研究者詳細 - 古川　淳一朗

写真a

フルカワ　ジュンイチロウ

古川　淳一朗

所属

システム工学部情報学領域

職名

講師

外部リンク

学歴

-

2016年

大阪大学大学院生命機能研究科博士後期課程修了（工学博士）
-

2013年

大阪大学大学院生命機能研究科博士前期課程修了 Graduate School of Frontier Biosciences
-

2011年

大阪大学基礎工学部卒 School of Engineering Science Direct Affiliates

学位

博士（工学） 2016年

経歴

2025年06月

-

継続中

和歌山大学システム工学部, 講師
2021年04月

-

2025年05月

特定国立研究開発法人理化学研究所情報統合本部ガーディアンロボットプロジェクト研究員
2020年05月

-

継続中

株式会社国際電気通信基礎技術研究所, 脳情報通信総合研究所, 連携研究員
2020年05月

-

2021年03月

特定国立研究開発法人理化学研究所バトンゾーン研究推進プログラムロボティクスプロジェクト研究員
2016年04月

-

2020年04月

株式会社国際電気通信基礎技術研究所脳情報通信総合研究所専任研究員

所属学協会

日本ロボット学会
IEEE

研究分野

情報通信 / 知能ロボティクス

論文

Transformer-based multitask assist control from first-person view image and user’s kinematic information for exoskeleton robots

Jun-ichiro Furukawa, Jun Morimoto (担当区分：筆頭著者,　責任著者 )

npj robotics 3 ( 13 ) 2025年06月 [査読有り]

DOI
Integrated Motion State Prediction for Sit-To-Stand and Stand-To-Sit Motions Toward Effective Power Assist Control

Kai Ren, Yuichi Nakamura, Kazuaki Kondo, Kei Shimonishi, Takahide Ito, Jun-ichiro Furukawa, Qi An

In proceedings (In press) - IEEE International Conference on Robotics and Automation (ICRA) 2025年05月 [査読有り]
Time Series Prediction of Sit-to-Stand Muscle Synergy Using Deep Learning

Julian Ilham, Yuichi Nakamura, Takahide Ito, Kazuaki Kondo, Jun-ichiro Furukawa, Qi An, Kei Shimonishi

In proceedings - IEEE/SICE International Symposium on System Integration (SII) ( IEEE ) 37 - 42 2025年01月 [査読有り]

DOI
Proprioceptive short-term memory in passive motor learning

Shinya Chiyohara, Jun-ichiro Furukawa, Tomoyuki Noda, Jun Morimoto, Hiroshi Imamizu

Scientific Reports ( Springer Science and Business Media LLC ) 13 ( 1 ) 2023年11月 [査読有り]

　概要を見る

Abstract

A physical trainer often physically guides a learner’s limbs to teach an ideal movement, giving the learner proprioceptive information about the movement to be reproduced later. This instruction requires the learner to perceive kinesthetic information and store the instructed information temporarily. Therefore, (1) proprioceptive acuity to accurately perceive the taught kinesthetics and (2) short-term memory to store the perceived information are two critical functions for reproducing the taught movement. While the importance of proprioceptive acuity and short-term memory has been suggested for active motor learning, little is known about passive motor learning. Twenty-one healthy adults (mean age 25.6 years, range 19–38 years) participated in this study to investigate whether individual learning efficiency in passively guided learning is related to these two functions. Consequently, learning efficiency was significantly associated with short-term memory capacity. In particular, individuals who could recall older sensory stimuli showed better learning efficiency. However, no significant relationship was observed between learning efficiency and proprioceptive acuity. A causal graph model found a direct influence of memory on learning and an indirect effect of proprioceptive acuity on learning via memory. Our findings suggest the importance of a learner’s short-term memory for effective passive motor learning.

DOI
Development of split-force-controlled body weight support (SF-BWS) robot for gait rehabilitation.

