Kyushu University Academic Staff Educational and Research Activities Database
Researcher information (To researchers) Need Help? How to update
Kazuo Kiguchi Last modified date:2023.09.27



Graduate School
Undergraduate School
Other Organization


Homepage
https://kyushu-u.elsevierpure.com/en/persons/kazuo-kiguchi
 Reseacher Profiling Tool Kyushu University Pure
http://system.mech.kyushu-u.ac.jp/index.html
System Engineering Laboratory HP .
Academic Degree
Doctor of Engineering, Master of Applied Science (University of Ottawa, Canada)
Country of degree conferring institution (Overseas)
Yes Master
Field of Specialization
Robotics, Biorobotics, Medical/Welfare Robots
Total Priod of education and research career in the foreign country
00years00months
Research
Research Interests
  • Medical and Human-Assist Robot, Power-Assist Robot, Human Prosthetic System, Human Motion Simulator
    keyword : Medical and Human-Assist Robot
    1992.01~2022.03.
Current and Past Project
  • Moonshot R&D "Goal 3: Realization of AI robots that autonomously learn, adapt to their environment, evolve in intelligence and act alongside human beings, by 2050"
Academic Activities
Books
1. Kazuo Kiguchi, Upper-Limb Exoskeletons for Physically Weak Persons, Advanced Robotic Systems International, Rehabilitation Robotics, Chapter 16,pp.287-302, 2007.08.
2. Kazuo Kiguchi, K.Watanabe, T.Fukuda, Neural Network and Fuzzy Control Techniques in Robotic Systems, CRC Press, Intelligence Systems Techniques and Applications, Volume II, Chapter 4, pp.73-95, 2003.04.
3. Kazuo Kiguchi, T.Fukuda, Fuzzy Theory Systems: Techniques and Applications, Academic Press, Volume 3, Chapter 47, pp.1323-1339, 1998.03.
4. Kazuo Kiguchi, Toshio Fukuda, Robot Force/Position Control with an Unknown Environment - Application of Soft Computing, World Scientific, Recent Advances in Circuits and Systems, Part III: Robotics, pp.275-280, 1998.03.
Reports
1. Kazuo Kiguchi, Keigo Watanabe, Kiyotaka Izumi, Toshio Fukuda, Position/force control of robot manipulators for a geometrically unknown environment using fuzzy vectors, Advanced Robotics, vol.14, no.5, pp.389-392, 2000, 2000.06.
Papers
1. Kanta Maemura, Kazuo Kiguchi, Simultaneous Control of Tonic Vibration Reflex and Kinesthetic Illusion for Elbow Joint Motion Toward Novel Robotic Rehabilitation, Proc. of 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 4773-4776, 2021.10, Robotic rehabilitation is one of the most promising applications of robotic technologies. It is known that patients’ active participation in rehabilitation is important for their recovery. On the other hand, mechanical vibration stimulation to muscles induces tonic vibration reflex (TVR) and kinesthetic illusion (KI) in the joint motion. In this paper, the possibility of a novel robotic rehabilitation method, in which the TVR is applied to an agonist muscle to enhance the intended motion of patients and the KI is simultaneously applied to an antagonist muscle to enhance the kinesthetic movement sensation of the generating intended motion by changing the frequency of vibration stimulation, is investigated. As the first step toward novel robotic rehabilitation, the proposed method is evaluated in elbow joint motion. The experimental results show the possibility of the proposed novel rehabilitation method..
2. #XIA JIUYUN, @KAZUO KIGUCHI, Sensorless Real-Time Force Estimation in Microsurgery Robots Using a Time Series Convolutional Neural Network,, IEEE Access, 10.1109/ACCESS.2021.3124304, 9, 149447-149455, 2021.12, Robotic-assisted microsurgeries provide several benefits to both patients and surgeons. Nevertheless, there are still some limitations and challenges associated with their outcome, one of which is a lack of force feedback. Without force information, the risk of delicate tissue damage from the excessive force applied by surgeons would be increased. Since it is difficult to install force sensors on microsurgical tools, a novel approach for estimating a force vector from the deformation of the surgical tool is proposed in this paper. In the proposed approach, a surgical instrument that deforms according to the magnitude of the tool-to-tissue force is designed, and a time series convolution neural network is used to make the nonlinear relationship between the visual information of the deformation of the surgical tool and the applied forces in such a way that the tool-to-tissue force can be estimated according to the deformation of the surgical instrument in a real-time manner. The experimental results prove that the applied force can be successfully estimated with high accuracy in three dimensions using the proposed method..
