Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
CAS, NLP |
2018-10-19 09:30 |
Miyagi |
Tohoku Univ. |
Person recognition by gait using an Extreme Learning Machine Hayato Benitani, Masaharu Adachi (Tokyo Denki Univ.) CAS2018-49 NLP2018-84 |
In this research, we propose a method of person recognition using an Extreme Learning Machine (ELM). We proposed an ELM ... [more] |
CAS2018-49 NLP2018-84 pp.63-68 |
BioX |
2018-10-12 09:00 |
Okinawa |
Nobumoto Ohama Memorial Hall |
Extraction of gait information and authentication by an infrared sensor network Shumpei Tsubakino, Mineichi Kudo (Hokkaido Univ.) BioX2018-25 |
We have improved a system that distinguishes persons from their gait measured by a ceiling infrared sensor network. The ... [more] |
BioX2018-25 pp.33-38 |
BioX |
2018-10-12 09:30 |
Okinawa |
Nobumoto Ohama Memorial Hall |
On Gait-based Age and Health Age Atsuya Sakata, Hirofumi Nishikawa, Noriko Takemura, Yasushi Makihara, Daigo Muramatsu, Yasushi Yagi (Osaka Univ.) BioX2018-26 |
In this paper, we analyze the relation between the health age, that can be calculated from the health check result, and ... [more] |
BioX2018-26 pp.39-44 |
BioX |
2018-10-12 10:00 |
Okinawa |
Nobumoto Ohama Memorial Hall |
Evaluation for individuality of intermediate output from CNN-based gait classification network Kousuke Moriwaki, Daigo Muramatsu, Noriko Takemura, Yasushi Yagi (Osaka Univ.) BioX2018-27 |
In personal authentication using convolutional neural network, we often use attribute information besides input's person... [more] |
BioX2018-27 pp.45-49 |
PRMU, BioX |
2018-03-19 16:30 |
Tokyo |
|
A Study of Comparison of Smart Phone Devices on Gait Recognition Using Devices Accelerometer Yizuou Chen, Yuji Watanabe (Nagoya City Univ.) BioX2017-70 PRMU2017-206 |
In this study, we compare gait recognition by some smart phone devices. We first collect 3-axes acceleration data for 19... [more] |
BioX2017-70 PRMU2017-206 pp.201-206 |
BioX |
2017-10-12 15:35 |
Okinawa |
Nobumoto Ohama Memorial Hall |
Performance Evaluation of Gait Recognition by Metric Learning using Joint Intensity Histogram Yushiro Kashimoto, Daigo Muramatsu, Yasushi Makihara, Yasushi Yagi (Osaka Univ.) BioX2017-28 |
We evaluate the performance of gait recognition algorithm using metric learning based on log-likelihood ratio of joint i... [more] |
BioX2017-28 pp.17-22 |
PRMU, SP |
2017-06-22 13:00 |
Miyagi |
|
Toward Description of Gaits by Onomatopoeia Based on the Relationship between Phoneme and Body-Parts Movement Hirotaka Kato, Takatsugu Hirayama, Yasutomo Kawanishi (Nagoya Univ.), Keisuke Doman (Chukyo Univ.), Ichiro Ide, Daisuke Deguchi, Hiroshi Murase (Nagoya Univ.) PRMU2017-25 SP2017-1 |
Gaits are expressed by various onomatopoeias according to their appearance. It is said that onomatopoeia has sound-symbo... [more] |
PRMU2017-25 SP2017-1 pp.1-6 |
PRMU, BioX |
2017-03-20 10:00 |
Aichi |
|
Robust Gait Recognition for Carrying-Status by SVM-based Metric Learning using Joint Intensity Histogram Atsuyuki Suzuki, Daigo Muramatsu, Yasushi Makihara, Yasushi Yagi (Osaka Univ.) BioX2016-37 PRMU2016-200 |
This paper describes a method of joint intensity metric learning to improve the robustness of gait recognition under car... [more] |
BioX2016-37 PRMU2016-200 pp.23-28 |
PRMU, BioX |
2017-03-20 15:20 |
Aichi |
|
The OU-ISIR MVLP: Multi-view Large Population Gait Database and Its Performance Evaluation Noriko Takemura, Yasushi Makihara, Daigo Muramatsu (Osaka Univ.), Tomio Echigo (OECU), Yasushi Yagi (Osaka Univ.) BioX2016-46 PRMU2016-209 |
This paper describes the world’s largest wide view variation gait database, the OU-ISIR Multi-View Large Population (MVL... [more] |
BioX2016-46 PRMU2016-209 pp.81-86 |
CAS, ICTSSL |
2017-01-27 11:10 |
Tokyo |
Kikai-Shinko-Kaikan Bldg. |
Group-Theoretic Consideration of Quadrupedal Locomotion Rhythms Generated by Hardware CPG Models Yoshinobu Maeda, Naruki Sasagawa, Satoshi Oyake (Niigata Univ.) CAS2016-103 ICTSSL2016-57 |
We have reproduced several quadrupedal locomotion rhythms on a hard-wired electronic circuit model by mimicking neural n... [more] |