Asuka Takai, Tatsuya Teramae, Tomoyuki Noda, Koji Ishihara, Jun-ichiro Furukawa, Hiroaki Fujimoto, Megumi Hatakenaka, Nobukazu Fujita, Akihiro Jino, Yuichi Hiramatsu, Ichiro Miyai, Jun Morimoto

Frontiers in human neuroscience 17 1197380 - 1197380 2023年07月 [査読有り]

　概要を見る

This study introduces a body-weight-support (BWS) robot actuated by two pneumatic artificial muscles (PAMs). Conventional BWS devices typically use springs or a single actuator, whereas our robot has a split force-controlled BWS (SF-BWS), in which two force-controlled actuators independently support the left and right sides of the user's body. To reduce the experience of weight, vertical unweighting support forces are transferred directly to the user's left and right hips through a newly designed harness with an open space around the shoulder and upper chest area to allow freedom of movement. A motion capture evaluation with three healthy participants confirmed that the proposed harness does not impede upper-body motion during laterally identical force-controlled partial BWS walking, which is quantitatively similar to natural walking. To evaluate our SF-BWS robot, we performed a force-tracking and split-force control task using different simulated load weight setups (40, 50, and 60 kg masses). The split-force control task, providing independent force references to each PAM and conducted with a 60 kg mass and a test bench, demonstrates that our SF-BWS robot is capable of shifting human body weight in the mediolateral direction. The SF-BWS robot successfully controlled the two PAMs to generate the desired vertical support forces.

DOI
Muscle Synergy Analysis Under Fast Sit-to-stand Assist : A Preliminary Study

Takahide Ito, Jun-ichiro Furukawa, Qi An, Jun Morimoto, Yuichi Nakamura

In proceedings - Augmented Humans Conference ( ACM ) 2023年03月 [査読有り]

DOI
Development of a Chair to Support Human Standing Motion -Seat movement mechanism using zip chain actuator-

Yamato Kuroda, Qi An, Hiroshi Yamakawa, Shingo Shimoda, Jun-ichiro Furukawa, Jun Morimoto, Yuichi Nakamura, Ryo Kurazume

In proceedings - IEEE/SICE International Symposium on System Integration (SII) 555 - 560 2022年01月 [査読有り]

DOI
Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot

Jun-ichiro Furukawa, Shotaro Okajima, Qi An, Yuichi Nakamura, Jun Morimoto (担当区分：筆頭著者,　責任著者 )

IEEE Robotics and Automation Letters 7 ( 2 ) 3890 - 3897 2022年 [査読有り]
A Collaborative Filtering Approach Toward Plug-and-Play Myoelectric Robot Control

Jun-ichiro Furukawa, Shinya Chiyohara, Tatsuya Teramae, Asuka Takai, Jun Morimoto (担当区分：筆頭著者,　責任著者 )

IEEE Transactions on Human-Machine Systems 51 ( 5 ) 514 - 523 2021年 [査読有り]

DOI
Composing An Assistive Control Strategy based on Linear Bellman Combination from Estimated User's Motor Goal

Jun-ichiro Furukawa, Jun Morimoto (担当区分：筆頭著者,　責任著者 )

IEEE Robotics and Automation Letters ( Institute of Electrical and Electronics Engineers (IEEE) ) 6 ( 2 ) 1051 - 1058 2021年 [査読有り]

DOI
Passive training with upper extremity exoskeleton robot affects proprioceptive acuity and performance of motor learning.

Shinya Chiyohara, Jun-ichiro Furukawa, Tomoyuki Noda, Jun Morimoto, Hiroshi Imamizu

Scientific Reports 10 ( 1 ) 11820 - 11820 2020年07月 [査読有り]

　概要を見る

Sports trainers often grasp and move trainees' limbs to give instructions on desired movements, and a merit of this passive training is the transferring of instructions via proprioceptive information. However, it remains unclear how passive training affects the proprioceptive system and improves learning. This study examined changes in proprioceptive acuity due to passive training to understand the underlying mechanisms of upper extremity training. Participants passively learned a trajectory of elbow-joint movement as per the instructions of a single-arm upper extremity exoskeleton robot, and the performance of the target movement and proprioceptive acuity were assessed before and after the training. We found that passive training improved both the reproduction performance and proprioceptive acuity. We did not identify a significant transfer of the training effect across arms, suggesting that the learning effect is specific to the joint space. Furthermore, we found a significant improvement in learning performance in another type of movement involving the trained elbow joint. These results suggest that participants form a representation of the target movement in the joint space during the passive training, and intensive use of proprioception improves proprioceptive acuity.

DOI
Exploiting Human and Robot Muscle Synergies for Human-in-the-loop Optimization of EMG-based Assistive Strategies.

