Directory of Researchers

TAMEI Tomoya
Center for Mathematical and Data Sciences
Associate Professor
Mechanical Engineering
Last Updated :2022/04/15

Researcher Profile and Settings

Affiliation

  • <Faculty / Graduate School / Others>

    Center for Mathematical and Data Sciences
  • <Related Faculty / Graduate School / Others>

    Faculty of Engineering / Department of Electrical and Electronic Engineering, Graduate School of Engineering / Department of Electrical and Electronic Engineering

Teaching

Research Activities

Research Areas

  • Informatics / Intelligent informatics

Awards

  • Oct. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), ICROS Award for IROS2015 Best Application Paper Award

    Nishanth Koganti, Jimson Ngeo, Tomoya Tamei, Kazushi Ikeda, Tomohiro Shibata

  • Sep. 2015 平成27年度日本神経回路学会論文賞

    大林 千尋, 為井 智也, 柴田 智広

Published Papers

  • Ryoto Takeuchi, Tomoya Tamei

    IEEE, 2020, IEEE Conference Proceedings, 2020 (SMC), 3735 - 3739

    [Refereed]

    International conference proceedings

  • Bryan Lao, Tomoya Tamei, Kazushi Ikeda

    2020, Frontiers Comput. Sci., 2, 3 - 3

    [Refereed]

    Scientific journal

  • Gaussian Process Latent Space Policies for Data-efficient Learning of Robotic Clothing

    Nishanth Koganti Tomohiro, Shibata Tomoya, Tamei Kazushi Ikeda

    2019, Advanced Robotics, 33 (1), 1 - 15

    [Refereed]

  • Bryan Lao, Tomoya Tamei, Kazushi Ikeda

    Understanding the contributions of therapist skill during intervention is essential for improving existing rehabilitation methodologies. This study aims to characterize therapist intervention on an important activity of daily living, the sit-to-stand motion. Using the concept of muscle synergy, we quantify and compare naturally-occurring standing strategies with those induced by a physical therapist. In this paper, we show that natural standing strategies are not shared among healthy subjects. However, each subject retains their own set of strategies. Moreover, the results suggest that a therapist does not introduce new strategies during therapy, but rather modulates the existing strategies of the individuals. Using such a low-dimensional representation of standing behavior allows for development of low-cost tools for wider distribution.

    IEEE, 2019, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2019 (EMBC), 2311 - 2315, English, International magazine

    [Refereed]

    International conference proceedings

  • Nishanth Koganti, Tomohiro Shibata, Tomoya Tamei, Kazushi Ikeda

    2019, Adv. Robotics, 33 (15-16), 800 - 814

    [Refereed]

    Scientific journal

  • Imparting Motor-Skills to Humanoid Robots Using Bayesian Nonparametric Latent Spaces

    Nishanth Koganti, Tomoya Tamei, Kazushi Ikeda, Tomohiro Shibata

    2017, Minisymposium on Implementation of Information Technologies for Biomedical Engineering, IEEE/EMBS International Conference on Engineering in Medicine and Biology

  • Nishanth Koganti, Tomoya Tamei, Kazushi Ikeda, Tomohiro Shibata

    2017, IEEE Trans. Robotics, 33 (4), 916 - 931

    [Refereed]

    Scientific journal

  • Felix Orlando Maria Joseph, Laxmidhar Behera, Tomoya Tamei, Tomohiro Shibata, Ashish Dutta, Anupam Saxena

    2017, Robotica, 35 (10), 1992 - 2017

    [Refereed]

    Scientific journal

  • Bayesian nonparametric motor-skill representations for robotic clothing assistance

    Nishanth Koganti, Tomoya Tamei, Kazushi Ikeda, Tomohiro Shibata

    2016, Workshop on Practical Bayesian Nonparametrics, Neural Information Processing Systems 2016, English

  • Bryan Lao, Tomoya Tamei, Kazushi Ikeda

    Understanding effective sit-to-stand (STS) movement is essential for improving rehabilitation strategies and developing services for the rapidly increasing number of elderly people. This study aims at identifying effective STS therapy by analyzing the kinematic synergies of movements induced by therapists of different skill-levels. Three synergies were found to share the same temporal pattern in both joint angles and center-of-mass spaces across all therapists. Effective strategy used by a skilled therapist and strategy flaws of less-experienced therapists were revealed through comparison of spatial patterns.

