研究者紹介システム

爲井 智也
タメイ トモヤ
数理・データサイエンスセンター
准教授
機械工学関係
Last Updated :2022/01/10

研究者情報

所属

  • 【主配置】

    数理・データサイエンスセンター
  • 【配置】

    工学部 電気電子工学科, 大学院工学研究科 電気電子工学専攻

学位

  • 博士(工学), 奈良先端科学技術大学院大学
  • AI×リハビリ・運動学習支援

授業科目

研究活動

研究分野

  • 情報通信 / 知能情報学

受賞

  • 2015年10月 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

  • 2015年09月 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

論文

  • Ryoto Takeuchi, Tomoya Tamei

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

    [査読有り]

    研究論文(国際会議プロシーディングス)

  • Bryan Lao, Tomoya Tamei, Kazushi Ikeda

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

    [査読有り]

    研究論文(学術雑誌)

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

    Nishanth Koganti, ShibataTomohiro, Tamei Tomoya, Ikeda Kazushi

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

    [査読有り]

  • 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.

    2019年, 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, 英語, 国際誌

    [査読有り]

    研究論文(国際会議プロシーディングス)

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

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

    [査読有り]

    研究論文(学術雑誌)

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

    Nishanth Koganti, Tomoya Tamei, Kazushi Ikeda, Tomohiro Shibata

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

    [査読有り]

    研究論文(学術雑誌)

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

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

    [査読有り]

    研究論文(学術雑誌)

  • Bayesian nonparametric motor-skill representations for robotic clothing assistance

    Nishanth Koganti, Tomoya Tamei, Kazushi Ikeda, Tomohiro Shibata

    2016年, Workshop on Practical Bayesian Nonparametrics, Neural Information Processing Systems 2016, 英語

  • 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.

    2016年, 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, 英語, 国際誌

    [査読有り]

    研究論文(国際会議プロシーディングス)

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

    Tomoya Tamei, Tomohiro Shibata, Kazushi Ikeda

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

    [査読有り]

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

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

    2015年, APSIPA Transactions on Signal and Information Processing, 4

    [査読有り]

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

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

    2015年, 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

    2015年, 3464 - 3469

    [査読有り]

    研究論文(国際会議プロシーディングス)

  • 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.

    2015年, 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, 英語, 国際誌

    [査読有り]

    研究論文(国際会議プロシーディングス)

  • 為井智也, 折戸靖幸, 柴田智広, 池田和司

    システム制御情報学会, 2015年, システム制御情報学会研究発表講演会講演論文集(CD-ROM), 59th, 927 - 929, 日本語

    研究論文(国際会議プロシーディングス)

  • 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.

    2014年08月14日, Journal of neuroengineering and rehabilitation, 11, 122 - 122, 英語, 国際誌

    [査読有り]

    研究論文(学術雑誌)

  • 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年, 運動障害, 24 (2)

    [査読有り]

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

    2014年, 2014-October (October), 124 - 129

    [査読有り]

    研究論文(国際会議プロシーディングス)

  • 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.

    2014年, 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, 英語, 国際誌

    [査読有り]

    研究論文(国際会議プロシーディングス)

  • 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年, Neural Networks, 53, 52 - 60, 英語, 国際誌

    [査読有り]

    研究論文(学術雑誌)

  • 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.

    2013年, 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, 英語, 国際誌

    [査読有り]

    研究論文(国際会議プロシーディングス)

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

    2013年, 36 - 6

    [査読有り]

    研究論文(国際会議プロシーディングス)

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

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

    [査読有り]

    研究論文(学術雑誌)

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

    Jimson Ngeo, Tomoya Tamei, Tomohiro Shibata

    2012年, 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.

    2012年, 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, 英語, 国際誌

    [査読有り]

    研究論文(国際会議プロシーディングス)

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

    2011年, 733 - 738

    [査読有り]

    研究論文(国際会議プロシーディングス)

  • 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.

    2011年, 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, 英語, 国際誌

    [査読有り]

    研究論文(国際会議プロシーディングス)

  • Tomoya Tamei, Tomohiro Shibata

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

    [査読有り]

    研究論文(学術雑誌)

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

    Obayashi, C, Tamei, T, Shibata, T

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

    [査読有り]

  • Tamei, Tomoya, Shibata, Tomohiro

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

    [査読有り]

  • 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年, 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年, 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, 英語, 国際誌

    [査読有り]

    研究論文(学術雑誌)

  • Virtual force/tactile sensors for interactive machines using user's biological signals

    Tamei, T, Ishii, S, Shibata, T

    2008年, Advanced Robotics, 8 (22), 893 - 922

    [査読有り]

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

    Tomoya Tamei, Tomohiro Shibata

    2008年, 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年, International Conference on Advanced Robotics (ICAR 2007), 647 - 652

    [査読有り]

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

  • 三層ニューラルネットワークにおけるRing-LWEベース準同型暗号を用いた効率的なプライバシー保護推論処理

    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年, 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年, 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年, 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年, Equilibrium Research, 76 (5)

  • 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年, 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年, 第9回日本人間行動進化学会

  • ロボット着衣支援のための潜在空間におけるモータースキル学習

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

    2016年, 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年, 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年, 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年, 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年, 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年, 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年, 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年, 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年, 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-

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