Masashi Hamaya, Takamitsu Matsubara, Jun-ichiro Furukawa, Yuting Sun, Satoshi Yagi, Tatsuya Teramae, Tomoyuki Noda, Jun Morimoto

In proceedings - IEEE International Conference on Robotics and Automation (ICRA) ( IEEE ) 549 - 555 2019年 [査読有り]

DOI
Shoulder Glenohumeral Elevation Estimation based on Upper Arm Orientation

Sara Hamdan, Erhan Oztop, Jun-ichiro Furukawa, Jun Morimoto, Barkan Ugurlu

In proceedings - Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS ( Institute of Electrical and Electronics Engineers Inc. ) 2018- 1481 - 1484 2018年10月 [査読有り]

　概要を見る

In this paper, the shoulder glenohumeral displacement during the movement of the upper arm is studied. Four modeling approaches were examined and compared to estimate the humeral head elevation (vertical displacement) and translation (horizontal displacement). A biomechanics-inspired method was used firstly to model the glenohumeral displacement in which a least squares method was implemented for parameter identification. Then, three Gaussian process regression models were used in which the following variable sets were employed: i) shoulder adduction/abduction angle, ii) combination of shoulder adduction/abduction and flexion/extension angles, iii) overall upper arm orientation in the form of quaternions. In order to test the respective performances of these four models, we collected motion capture data and compared the models' representative capabilities. As a result, Gaussian process regression that considered the overall upper arm orientation outperformed the other modeling approaches
however, it should be noted that the other methods also provided accuracy levels that may be sufficient depending on task requirements.

DOI
Development of Shoulder Exoskeleton Toward BMI Triggered Rehabilitation Robot Therapy.

Miho Ogura, Jun-ichiro Furukawa, Tatsuya Teramae, Tomoyuki Noda, Kohei Okuyama, Michiyuki Kawakami, Meigen Liu, Jun Morimoto

In proceedings - IEEE International Conference on Systems, Man, and Cybernetics (SMC) ( IEEE ) 1105 - 1109 2018年 [査読有り]

DOI
Human Movement Modeling to Detect Biosignal Sensor Failures for Myoelectric Assistive Robot Control

Jun-ichiro Furukawa, Tomoyuki Noda, Tatsuya Teramae, Jun Morimoto (担当区分：筆頭著者,　責任著者 )

IEEE Transactions on Robotics ( IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC ) 33 ( 4 ) 846 - 857 2017年08月 [査読有り]

　概要を見る

In this study, we propose a human movement model both for myoelectric assistive robot control and biosignal-sensor-failure detection. We particularly consider an application to upper extremity exoskeleton robot control. When using electromyography (EMG)-based assistive robot control, EMG electrodes can be easily disconnected or detached from skin surfaces because the human body is always in contact with the robot. If multiple electrodes are used to estimate multiple joint movements, the probability of sensor electrode misplacement increases due to human error. To cope with the aforementioned issues, we propose a novel human movement estimation model that takes anomalies into account as uncertain observations. We estimated human joint torques by automatically modulating the contribution of each sensor channel for the movement estimation based on anomaly scores that were computed according to synergistic muscular coordination. We compared our proposed method with conventional approaches during drinking-movement estimation with five healthy subjects in the three aforementioned anomaly situations and showed the effectiveness of our proposed method. We applied it to a four-DOF upper limb assistive exoskeleton robot and showed proper control in sensor failure situations.

DOI
Database-driven approach for Biosignal-based robot control with collaborative filtering.

Jun-ichiro Furukawa, Asuka Takai, Jun Morimoto (担当区分：筆頭著者,　責任著者 )

In proceedings - IEEE-RAS International Conference on Humanoid Robotics (Humanoids) ( IEEE ) 606 - 611 2017年 [査読有り]

DOI
An EMG-Driven Weight Support System With Pneumatic Artificial Muscles

Jun-ichiro Furukawa, Tomoyuki Noda, Tatsuya Teramae, Jun Morimoto (担当区分：筆頭著者,　責任著者 )

IEEE Systems Journal ( IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC ) 10 ( 3 ) 1026 - 1034 2016年09月 [査読有り]