    IEEE, 2016, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2016, 6282 - 6285, English, International magazine

    [Refereed]

    International conference proceedings

  • Comparison of an Expert and Non-Experts in Standing up Guidance

    Tomoya Tamei, Tomohiro Shibata, Kazushi Ikeda

    2015, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2015)

    [Refereed]

  • Kinect-based posturography for in-home rehabilitation of balance disorders

    Tomoya Tamei, Yasuyuki Orito, Hiroyuki Funaya, Kazushi Ikeda, Yohei Okada, Tomohiro Shibata

    2015, APSIPA Transactions on Signal and Information Processing, 4

    [Refereed]

  • In-home measurement system of user's motion and center of pressure

    Tamei, Tomoya, Orito, Yasuyuki, Ikeda, Kazushi, Shibata, Tomohiro

    2015, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015

  • Nishanth Koganti, Jimson Gelbolingo Ngeo, Tomoya Tamei, Kazushi Ikeda, Tomohiro Shibata

    IEEE, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), 3464 - 3469

    [Refereed]

    International conference proceedings

  • Jimson Ngeo, Tomoya Tamei, Kazushi Ikeda, Tomohiro Shibata

    Accurate proportional myoelectric control of the hand is important in replicating dexterous manipulation in robot prostheses and orthoses. However, this is still difficult to achieve due to the complex and high degree-of-freedom (DOF) nature present in the governing musculoskeletal system. To address this problem, we suggest using a low dimensional encoding based on nonlinear synergies to represent both the high-DOF finger joint kinematics and the coordination of muscle activities taken from surface electromyographic (EMG) signals. Generating smooth multi-finger movements using EMG inputs is then done by using a shared Gaussian Process latent variable model that learns a dynamical model between both the kinematic and EMG data represented in a shared latent space. The experimental results show that the method is able to synthesize continuous movements of a full five-finger hand model, with total dimensions as large as 69 (although highly redundant and correlated). Finally, by comparing the estimation performances when the number of EMG latent dimensions are varied, we show that these synergistic features can capture the variance, shared and specific to the observed kinematics.

    IEEE, 2015, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2015, 2095 - 2098, English, International magazine

    [Refereed]

    International conference proceedings

  • Tomoya Tamei, Yasuyuki Orito, Tomohiro Shibata, Kazushi Ikeda

    IEEE, 2015, Asia-Pacific Signal and Information Processing Association Annual Summit and Conference(APSIPA), 59th, 927 - 929, Japanese