3. WENBIN LIU, TAKERU KAI, and KAZUO KIGUCHI, Tremor Suppression with Mechanical Vibration Stimulation, IEEE Access, 10.1109/ACCESS.2020.3045023, 8, 226199-226212, 2020.12, Tremor, which is one of the most common movement disorders, is a repetitive movement that is caused by periodic muscle contraction and relaxation. To suppress tremors of upper-limb tremor patients, such as essential tremor (ET) patients, many kinds of devices have been developed. On the other hand, when mechanical vibration stimulation is applied to a human muscle, sustained muscle contraction, which is referred to as the tonic vibration reflex (TVR), is induced in the stimulated muscle. In this study, a novel tremor suppression method that utilizes the periodic TVR to induce muscle contraction/relaxation to generate the counterphase motion of the ET is proposed and applied to the forearm pronation-supination ET. In the proposed method, periodic vibration stimulation is applied to generate the periodic TVR in the pronator teres muscle and/or supinator muscle. First, the results confirmed that the TVR can be induced by applying mechanical vibration stimulation to the pronator teres muscle and supinator muscle since the forearm pronation-supination tremor is one of the key features of the ET. Furthermore, the findings also confirmed that the TVR intensity that is induced in these muscles can be adjusted by changing the vibration stimulation frequency. Second, the results show that the counterphase motion of the ET (i.e., periodic pronation-supination motion) can be generated by applying the proposed method. The effectiveness of the proposed method for tremor suppression is evaluated by comparing the generated motion with the ET motion..
4. Koki Honda, Kazuo Kiguchi, Control of Human Motion Change Based on Vibration Stimulation for Upper-Limb Perception-Assist, IEEE Access, 10.1109/ACCESS.2020.2969789, 8, 22697-22708, 2020.01, To prevent accidents occurring in daily life, such as the collision between objects in the environment and our body, the perception-assist technique, which can automatically change a dangerous motion to a safe motion, has been considerably studied. In addition, it has recently been found by the authors that a human's motion is different from his/her intended motion if vibration stimulation is presented on an
antagonist muscle. In this study, a method to change human motion in a controlled manner using vibration stimulation is proposed for the perception-assist and applied for elbow-joint-extension motion change. In this study, it is elucidated that the amount of elbow-joint-extension motion change can be changed with respect to the frequency change of the vibration stimulation. Furthermore, it is elucidated that the change in the human elbow-joint-extension motion can be generated soon after the vibration stimulation is provided. Based on the characteristics of motion change with respect to vibration stimulation, the proposed method controls the change of the user's elbow-joint-extension motion by adjusting the frequency of the vibration stimulation to achieve the target motion, which is different from the user's intended motion. The effectiveness of the proposed method toward the change in the human elbow-joint-extension motion was experimentally evaluated..
5. @D.S.V. Bandara, @Jumpei Arata, @Kazuo Kiguchi, Non Invasive BCI Approach for Prediction of Motion Intention of ADL Tasks for an Upper – Limb Wearable Robot, International Journal of Advanced Robotic Systems, 1-10, 2018.02.
6. D.S.V. Bandara, Jumpei Arata, Kazuo Kiguchi, Towards control of a transhumeral prosthesis with EEG signals”, Bioengineering, 5, issue 2, 26 (Open Access), 2018.02, [URL].
7. Kazuo Kiguchi, Yoshiaki Hayashi, A Study on Effect of Muscle Tension on Artificial Hip Joint Dislocation, The 15th IUMRS-International Conference in Asia (IUMRS-ICA 2014), 2014.08.
8. Kazuo Kiguchi, Thilina Dulantha Lalitharatne, Yoshiaki Hayashi, Estimation of Forearm Supination/Pronation Motion Based on EEG signals to Control an Artificial Arm, Journal of Advanced Design, Systems, and Manufacturing, vol.7, no.1, pp.74-81, 2013.02.
9. Kazuo Kiguchi, Yoshiaki Hayashi, An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot, IEEE Trans. on Systems, Man, and Cybernetics, Part B, 10.1109/TSMCB.2012.2185843, 42, 4, 1064-1072, 2012.08, Many kinds of power-assist robots have been developed in order to assist self-rehabilitation and/or daily life motions of physically weak persons. Several kinds of control methods have been proposed to control the power-assist robots according to user’s motion intention. In this paper, an EMG-based impedance control method for an upper-limb power-assist exoskeleton robot is proposed to control the robot in accordance with the user’s motion intention. The proposed method is simple, easy to design, human-like, and adaptable to any user. A neuro-fuzzy matrix modifier is applied to make the controller adaptable to any users. Not only the characteristics of EMG signals but also the characteristics of human body are taken into account in the proposed method. The effectiveness of the proposed method was evaluated by the experiments..