　概要を見る

In this paper, we introduce our newly developed biosignal-based vertical weight support system that is composed of pneumatic artificial muscles (PAMs) and an electromyography (EMG) measurement device. By using our developed weight support system, assist force can be varied based on measured muscle activities; most existing systems can only generate constant assist forces. In this paper, we estimated knee and ankle joint torques from measured EMGs using floating base inverse dynamics. Knee and ankle joint estimated torques are converted to vertical forces by the kinematic model of a subject. The converted vertical forces are used as force inputs for the PAM actuator system. To validate our system's control performance, four healthy subjects performed a one-leg squat with his left leg while his right leg was assisted by our proposed system. We used the vertical force estimated from the measured EMG signals as a control input to the weight support system. We compared EMG magnitudes with four different experimental conditions: 1) normal two-leg squat; 2) one-leg squat without the assist system; 3) one-leg squat with EMG-based weight support; and 4) one-leg squat with constant force support. The EMG magnitude with the proposed weight support system was much closer to that with normal two-leg squat than that with one-leg squat without the assist system and than that with one-leg squat with constant force support.

DOI
Fault tolerant approach for biosignal-based robot control

Jun-ichiro Furukawa, Tomoyuki Noda, Tatsuya Teramae, Jun Morimoto (担当区分：筆頭著者,　責任著者 )

Advanced Robotics ( TAYLOR & FRANCIS LTD ) 29 ( 7 ) 505 - 514 2015年04月 [査読有り]

　概要を見る

This paper proposes a fault tolerant framework for biosignal-based robot control with multiple sensor electrodes. In this approach, to cope with sensor faults, a reliable joint torque estimation model is selected from a group of estimation models based on sensor failure classifiers. The correlation among the electromyography (EMG) signal streams is used as input feature vectors for fault detection. To validate our proposed method, we artificially disconnect an EMG electrode or detach one side of an EMG probe from the skin surface during elbow-joint torque estimation experiments with five participants. When one EMG sensor electrode experiences one of the problems, the experimental results show that the joint torque can be estimated with significantly fewer errors using our proposed approach than a joint torque estimation method without sensor fault detection or than a method with a conventional sensor fault detection algorithm. Furthermore, we controlled a mannequin-arm-attached one-DOF exoskeleton based on the estimated torque profiles by generating movements with the estimated torque derived from the selected model.

DOI
Estimating Joint Movements from Observed EMG Signals with Multiple Electrodes under Sensor Failure Situations toward Safe Assistive Robot Control

Jun-ichiro Furukawa, Tomoyuki Noda, Tatsuya Teramae, Jun Morimoto (担当区分：筆頭著者,　責任著者 )

In proceedings - IEEE International conference on robotics and automation (ICRA) ( IEEE COMPUTER SOC ) 4985 - 4991 2015年 [査読有り]

　概要を見る

In this paper, we propose an estimation method of human joint movements from measured EMG signals for assistive robot control. We focus on how to estimate joint movements using multiple EMG electrodes even under sensor failure situations. In real world applications, EMG sensor electrodes might become disconnected or detached from skin surfaces. If we consider EMG-based robot control for assistive robots, such sensor failures lead to significant errors in the estimation of user joint movements. To cope with these sensor failures, we propose a state estimation model that takes uncertain observations into account. Sensor channel anomalies are found by checking the covariance of the EMG signals measured by multiple EMG electrodes. To validate the proposed control framework, we artificially disconnect an EMG electrode or detach one side of an EMG probe from the skin surface during elbow joint movement estimation. We show proper control of a one-DOF exoskeleton robot based on the estimated joint torque using our proposed method even when one EMG electrode has a sensor problem; a standard method with no tolerability against uncertain observations was unable to deal with these fault situations. Furthermore, the errors of the estimated joint torque with our proposed method were smaller than the standard method or a method with a conventional sensor fault detection algorithm.

DOI
Brain-machine interfacing control of whole-body humanoid motion

Karim Bouyarmane, Joris Vaillan, Norikazu Sugimoto, François Keith, Jun-ichiro Furukawa, Jun Morimoto

Frontiers in Systems Neuroscience ( Frontiers Research Foundation ) 8 ( 00138 ) 2014年08月 [査読有り]

　概要を見る

We propose to tackle in this paper the problem of controlling whole-body humanoid robot behavior through non-invasive brain-machine interfacing (BMI), motivated by the perspective of mapping human motor control strategies to human-like mechanical avatar. Our solution is based on the adequate reduction of the controllable dimensionality of a high-DOF humanoid motion in line with the state-of-the-art possibilities of non-invasive BMI technologies, leaving the complement subspace part of the motion to be planned and executed by an autonomous humanoid whole-body motion planning and control framework. The results are shown in full physics-based simulation of a 36-degree-of-freedom humanoid motion controlled by a user through EEG-extracted brain signals generated with motor imagery task. © 2014 Bouyarmane, Vaillant, Sugimoto, Keith, Furukawa and Morimoto.