    International conference proceedings

  • Jimson G Ngeo, Tomoya Tamei, Tomohiro Shibata

    BACKGROUND: Surface electromyography (EMG) signals are often used in many robot and rehabilitation applications because these reflect motor intentions of users very well. However, very few studies have focused on the accurate and proportional control of the human hand using EMG signals. Many have focused on discrete gesture classification and some have encountered inherent problems such as electro-mechanical delays (EMD). Here, we present a new method for estimating simultaneous and multiple finger kinematics from multi-channel surface EMG signals. METHOD: In this study, surface EMG signals from the forearm and finger kinematic data were extracted from ten able-bodied subjects while they were tasked to do individual and simultaneous multiple finger flexion and extension movements in free space. Instead of using traditional time-domain features of EMG, an EMG-to-Muscle Activation model that parameterizes EMD was used and shown to give better estimation performance. A fast feed forward artificial neural network (ANN) and a nonparametric Gaussian Process (GP) regressor were both used and evaluated to estimate complex finger kinematics, with the latter rarely used in the other related literature. RESULTS: The estimation accuracies, in terms of mean correlation coefficient, were 0.85 ± 0.07, 0.78 ± 0.06 and 0.73 ± 0.04 for the metacarpophalangeal (MCP), proximal interphalangeal (PIP) and the distal interphalangeal (DIP) finger joint DOFs, respectively. The mean root-mean-square error in each individual DOF ranged from 5 to 15%. We show that estimation improved using the proposed muscle activation inputs compared to other features, and that using GP regression gave better estimation results when using fewer training samples. CONCLUSION: The proposed method provides a viable means of capturing the general trend of finger movements and shows a good way of estimating finger joint kinematics using a muscle activation model that parameterizes EMD. The results from this study demonstrates a potential control strategy based on EMG that can be applied for simultaneous and continuous control of multiple DOF(s) devices such as robotic hand/finger prostheses or exoskeletons.

    14 Aug. 2014, Journal of neuroengineering and rehabilitation, 11, 122 - 122, English, International magazine

    [Refereed]

    Scientific journal

  • 前屈姿勢と側屈姿勢を合併したパーキンソン病患者に対する直流前庭電気刺激-電極極性が効果に与える影響の検討-

    岡田洋平, 岡田洋平, 喜多頼広, 喜多頼広, 中村潤二, 中村潤二, 柴田智広, 柴田智広, 船谷浩之, 折戸靖幸, 爲井智也, 池田和司, 和田佳郎, 形岡博史, 上野聡, 冷水誠, 冷水誠, 森岡周, 森岡周, 庄本康治

    2014, 運動障害, 24 (2)

    [Refereed]

  • Nishanth Koganti, Tomoya Tamei, Takamitsu Matsubara, Tomohiro Shibata

    IEEE, 2014, The 23rd IEEE International Symposium on Robot and Human Interactive Communication(RO-MAN), 2014-October (October), 124 - 129

    [Refereed]

    International conference proceedings

  • Jimson Ngeo, Tomoya Tamei, Tomohiro Shibata

    Surface electromyographic (EMG) signals have often been used in estimating upper and lower limb dynamics and kinematics for the purpose of controlling robotic devices such as robot prosthesis and finger exoskeletons. However, in estimating multiple and a high number of degrees-of-freedom (DOF) kinematics from EMG, output DOFs are usually estimated independently. In this study, we estimate finger joint kinematics from EMG signals using a multi-output convolved Gaussian Process (Multi-output Full GP) that considers dependencies between outputs. We show that estimation of finger joints from muscle activation inputs can be improved by using a regression model that considers inherent coupling or correlation within the hand and finger joints. We also provide a comparison of estimation performance between different regression methods, such as Artificial Neural Networks (ANN) which is used by many of the related studies. We show that using a multi-output GP gives improved estimation compared to multi-output ANN and even dedicated or independent regression models.

    IEEE, 2014, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2014, 3537 - 3540, English, International magazine

    [Refereed]

    International conference proceedings

  • Chihiro Obayashi, Tomoya Tamei, Tomohiro Shibata

    This paper proposes a novel robotic trainer for motor skill learning. It is user-adaptive inspired by the assist-as-needed principle well known in the field of physical therapy. Most previous studies in the field of the robotic assistance of motor skill learning have used predetermined desired trajectories, and it has not been examined intensively whether these trajectories were optimal for each user. Furthermore, the guidance hypothesis states that humans tend to rely too much on external assistive feedback, resulting in interference with the internal feedback necessary for motor skill learning. A few studies have proposed a system that adjusts its assistive strength according to the user's performance in order to prevent the user from relying too much on the robotic assistance. There are, however, problems in these studies, in that a physical model of the user's motor system is required, which is inherently difficult to construct. In this paper, we propose a framework for a robotic trainer that is user-adaptive and that neither requires a specific desired trajectory nor a physical model of the user's motor system, and we achieve this using model-free reinforcement learning. We chose dart-throwing as an example motor-learning task as it is one of the simplest throwing tasks, and its performance can easily be and quantitatively measured. Training experiments with novices, aiming at maximizing the score with the darts and minimizing the physical robotic assistance, demonstrate the feasibility and plausibility of the proposed framework.

