPO (2nd) |
2021-12-15 - 2021-12-17 |
Online | Online | Estimation and Classification of Short Video Viewers' Emotions by Random Forest Using Bio-signals Ryuichi Inoue, Mutsumi Suganuma, Wataru Kameyama (Waseda Univ.) |
The authors have been conducting a research of video viewers’ emotion estimation by machine learning using bio-signals t... [more] | |
| HCGSYMPO (2nd) |
2021-12-15 - 2021-12-17 |
Online | Online | Enhancement of sign language motion classification accuracy by adding finger information using OpenPose Tsukasa Wakao, Wataru Odagiri, Sato Tatsuya, Yuusuke Kawakita, Hiromitsu Nishimura, Hiroshi Tanaka (KAIT) |
As a sign language motion classification method, the direction vector from neck to shoulder, elbow, and wrist was calcul... [more] | |
| WIT, HI-SIGACI | 2021-12-08 16:00 |
Online | Online | Detection of Footstepping Motion While Seated for Tourism System Using Virtual Reality Yuito Yamada, Yudai Ueda, Takuma Tsuchiya, Sho Ooi, Norihiko Kawai, Mutsuo Sano (OIT) WIT2021-35 |
We create a system that aims to maintain the health of the elderly through sightseeing of nature using virtual reality. ... [more] | WIT2021-35 pp.18-21 |
| MBE, NC (Joint) |
2021-10-28 14:20 |
Online | Online | A Study on Affective BCI Using Reservoir Computing and Fractal Analysis Yuuma Matsuda, Masahiro Nakagawa (NUT) MBE2021-22 |
Today, the Brain Computer Interface (BCI) using EEG for quantification of sensitivity have been studying, and the deep l... [more] | MBE2021-22 pp.26-31 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2021-06-29 14:20 |
Online | Online | Investigation of pretext task for a classification model of human interaction motion Kenshiro Ata, Yusuke Nishimura (Osaka Univ.), Yuya Okadome, Yutaka Nakamura (RIKEN GRP), Hiroshi Ishiguro (Osaka Univ.) NC2021-14 IBISML2021-14 |
The technology of recognizing human actions has been used not only for recognizing human activities but also for control... [more] | NC2021-14 IBISML2021-14 pp.97-102 |
| HCGSYMPO (2nd) |
2020-12-15 - 2020-12-17 |
Online | Online | Relationship between subjective evaluation of emotional stimulation images and pupillary reaction Nijika Murokawa, Minoru Nakayama (Tokyo Tech) |
In this study, we investigated the relationship between the observer's subjective impression and the pupillary light ref... [more] | |
| PRMU | 2020-10-10 11:00 |
Online | Online | Extraction and Classification of Trajectory Motion in Lyric Video Shota Sakaguchi (Kyushu Univ.), Jun Kato, Masataka Goto (AIST), Seiichi Uchida (Kyushu Univ.) PRMU2020-37 |
The purpose of this paper is to track lyric words in a lyric video and classify their movements. Lyric videos are create... [more] | PRMU2020-37 pp.116-121 |
| RECONF | 2020-05-29 10:25 |
Online | Online | FPGA-based human motion estimation based on amalgamated data from multiple sensors Xin Du, Yutaka Shinkai, Mizuki Itoh, Yoshiki Yamaguchi (Tsukuba Univ.) RECONF2020-12 |
This study proposes an FPGA-based system for identifying and estimating human motion by detecting data from sensors in d... [more] | RECONF2020-12 pp.65-70 |
| EMCJ, MICT (Joint) |
2020-03-13 11:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. (Cancelled but technical report was issued) |
Human motion classification by convolutional neural network using signal strength of WBAN Shintaro Sano, Aoyagi Takahiro (TokyoTech) MICT2019-53 |