DOI
EMGを用いたヒト運動中の関節トルク推定

古川淳一朗, 野田智之, 寺前達也, 森本淳 (担当区分：筆頭著者,　責任著者 )

第18回ロボティクスシンポジア講演論文集 18th 510 - 515 2013年03月 [査読有り]
アシストロボットのインタラクションを考慮した筋電位に基づく力制御

野田智之, 古川淳一朗, 寺前達也, 玄相昊, 森本淳

第18回ロボティクスシンポジア講演論文集 18th 392 - 398 2013年03月 [査読有り]
BCI control of whole-body simulated humanoid by combining motor imagery detection and autonomous motion planning

Karim Bouyarmane, Joris Vaillan, Norikazu Sugimoto, François Keith, Jun-ichiro Furukawa, Jun Morimoto

In proceedings - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8226 ( 1 ) 310 - 318 2013年 [査読有り]

　概要を見る

In this paper we demonstrate the coupling of an autonomous planning and control framework for whole-body humanoid motion, with a brain-computer interface (BCI) system in order to achieve online real-time biasing and correction of the offline planned motion. Using the contact-before-motion planning paradigm, the humanoid autonomously plans, in a first stage, its motion to reach a desired goal configuration or contact location. In the second stage of the approach, the humanoid executes the planned motion and the user can exert online some control on the motion being executed through an EEG decoding interface. The method is applied and demonstrated in a dynamics simulator with full collision-detection on a model of the humanoid robot HRP2. © Springer-Verlag 2013.

DOI
An electromyogram based force control coordinated in assistive interaction

Tomoyuki Noda, Jun-ichiro Furukawa, Tatsuya Teramae, Sang-Ho Hyon, Jun Morimoto

In proceedings - IEEE International Conference on Robotics and Automation (ICRA) 2657 - 2662 2013年 [査読有り]

　概要を見る

This study proposes the design of electromyography (EMG)-based force feedback controller which explicitly considers human-robot interaction for the exoskeletal assistive robot. Conventional approaches have been only consider one-directional mapping from EMG to control input for assistive robot control. However, EMG and force generated by the assistive robot interfere each other, e.g., amplitude of EMG decreases if limb movements are assisted by the robot. In our proposed method, we first derive the nonlinear mapping from EMG signal to muscle force for estimating human joint torque, and convert it to assistive force using human musculoskeletal model and robot kinematic model. Additionally the feedforward interaction torque is feedback into torque controller to acquire the necessity loads. To validate the feasibility of the proposed method, assistive One-DOF system was developed as the real equipment and the simulator. We compared the proposed method with conventional approaches using both the simulated and the real One-DOF systems. As the result, we found that the proposed model was able to estimate the necessary torque adequately to achieve stable human-robot interaction. © 2013 IEEE.

DOI
Brain-Controlled Exoskeleton Robot for BMI Rehabilitation

Tomoyuki Noda, Norikazu Sugimoto, Jun-ichiro Furukawa, Masa-aki Sato, Sang-Ho Hyon, Jun Morimoto

In proceedings - IEEE-RAS International Conference on Humanoid Robots (Humanoids) ( IEEE ) 21 - 27 2012年 [査読有り]

　概要を見る

In this paper, we introduce our attempt to develop an assistive robot system which can contribute to Brain-Machine Interface (BMI) rehabilitation. For the BMI rehabilitation, we construct a Electroencephalogram(EEG)-Exoskeleton robot system, where the exoskeleton robot is connected to the EEG system so that the users can control the exoskeleton robot by using their brain activities. We use a classification method which considers covariance matrices of measured EEG signals as inputs to decode brain activities. The decoded brain activities are used to control exoskeleton movements. In this study, we consider assisting the stand-up movement which is one of the most frequently appeared movements in daily life and also a standard movement as a rehabilitation training. To assist the stand-up movement, we develop a force control model which takes dynamics of tendon string into account for the pneumatic-electric hybrid actuation system used in our exoskeleton robot. The results show that the exoskeleton robot successfully assisted user stand-up movements, where the assist system was activated by the decoded brain activities.