    2014, Neural Networks, 53, 52 - 60, English, International magazine

    [Refereed]

    Scientific journal

  • Jimson Ngeo, Tomoya Tamei, Tomohiro Shibata, M. J. Felix Orlando, Laxmidhar Behera, Anupam Saxena, Ashish Dutta

    Patients suffering from loss of hand functions caused by stroke and other spinal cord injuries have driven a surge in the development of wearable assistive devices in recent years. In this paper, we present a system made up of a low-profile, optimally designed finger exoskeleton continuously controlled by a user's surface electromyographic (sEMG) signals. The mechanical design is based on an optimal four-bar linkage that can model the finger's irregular trajectory due to the finger's varying lengths and changing instantaneous center. The desired joint angle positions are given by the predictive output of an artificial neural network with an EMG-to-Muscle Activation model that parameterizes electromechanical delay (EMD). After confirming good prediction accuracy of multiple finger joint angles we evaluated an index finger exoskeleton by obtaining a subject's EMG signals from the left forearm and using the signal to actuate a finger on the right hand with the exoskeleton. Our results show that our sEMG-based control strategy worked well in controlling the exoskeleton, obtaining the intended positions of the device, and that the subject felt the appropriate motion support from the device.

    IEEE, 2013, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2013, 338 - 341, English, International magazine

    [Refereed]

    International conference proceedings

  • Nishanth Koganti, Tomoya Tamei, Takamitsu Matsubara, Tomohiro Shibata

    ACM, 2013, Advances In Robotics 2013(AIR), 36 - 6

    [Refereed]

    International conference proceedings

  • Felix Orlando Maria Joseph, Ashish Dutta, Anupam Saxena, Laxmidhar Behera, Tomoya Tamei, Tomohiro Shibata

    2013, Robotica, 31 (5), 797 - 809

    [Refereed]

    Scientific journal

  • Continuous Estimation of Finger Joint Angles Using Inputs from an EMG-to-Muscle Activation Model

    Jimson Ngeo, Tomoya Tamei, Tomohiro Shibata

    2012, IEICE Technical Report MEとバイオサイバネティクス研究会, 112 (232), 17 - 22

  • Jimson Ngeo, Tomoya Tamei, Tomohiro Shibata

    Prediction of dynamic hand finger movements has many clinical and engineering applications in the control of human interface devices such as those used in virtual reality control, robot prosthesis and rehabilitation aids. Surface electromyography (sEMG) signals have often been used in the mentioned applications because these reflect the motor intention of users very well. In this study, we present a method to estimate the finger joint angles of a hand from sEMG signals that considers electromechanical delay (EMD), which is inherent when EMG signals are captured alongside motion data. We use the muscle activation obtained from the sEMG signals as input to a neural network. In this muscle activation model, the EMD is parameterized and automatically obtained through optimization. With this method, we can predict the finger joint angles with sEMG signals in both periodic and nonperiodic free movements of the flexion and extension movement of the fingers. Our results show correlation as high as 0.92 between the actual and predicted metacarpophalangeal (MCP) joint angles for periodic finger flexion movements, and as high as 0.85 for nonperiodic movements, which are more dynamic and natural.