CAS2016-103 ICTSSL2016-57 pp.121-126 |
BioX |
2016-08-18 16:40 |
Miyagi |
|
Effect of Feature Selection for Gait Recognition on 5 Walking States Using Android Device Yuji Watanabe, Sara San (Nagoya City Univ.) BioX2016-12 |
In our previous study, we collected gait data using an Android application when 8 subjects walked in the following 5 wal... [more] |
BioX2016-12 pp.27-32 |
PRMU, BioX |
2016-03-25 10:15 |
Tokyo |
|
Cross-view Gait Recognition using Convolutional Neural Network Kohei Shiraga, Yasushi Makihara, Daigo Muramatsu (Osaka Univ.), Tomio Echigo (Osaka Electro-Communication Univ.), Yasushi Yagi (Osaka Univ.) BioX2015-57 PRMU2015-180 |
We propose a robust cross-view gait recognition method employing a convolutional neural network (CNN) in this paper. We ... [more] |
BioX2015-57 PRMU2015-180 pp.87-92 |
MSS, CAS, IPSJ-AL [detail] |
2015-11-20 11:50 |
Kagoshima |
Ibusuki CityHall |
Reproduction of switching phenomena between quadrupedal locomotion patterns by using electronic circuit CPG network Akihiro Maruyama, Satoshi Oyake, Naruki Sasagawa (Niigata Univ.), Tomoyasu Ichimura (GNCT), Yoshinobu Maeda (Niigata Univ.) CAS2015-48 MSS2015-22 |
It is thought that central pattern generators (CPGs) hypothesized in the spinal cord should account for the reproduction... [more] |
CAS2015-48 MSS2015-22 pp.25-30 |
BioX |
2015-08-24 15:10 |
Hokkaido |
Otaru Port Marina |
A Study on Mutual Subspace Method Applied in Gait Recognition Yumi Iwashita (Kyushu Univ.), Hitoshi Sakano (NTT Data Corp.), Ryo Kurazume (Kyushu Univ.) BioX2015-15 |
We previously proposed a gait-based person recognition method using a mutual subspace method (MSM), with an assumption t... [more] |
BioX2015-15 pp.11-14 |
HCGSYMPO (2nd) |
2014-12-17 - 2014-12-19 |
Yamaguchi |
Kaikyo Messe Shimonoseki |
Estimation of foot position during swing phase for shoe-type gait measurement device Hiraku Edayoshi, Widodo Romy Budhi, Chikamune Wada (Kyutech) |
We have been developing a shoe-type gait measurement device that could estimate foot position while walking for walking ... [more] |
|
WIT |
2014-10-19 16:50 |
Kumamoto |
|
Basic study on the development of system for improving walking function by electrical stimulation Kazuhiro Funaki, Masuhiro Nitta, Yoshihiko Tagawa (KIT) WIT2014-46 |
We studied an assist system for improving gait by electrical stimulation based on the recognition result of a neural net... [more] |
WIT2014-46 pp.39-42 |
CAS |
2014-02-06 15:20 |
Kanagawa |
Nippon Maru Training center |
Reproduction of four-leg animal gaits using an asymmetric hardware CPG model Genichiro Ishigoka, Akihiro Maruyama, Hayate Kojima (Niigata Univ.), Tomoyasu Ichimura (ONCT), Yoshinobu Maeda (Niigata Univ.) CAS2013-80 |
In this technical report, we proposed a hardware model of the central pattern generator, or CPG, which controlled the ga... [more] |
CAS2013-80 pp.41-44 |
WIT |
2013-10-27 10:15 |
Kagoshima |
|
Development of assist system for improving gait by electrical stimulation using wireless sensors Kazuhiro Funaki, Ryuya Kakimoto, Masuhiro Nitta, Yoshihiko Tagawa (Kyushu Inst. of Tech.) WIT2013-57 |
We studied an assist system for improving gait by electrical stimulation based on the recognition result of a neural net... [more] |
WIT2013-57 pp.77-81 |
NC, MBE |
2013-09-24 10:55 |
Niigata |
Niigata University Ekinan-Campus "TOKIMATE" |
Four-leg gait simulation research using CPG network Hayate Kojima (Niigata Univ.), Tomoyasu Ichimura (ONCT), Akira Tsukada (TNCT), Taishin Nomura (Osaka Univ.), Yoshinobu Maeda (Niigata Univ.) MBE2013-39 NC2013-25 |
In the mammalian spinal cord, a central pattern generator (CPG) is supposed to be as neural networks. The CPG is respons... [more] |
MBE2013-39 NC2013-25 pp.17-22 |
NLP |
2013-07-08 13:25 |
Okinawa |
Miyako Island Marine Terminal |
Stable Gait Generation for the Compass-type Biped Robot based on a Fusion of Discrete Mechanics and Nonlinear Optimization Tatsuya Kai (Tokyo Univ. of Science) NLP2013-31 |
This paper develops a new stable gait generation method based on discrete mechanics and nonlinear optimization for the c... [more] |
NLP2013-31 pp.25-30 |