DOI

▼全件表示

Misc

外骨格ロボットの運動学習手法

古川淳一朗, 森本淳 (担当区分：筆頭著者 )

日本ロボット学会誌 ( The Robotics Society of Japan ) 42 ( 10 ) 947 - 952 2024年12月

DOI
Standing to Sitting Assistance - Control Strategy for an Assistive Robotic Chair Based on Human Motion Prediction

Elena Basei, Jun-ichiro Furukawa, Takahide Ito, Qi An, Jun Morimoto (担当区分：責任著者 )

IEEE 20th International Conference on Automation Science and Engineering (CASE), Work-in-progress and Industry Presentation-only Papers 2024年08月 [査読有り]
加齢が起立動作中の筋力モビリティ楕円体に与える影響の解析と支援椅子の座面制御法への応用

早瀬瑞華, 菊池謙, 古川淳一朗, 井藤隆秀, 森本淳, 中村裕一, 淺間一, 山下淳, 安琪

第３３回ライフサポート学会フロンティア講演会予稿集 49 2024年03月
加齢が起立動作中の筋力モビリティ楕円体を考慮した支援椅子の座面制御方法の開発

早瀬瑞華, 菊池謙, 古川淳一朗, 井藤隆秀, 森本淳, 中村裕一, 淺間一, 山下淳, 安琪

2024年度精密工学会春季大会学術講演会講演論文集 452 - 453 2024年03月
加齢が起立動作中の筋力モビリティ楕円体に与える影響

早瀬瑞華, 菊池謙, 古川淳一朗, 井藤隆秀, 森本淳, 中村裕一, 淺間一, 山下淳, 安琪

第２４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講演論文集 3004 - 3006 2023年12月
上肢肩屈曲アシスト時の共同運動予測モデルを用いたアシスト率の最適化~脳卒中片麻痺患者によるフレームワークの実証~

寺前達也, 畠中めぐみ, 神尾昭宏, 平松佑一, 古川淳一朗, 古川淳一朗, 宮井一郎, 野田智之

日本ロボット学会学術講演会予稿集(CD-ROM) 40th 2022年
ヒトの起立動作を支援する椅子の開発～ジップチェーンアクチュエータを用いた座面の移動機構～

黒田大登, 安琪, 山川博司, 下田真吾, 古川淳一朗, 森本淳, 中村裕一, 倉爪亮

日本ロボット学会学術講演会 39th 2021年09月
左右独立免荷ロボットを用いたトレッドミル歩行による、脳卒中後片麻痺患者の歩行パターン変容

藤田暢一, 藤本宏明, 平松佑一, 高井飛鳥, 寺前達也, 古川淳一朗, 畠中めぐみ, 神尾昭宏, 野田智之, 森本淳, 宮井一郎

理学療法学 47 ( Suppl.1 ) 127 - 127 2021年03月
Split-force BWS robotによる脳卒中患者に対するトレッドミル訓練

藤本宏明, 高井飛鳥, 野田智之, 寺前達也, 藤田暢之, 平松佑一, 畠中めぐみ, 神尾昭宏, 古川淳一朗, 矢倉一, 河野悌司, 乙宗宏範, 森本淳, 宮井一郎

The Japanese Journal of Rehabilitation Medicine 57 ( 特別号 ) 1 - 1 2020年07月
ユーザ嗜好に基づくEMGを用いた運動支援制御器のベイズ最適化

濱屋政志, 濱屋政志, 松原崇充, 松原崇充, 古川淳一朗, SUN Yuting, 八木聡明, 寺前達也, 野田智之, 森本淳

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(CD-ROM) 19th ROMBUNNO.1C5‐07 2018年12月
脳卒中患者に対する上肢外骨格ロボット端末適用の臨床的検証

畠中めぐみ, 古川淳一朗, 寺前達也, 神尾昭宏, 平松佑一, 服部憲明, 服部憲明, 服部憲明, 乙宗宏範, 乙宗宏範, 藤本宏明, 河野悌司, 河原田倫子, 吉岡知美, 矢倉一, 野田智之, 宮井一郎, 森本淳

Japanese Journal of Rehabilitation Medicine ( (公社)日本リハビリテーション医学会 ) 55 ( Supplement ) ROMBUNNO.1‐5‐5‐7(J‐STAGE) - 5 2018年05月
表面筋電のチャネルの冗長性を利用したロバストな関節トルク推定

古川淳一朗, 野田智之, 森本淳 (担当区分：筆頭著者 )