    IEEE, 2012, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2012, 2756 - 2759, English, International magazine

    [Refereed]

    International conference proceedings

  • Tomoya Tamei, Takamitsu Matsubara, Akshara Rai, Tomohiro Shibata

    IEEE, 2011, 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011)(Humanoids), 733 - 738

    [Refereed]

    International conference proceedings

  • Tomoya Tamei, Chihiro Obayashi, Tomohiro Shibata

    Acquiring the skillful movements of experts is a difficult task in many fields. If we find quantitative indices of skillful movement, we can develop an adaptive training system using the indices. We focused on throwing darts in our previous study. It was found that optimization criteria of sum of squared joint torque changes over time was negatively correlated with subject's scores, suggesting that the experts optimally controlled the shoulder elevations and rotation around the elbow joint in terms of dynamics. In this study, we investigate the relationship between the skill level of subjects and their utilization joint torque components such as the muscular torque, interaction torque and gravity torque. It is shown found that the sum of squared joint torque components of the subjects correlates with their scores, suggesting that the subjects who can take higher scores utilize the interaction torque of the elbow joint without shoulder displacement.

    IEEE, 2011, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2011, 1283 - 1286, English, International magazine

    [Refereed]

    International conference proceedings

  • Tomoya Tamei, Tomohiro Shibata

    2011, Adv. Robotics, 25 (5), 563 - 580

    [Refereed]

    Scientific journal

  • Development of an Adaptive Robotic Trainer: Application to Darts Throwing

    Obayashi, C, Tamei, T, Shibata, T

    2010, Proceedings of the 1st International Conference on Applied Bionics and Biomechanics (ICABB2010)

    [Refereed]

  • Tamei, Tomoya, Shibata, Tomohiro

    2010, 2010 5th International Conference on System of Systems Engineering, SoSE 2010, 1 - 6

    [Refereed]

  • ダーツ投擲動作における熟達者と非熟達者の比較

    大林 千尋, 為井 智也, 柴田 智広, 池田 和司

    2009, 第24 回生体・生理工学シンポジウム論文集(BPES 2009), 295 (298)

  • Chihiro Obayashi, Tomoya Tamei, Akira Imai, Tomohiro Shibata

    Acquiring skillful movements of experts is a difficult task in many fields. Since non-experts often fail to find out how to improve their skill, it is desirable to find quantitative indices of skillful movements that clarify the difference between experts and non-experts. If we find quantitative indices, we can develop an adaptive training system using the indices. In this study, we quantitatively compare dart-throwing movements between experts and non-experts based on their scores, motions, and EMG signals. First, we show that the variance of upper-limb motion trajectories of the experts is significantly smaller than that of the non-experts. Then, we show that the displacement and the variance of the shoulder of the experts are also significantly smaller than those of the non-experts. The final result is the highlight of this study. We investigated their upper-limb motions from the viewpoint of trajectory optimization. In this study, we focus on two popular optimization criteria, i.e., sum of squared jerk over a trajectory and sum of squared joint-torque change over a trajectory. We present that the sum of squared joint torques of the subjects was negatively correlated with their scores (p < 0.05), whereas the other criteria were not.

    2009, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2009, 2647 - 50, English, International magazine

    [Refereed]

    Scientific journal

  • Tamei, T, Ishii, S, Shibata, T

    Informa UK Limited, 2008, Advanced Robotics, 8 (22), 893 - 922

    [Refereed]

    Scientific journal

  • Policy Gradient Learning of Cooperative Interaction with a Robot Using User’s Biological Signals

    Tomoya Tamei, Tomohiro Shibata

    2008, 15th International Conference on Neural Information Processing (ICONIP 2008)

  • Dynamic and cooperative interaction with a robot that possesses no force/tactile sensors

    Tamei, T, Ishii, S, Shibata, T

    2007, International Conference on Advanced Robotics (ICAR 2007), 647 - 652

    [Refereed]

  • Development of learning support system for piano-keying- Relationship between the activity of finger muscles and key-release velocities of an expert -

    Tamei, T, Shibata, T, Ishii, S

    2006, The Eleventh International Symposium on Artificial Life and Robotics

  • 筋電信号に基づいた示指によるピアノ打鍵時の脱力度評価

    為井 智也, 柴田 智広, 石井 信

    2005, 情報処理学会技術研究報告, vol.2005-MUS-61, 47 (52)