In this report, human motion classification by convolutional neural networks (CNNs) using the signal strength of WBANs (W... [more] | MICT2019-53 pp.5-9 |
| RISING (2nd) |
2019-11-26 10:30 |
Tokyo | Fukutake Learning Theater, Hongo Campus, Univ. Tokyo | [Invited Lecture]
Wearable Body Motion Classification System using Low-Cost Wireless Sensor Network Masahiro Mitta, Minseok Kim (Niigata Univ.) |
In this work, we developed a real-time motion identification system using the radio channel characteristics of wearable ... [more] | |
| RISING (2nd) |
2019-11-26 14:10 |
Tokyo | Fukutake Learning Theater, Hongo Campus, Univ. Tokyo | [Invited Lecture]
Reliable and Low-Energy Wireless Body Area Network by Machine Learning
-- Transmission Power Control based on Human Motion Classification using Features Automatically Extracted From Signal Strength -- Shintaro Sano, Takahiro Aoyagi (Tokyo Tech) |
In wireless body area networks (WBANs), both high reliability and low power consumption are required. Our research group... [more] | |
| HCS | 2019-10-26 11:20 |
Tokyo | Nihon Univ. | Emotional speech classification using stacking by DNN and RF Nayuta Tanaami, Minoru Hayashi (Meisei Univ.) HCS2019-42 |
Conventionally, support vector machine (SVM) has been used for emotional speech classification. Recently, however, resea... [more] | HCS2019-42 pp.11-15 |
| SP | 2019-08-28 17:00 |
Kyoto | Kyoto Univ. | Speech Emotion Classification based on Multi-Label Emotion Existence Estimation Atsushi Ando, Ryo Masumura, Hosana Kamiyama, Satoshi Kobashikawa, Yushi Aono (NTT) SP2019-16 |
This paper presents a novel speech emotion classification that addresses the ambiguous nature of emotions in speech. Mos... [more] | SP2019-16 pp.39-44 |
| NLC, IPSJ-ICS | 2019-06-21 15:40 |
Hiroshima | Hiroshima University of Economics (Tatemachi Campus) | Semi-Automatic Labeling for Emotion Classification with Deep Learning Kyosuke Masuda, Hiromitsu Nishizaki (Univ. of Yamanashi) NLC2019-5 |
We previously considered that the emotional classification method based on deep learning for tweets on social networking... [more] | NLC2019-5 pp.29-33 |
| SIP, MI, IE | 2019-05-24 09:00 |
Aichi | Hierarchical Personal Identification in Short and Long Distance using Blink Motion Features Mariko Nakano, Ritsuko Tokunaga, Yoko Uchida, Daisuke Sugimura (Tsuda Univ.) SIP2019-7 IE2019-7 MI2019-7 |
We propose a method for person identification using blink motion features. Previous methods are difficult to achieve a p... [more] | SIP2019-7 IE2019-7 MI2019-7 pp.29-32 |
|
| IMQ, IE, MVE, CQ (Joint) [detail] |
2019-03-14 10:50 |
Kagoshima | Kagoshima University | Estimation of Video Viewers' Emotion by SRC using Bio-signals and Facial Feature Points Yui Tagami, Mutsumi Suganuma, Wataru Kameyama (Waseda Univ.), Simon Clippingdale (NHK STRL) CQ2018-95 |
Aiming for making more accurate content recommendation system, we estimate video viewers’ emotion based on questionnaire... [more] | CQ2018-95 pp.19-24 |
| CS | 2018-11-01 11:00 |
Ehime | The Shiki Museum | Emotion Extraction from Face Images and its Quantification/Visualization
-- Experimental Evaluation in Music Concerts and Rakugo Performance -- Seima Todo, Tetsuo Tsujioka, Kazunobu Okazaki, Akihiro Odanaka (Osaka City Univ.) CS2018-63 |
Nowadays, machine learning, especially a deep learning technique for image classification, has been one of attractive re... [more] | CS2018-63 pp.43-49 |
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