日本ロボット学会学術講演会予稿集, RSJ2013AC3F1-05 2013年09月
ヒト運動時のEMG信号と関節角を観測データとした2足ロボットモデルの制御

古川淳一朗, 野田智之, 森本淳 (担当区分：筆頭著者 )

日本ロボット学会学術講演会予稿集, RSJ2012AC2K1-6 2012年09月

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受賞（研究活動に関するもの）

理研桜舞賞

受賞者：古川淳一朗

2025年03月理化学研究所
real-time functional imaging and neurofeedback conference, travel award

受賞者：古川淳一朗

2017年
ATR社内表彰奨励賞

受賞者：古川淳一朗

2016年
第12回IEEE関西支部学生研究奨励賞

受賞者：古川淳一朗

2016年
博士前期課程首席修了

受賞者：古川淳一朗

2013年03月大阪大学大学院生命機能研究科

講演・口頭発表等

ヒトの運動をさりげなくアシストするロボットの研究開発

古川淳一朗

GRPシンポジウム 2022年03月18日
Composing An Assistive Control Strategy based on Linear Bellman Combination from Estimated User's Motor Goal

Jun-ichiro Furukawa, Jun Morimoto

IEEE International Conference on Robotics and Automation (ICRA2021) 2021年06月
Split-force BWS robotによる脳卒中患者に対するトレッドミル訓練

藤本宏明, 高井飛鳥, 野田智之, 寺前達也, 藤田暢一, 平松佑一, 畠中めぐみ, 神尾昭宏, 古川淳一朗, 矢倉一, 河野悌司, 乙宗宏範, 森本淳, 宮井一郎

第５７回日本リハビリテーション医学会学術集会 2020年08月19日
A novel robot for separate body weight supported treadmill training to modulate gait patterns in patients with hemiparetic stroke

Hiroaki Fujimoto, Tatsuya Teramae, Tomoyuki Noda, Asuka Takai, Nobukazu Fujita, Megumi Hatakenaka, Yuichi Hiramatsu, Akihiro Jino, Jun-ichiro Furukawa, Hajime Yagura, Teiji Kawano, Hironori Otomune, Jun Morimoto, Ichiro Miyai

Society for Neuroscience Annual Meeting (Neuroscience2019) 2019年10月
Optimizing shoulder flexion practice using an exoskeleton robot in patients with hemiparetic stroke

Megumi Hatakenaka, Jun-ichiro Furukawa, Tatsuya Teramae, Akihiro Jino, Yuichi Hiramatsu, Hiroaki Fujimoto, Nobukazu Fujita, Hironori Otomune, Teiji Kawano, Noriaki Hattori, Hajime Yagura, Tomoyuki Noda, Jun Morimoto, Ichiro Miyai

13th International Society of Physical and Rehabilitation Medicine World Congress (ISPRM2019) 2019年06月09日
Intelligent-BWS: a novel robot for separate body weight support treadmill training in poststroke gait disorder-a preliminary case study

Hiroaki Fujimoto, Asuka Takai, Nobukazu Fujita, Megumi Hatakenaka, Tomoyuki Noda, Tatsuya Teramae, Jun-ichiro Furukawa, Nao Nakano, Akihiro Jino, Yuichi Hiramatsu, Hironori Otomune, Teiji Kawano, Hajime Yagura, Noriaki Hattori, Jun Morimoto, Ichiro Miyai

13th International Society of Physical and Rehabilitation Medicine World Congress (ISPRM2019) 2019年06月09日
Changes of Muscle Synergy with Modulation of Gravity Load during Shoulder Flexion in Patients with Hemiparetic Stroke-Quantitative Evaluation Using an Exoskeleton Robot

Miho Ogura, Michiyuki Kawakami, Tatsuya Teramae, Kohei Okuyama, Miho Kuroki, Tomoyuki Noda, Jun-ichiro Furukawa, Jun Morimoto, Meigen Liu

13th International society of physical and Rehabilitation Medicine World Congress (ISPRM2019) 2019年06月09日
A robot for split-force body weight-supported treadmill training modulates gait patterns of patients with hemiparetic stroke: case studies

Hiroaki Fujimoto, Tatsuya Teramae, Tomoyuki Noda, Asuka Takai, Nobukazu Fujita, Megumi Hatakenaka, Yuichi Hiramatsu, Akihiro Jino, Jun-ichiro Furukawa, Hajime Yagura, Teiji Kawano, Hironori Otomune, Jun Morimoto, Ichiro Miyai