10. Kazuo Kiguchi, Control of a Redundant 7DOF Upper-Limb Power-Assist Exoskeleton Robot, Journal of Artificial Intelligence and Soft Computing Research, vol.1, no.3, pp,207-214, 2011.08.
11. Kazuo Kiguchi, Manoj Liyanage, Yasunori Kose, Perception Assist with an Active Stereo Camera for an Upper-Limb Power-Assist Exoskeleton, International Journal of Robotics and Mechatronics, vol. 21, no. 5, pp.614-620, 2009.08.
12. Kazuo Kiguchi, Mohammad Habibur Rahman, Makoto Sasaki, Kenbu Teramoto, Development of a 3DOF Mobile Exoskeleton Robot for Human Upper Limb Motion Assist, Robotics and Autonomous Systems, vol.56, no.8, pp.678-691, 2008.08.
13. Kazuo Kiguchi, Hui He, Kenbu Teramoto, A Study on Multi-Dimensional Fuzzy Q-Learning for Intelligent Robots, International Journal of Fuzzy Systems, vol.9, no.2, pp.92-104, 2007.06.
14. Kazuo Kiguchi, Active Exoskeletons for Upper-Limb Motion Assist, Journal of Humanoid Robotics, vol. 4, no. 3, pp. 607-624, 2007.03.
15. Kazuo Kiguchi, Yoshihiko Sakamoto, Kenbu Teramoto, Jiro Uozumi, Keiji Nakashima, A Study on Bladder Compression Systems for Urination Assist, International Journal of Human-friendly Welfare Robotic Systems, vol.6, no.2, pp.62-66, 2005.07.
16. Kazuo Kiguchi, Ryo Esaki, Toshio Fukuda, Development of a Wearable Exoskeleton for Daily Forearm Motion Assist, Advanced Robotics, vol.19, no.7, pp.751-771, 2005.03.
17. Kazuo Kiguchi, Takakazu Tanaka, Toshio Fukuda, Neuro-Fuzzy Control of a Robotic Exoskeleton with EMG Signals, IEEE Transactions on Fuzzy Systems, 10.1109/TFUZZ.2004.832525, vol.12, no.4, 481-490, 2004.08, We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important information, however, it is not very easy to predict the user’s upper-limb motion (elbow and shoulder motion) based on the EMG signals in real-time because of the difficulty in using the EMG signals as the controller input signals. In this paper, we propose a robotic exoskeleton for human upper-limb motion assist, a hierarchical neuro-fuzzy controller for the robotic exoskeleton, and its adaptation method..
18. Kazuo Kiguchi, K.Iwami, K.Watanabe, T.Fukuda, Controller Adjustment of an Exoskeleton Robot for Shoulder Motion Assist, Journal of Robotics and Mechatronics, vol.16, no.3, pp.245-255, 2004.03.
19. Kazuo Kiguchi, Keigo Watanabe, Toshio Fukuda, Trajectory Planning of Mobile Robots with DNA Computing, Journal of Advanced Computational Intelligence on Computational Intelligence in Robotics and Automation, vol.8, no.3, pp.295-301, 2004.03.
20. Kazuo Kiguchi, Koya Iwami, Keigo Watanabe, Toshio Fukuda, An Assist Level Adjustment Method of an Active Shoulder Orthosis, International Journal of Human-friendly Welfare Robotic Systems, Vol. 4, No. 2, pp.8-12, 2003.06.
21. Kazuo Kiguchi, Thrishantha Nanayakkara, Keigo Watanabe, Toshio Fukuda, Multi-Dimensional Reinforcement Learning Using a Vector Q-Net - Application to Mobile Robots, Internal Journal of Control, Automation and Systems, vol.1, no.1, pp.142-148, 2003.03.
22. Kazuo Kiguchi, Koya Iwami, Makoto Yasuda, Keigo Watanabe, Toshio Fukuda, An Exoskeletal Robot for Human Shoulder Joint Motion Assist, IEEE/ASME Trans. on Mechatronics, vol.8, no.1, pp.125-135, 2003.03.
23. K.Kiguchi, S.Kariya, T.Tanaka, K.Watanabe, T.Fukuda, An Interface between an Exoskeletal Elbow Motion Assist Robot and a Human Upper-Arm, Journal of Robotics and Mechatronics, vol.14, no.5, pp.439-452, 2002.10.
24. Kazuo Kiguchi, Yukihiro Kusumoto, Keigo Watanabe, Kiyotaka Izumi, Toshio Fukuda, Energy Optimal Gait Analysis of Quadruped Robots, Artificial Life and Robotics – An International Journal, vol.6, pp.120-125, 2002.09.