  • Extended force/tactile senses of machines by measurement of user's biological signals

    Nomura, T, Shibata, T, Tamei, T, Ishii, S

    2005, 36th International Symposium on Robotics

MISC

  • 運動課題における言語インストラクションの自動生成と意味関係の抽出

    竹内亮人, 為井智也

    2020, 日本ロボット学会学術講演会予稿集(CD-ROM), 38th

  • Efficient Privacy-Preserving Prediction for Three-Layer Feedforward Neural Networks Using Ring-LWE-based Homomorphic Encryption

    手塚雄大, WANG Lihua, WANG Lihua, 林卓也, KIM Sangwook, 為井智也, 大森敏明, 小澤誠一

    2019, 人工知能学会全国大会(Web), 33rd

  • 重力感受性増強装置(TPAD)を用いたゴルフパターのトレーニング法の開発

    阿久根康平, 為井智也, 和田佳郎, 和田佳郎, 塩崎智之, 山中敏彰, 山中敏彰, 北原糺, 北原糺

    2019, Equilibrium Research, 78 (5)

  • ゴルフスイングフォームの熟達者・非熟達者比較-グラフ理論を用いた身体部位間協調の可視化-

    為井智也, 和田佳郎, 和田佳郎

    2017, Equilibrium Research, 76 (5)

  • 重力感受性に注目したゴルフトレーニング法の開発:7番アイアンスイング中における頭部安定化

    和田佳郎, 和田佳郎, 宇野春日, 植田駿, 伊藤妙子, 村井孝行, 乾洋史, 乾洋史, 山中敏彰, 山中敏彰, 北原糺, 北原糺, 爲井智也

    2017, Equilibrium Research, 76 (5)

  • D-7-15 Paired changes in electromechanical delay and proficiency in motor task

    Shigasak Yuya, Tamei Tomoya, Ikeda Kazushi

    The Institute of Electronics, Information and Communication Engineers, 01 Mar. 2016, Proceedings of the IEICE General Conference, 2016 (1), 93 - 93, Japanese

  • 二者の相互作用による知覚傾向の収束:心理物理的技法によるSherif実験再訪

    黒田起吏, 為井 智也, 池田 和司, 亀田達也

    2016, 日本社会心理学会第57回大会

  • 二者の相互作用による知覚傾向の収束:心理物理的技法によるSherif実験再訪

    黒田起吏, 為井 智也, 池田 和司, 亀田達也

    2016, 第9回日本人間行動進化学会

  • Motor-skill Learning in Latent Spaces for Robotic Clothing Assistance

    KOGANTI Nishanth, KOGANTI Nishanth, JOSHI Ravi P., TAMEI Tomoya, IKEDA Kazushi, SHIBATA Tomohiro

    2016, 日本ロボット学会学術講演会予稿集(CD-ROM), 34th

  • バレエダンサーと行った簡易型モーションキャプチャシステムを用いた姿勢制御研究

    和田佳郎, 和田佳郎, 辻本憲広, 山中敏彰, 村井孝行, 北原糺, 爲井智也, 柴田智広

    2016, 日本耳鼻咽喉科学会会報, 119 (4)

  • 集合知の発生条件を探る: 共通の反応関数の形成

    黒田起吏, 為井 智也, 池田 和司, 小川昭利, 亀田達也

    2015, 第19回実験社会科学カンファレンス

  • モデルパラメータ同定に基づくバランス能力の評価

    折戸靖幸, 為井 智也, 柴田 智広, 池田 和司

    2014, SICE-SSI2014

  • Baxterを用いた生活機能支援ロボティクスの教育研究

    柴田 智広, Koganti Nishanth, 為井 智也, 松原 崇充, 池田 和司

    2014, 第15回 計測自動制御学会 システムインテグレーション部門講演会 SI2014

  • Development of Low-cost and Accurate Posturography Using Kinect for In-home Rehabilitation of Balance Disorders