American Society of Neurology (ASNR) 2019年
脳卒中後の上肢麻痺に対する療法士ハンドリング技術の上肢外骨格ロボットを用いた定量化の試み

神尾昭宏, 畠中めぐみ, 古川淳一朗, 野田智之, 宮井一郎

第53回作業療法学会 2019年
左右独立免荷ロボットを用いたトレッドミル歩行による、脳卒中後片麻痺患者の歩行パターン変容

藤田暢一, 藤本宏明, 平松佑一, 高井飛鳥, 寺前達也, 古川淳一朗, 畠中めぐみ, 神尾昭宏, 野田智之, 森本淳, 宮井一郎

第１７回神経理学療法学術大会 2019年
脳卒中患者に対する上肢外骨格ロボット端末適用の臨床的検証

畠中めぐみ, 古川淳一朗, 寺前達也, 神尾昭宏, 平松佑一, 服部憲明, 乙宗宏範, 藤本宏明, 河野悌司, 川原田倫子, 吉岡知美, 矢倉一, 野田智之, 宮井一郎, 森本淳

第55回日本リハビリテーション医学会学術集会 2018年
Estimating humerus head elevation in human shoulder joints via gaussian process regression

Sara Hamdan, Erhan Oztop, Jun-ichiro Furukawa, Jun Morimoto, Barkan Ugurlu

Turkey Robotics Conference 2018年
A data-driven approach for estimating human behavior with collaborative filtering

Jun-ichiro Furukawa, Jun Morimoto

Joint workshop of UCL-ICN, NTT, UCL-Gatsby, and AIBS 2018年
ユーザ嗜好に基づくEMGを用いた運動支援制御器のベイズ最適化

濱屋政志, 松原崇充, 古川淳一朗, 孫雨庭, 八木聡明, 寺前達也, 野田智之, 森本淳

第１９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 2018年
Human Movement Estimation from Multiple Biosignal Observations toward Safe Assistive Robot Control

Jun-ichiro Furukawa, Tomoyuki Noda, Tatsuya Teramae, Jun Morimoto

real-time functional imaging and neurofeedback conference 2017年
A Forward and Inverse Optimal Control Framework to Generate Humanoi Robot Movements with Hierarchical MPC

Koji Ishihara, Jun-ichiro Furukawa, Jun Morimoto

The Multi-disciplinary Conference on Reinforcement Learning and Decision Making 2017年
Proprioceptive Gain Affects Motor Learning

Shinya Chiyohara, Jun-ichiro Furukawa, Jun Morimoto, Hiroshi Imamizu

real-time functional imaging and neurofeedback conference 2017年
Optimizing neurorehabilitation for stroke using an exoskeleton robot

Megumi Hatakenaka, Jun-ichiro Furukawa, Tatsuya Teramae, Michal Gloger, Akihiro Jino, Yuichi Hiramatsu, Noriaki Hattori, Teiji Kawano, Hajime Yagura, Tomoyuki Noda, Jun Morimoto, Ichiro Miyai

XXIII World Congress of Neurology 2017年
表面筋電のチャネルの冗長性を利用したロバストな関節トルク推定

古川淳一朗, 野田智之, 森本淳

第31回日本ロボット学会学術講演会 2013年
EMGを用いたヒト運動中の関節トルク推定

古川淳一朗, 野田智之, 寺前達也, 森本淳

第18回ロボティクスシンポジア 2013年
ヒト運動時のEMG信号と関節角を観測データとした２足ロボットモデルの制御

古川淳一朗, 野田智之, 森本淳

第30回日本ロボット学会学術集会 2012年
アシストロボットのインタラクションを考慮した筋電位に基づく力制御

野田智之, 古川淳一朗, 寺前達也, 森本淳

第18回ロボティクスシンポジア
筋電情報を用いたロボット制御とその応用

古川淳一朗

スポーツ心理学会第４５回大会シンポジウム
Estimating joint movements from observed EMG signals with multiple electrodes under sensor failure situations toward safe assistive robot control

Jun-ichiro furukawa

IEEE International Conference on Robotics;Automation (ICRA), Seattle, WA, USA
Database-driven approach for Biosignal-based robot control with collaborative filtering

Jun-ichiro Furukawa

IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), Birmingham, UK
Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot

Jun-ichiro Furukawa

IEEE International Conference on Robotics and Automation (ICRA2022)

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