    Yasuyuki Orito, Hiroyuki Funaya, Tomoya Tamei, Tomohiro Shibata, Kazushi Ikeda

    2014, The 19th International Symposium on Artificial Life and Robotics 2014 (AROB 19th '14), 185 - 188

  • Dynamical Modelling of Clothing Materials using GP-LVM for Robotic Clothing Assistance

    Nishanth Koganti, Tomoya Tamei, Tomohiro Shibata

    2014, 第32回日本ロボット学会学術講演会

  • Assessment of Kinect SDK’s Skeleton Joints - Comparison with Joint Centers in Biomechanical Model

    Tomoya Tamei, Hiroyuki Funaya, Kazushi Ikeda, Tomohiro Shibata

    2014, IEEE Healthcare Innovation & Point-of-Care Technologies (HI-POCT 2014)

  • Motion Tracking of a Subject Lying on a Bed Using RGB-D Sensor,” IEEE Healthcare Innovation & Point-of-Care Technologies (HI-POCT 2014)

    Tomoya Tamei, Krati Saxena, Kazushi Ikeda, Tomohiro Shibata

    2014, IEEE Healthcare Innovation & Point-of-Care Technologies (HI-POCT 2014)

  • 前屈姿勢異常を呈するパーキンソン病患者におけるKinectを利用した在宅における姿勢評価,姿勢フィードバックトレーニングの試み-症例報告-

    岡田洋平, 岡田洋平, 柴田智広, 船谷浩之, 折戸靖幸, 爲井智也, 池田和司, 冷水誠, 冷水誠, 森岡周, 森岡周

    2014, 日本理学療法学術大会(Web), 49th

  • インステップキック運動における動的作業方向動作精度の解析

    泉直克, 為井 智也, 池田 和司, 柴田 智広

    2013, 電子情報通信学会総合大会

  • User-Adaptive Robotic Training Accelerates Learning of Darts Throwing

    Chihiro Obayashi, Tomoya Tamei, Tomohiro Shibata

    2013, Neuro2013

  • Continuous Estimation of Human-Cloth topological relationship using depth sensor for Robotic Clothing Assistance

    Nishanth Koganti, Tomoya Tamei, Takamitsu Matsubara, Tomohiro Shibata

    2013, 第31回日本ロボット学会学術講演会

  • Kinectを用いた姿勢制御研究の試み

    辻本憲広, 和田佳郎, 爲井智也, 柴田智広

    2013, Equilibrium Research, 72 (5)

  • 適応的なロボットによるヒトの運動学習や生活機能の支援

    柴田智広, 爲井智也, 大林千尋, 松原崇充

    2013, 日本生理人類学会誌, 18

  • Adaptive Robotic Clothing Assistance through Reinforcement Learning with Time-varying Synergies Model

    Tomoya Tamei, Takamitsu Matsubara, Tomohiro Shibata

    2012, 第30回日本ロボット学会学術講演会

  • Hybrid control of a three finger hand exoskeleton based on EMG and inverse kinematics model.

    M. Felix Orlando, Ashish Dutta, Anupam Saxena, Laxmidhar Behera, Tomohiro Shibata, Tomoya Tamei

    2012, 第30回日本ロボット学会学術講演会

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    産業開発機構, 2012, 映像情報Industrial, 44 (2), 7 - 12, Japanese

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    2011, 第29回日本ロボット学会学術講演会

  • Development of Adaptive Robotic Trainer for Novices of Darts Throwing

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    2011, 第29回日本ロボット学会学術講演会

  • Adaptive Robotic Training for Darts based on Comparisons of Experts and Non-experts

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    2010, 第28回日本ロボット学会学術講演会

  • ユーザーの生体情報を用いたロボットとの力覚的協調の強化学習

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    2009, 電子情報通信学会 2009年総合大会講演論文集

  • Virtual Force/Tactile Sensors for Dynamic and Cooperative Interaction with a Robot Using User's Biological Signals

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    2007, Neuro 2007