25. K.Kiguchi, K.Watanabe, T.Fukuda, Generation of Efficient Adjustment Strategies for a Fuzzy-Neuro Force Controller using Genetic Algorithms – Application to Robot Force Control in an Unknown Environment, The International Journal of Information Sciences, vol.145, no.1-2, pp.113-126, 2002.08.
26. Kazuo Kiguchi, Shingo Kariya, Keigo Watanabe, Toshio Fukuda, Application of Multiple Fuzzy-Neuro Controllers of an Exoskeletal Robot for Human Elbow Motion Support, Transactions on Control, Automation and Systems Engineers (CASE), vol.4, no.1, pp.49-55, 2002.03.
27. K.Kiguchi, T.Fukuda, Fuzzy Neural Hybrid Position/Force Control for Robot Manipulators, Journal of Applied Mathematics and Computer Science, vol.6, no.3, pp.101-121.
28. K.Kiguchi, T.Fukuda, Intelligent Position/Force Controller for Industrial Robot Manipulators Application of Fuzzy Neural Networks, IEEE Transactions on Industrial Electronics, vol.44, no.6, pp.753-761.
29. K.Kiguchi, T.Fukuda, Neural Network Controllers for Robot Manipulators Application of Damping Neurons, Advanced Robotics, vol.12, no.3, pp.191-208.
30. K.Kiguchi, T.Fukuda, Y.Koga, T.Watanabe, K.Terajima, T.Hayashi, M.Sakamoto, M.Matsueda, Y.Suzuki, H.Segawa, Development of A Physiological Knee Motion Simulator, Advanced Robotics, vol.13, no.2, pp.171-188.
31. K.Kiguchi, T.Fukuda, Position/Force Control of Robot Manipulators for Geometrically Unknown Objects Using Fuzzy Neural Networks, IEEE Transactions on Industrial Electronics, vol.47, no.3, pp.641-649.
32. K.Kiguchi, K.Watanabe, K.Izumi, T.Fukuda, Two-Stage Adaptive Robot Position/Force Control Using Fuzzy Reasoning and Neural Networks, Advanced Robotics, vol.14, no.3, pp.153-168.
33. K.Kiguchi, H.Miyaji, K.Watanabe, K.Izumi, T.Fukuda, Generation of an Optimal Architecture of Neuro Force Controllers for Robot Manipulators in Unknown Environments Using Genetic Programming with Fuzzy Fitness Evaluation, Soft Computing, vol.5, no.3, pp.237-242.
34. K.Kiguchi, S.Kariya, K.Watanabe, K.Izumi, T.Fukuda, An Exoskeletal Robot for Human Elbow Motion Support – Sensor Fusion, Adaptation, and Control, IEEE Trans. on Systems, Man, and Cybernetics, Part B, vol.31, no.3, pp.353-361.
35. Kazuo Kiguchi, Koya Iwami, Keigo Watanabe, Toshio Fukuda, A Study of an EMG-Based Exoskeletal Robot for Human Shoulder Motion Support, JSME International Journal, Series C, vol.44, no.4, pp.1133-1141.
Presentations
1. Kazuo Kiguchi, Thilina Dulantha Lalitharatne, Yoshiaki Hayashi, Control of Lower-Limb Power-Assist Robot Based on EEG signals, Proceedings of The 2nd IFToMM Asian Conference on Mechanism and Machine Science (Asian-MMS2012).
2. Kazuo Kiguchi, Kodai Yoshino, Yoshiaki Hayashi, Munenori Uemura, Morimasa Tomikawa, Makoto Hashizume, Difference Between Experienced Surgeons and Beginners in Suturing Skills in Laparoscopic Surgery, Proceedings of the 10th IASTED International Conference on Biomedical Engineering (BioMed 2013),, 2013.02.
3. Kazuo Kiguchi, Kaori Tamura, Yoshiaki Hayashi, Estimation of Joint Force/Torque Based on EMG signals, Proceedings of the SSCI 2013 (Proc. of 2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2013), 2013.04.
4. Kazuo Kiguchi, Yoshiaki Hayashi, Upper-Limb Tremor Suppression with a 7DOF Exoskeleton Power-Assist Robot, Proceedings of 35th Annual International Conference on the IEEE Engineering in Medicine and Biology Society (EMBC2013), 2013.07.
5. Kazuo Kiguchi, Yoshiaki Hayashi, Estimation of User’s Motion Intention of Hand based on both EMG and EEG Signals
, ICINCO2013, 2013.07.
6. Kazuo Kiguchi, Control of Human Assist Robots Based On User’s Motion Intention, 14th International Symposium on Advanced Intelligent Systems (ISIS2013), 2013.11, Human assist robots are expected to play an important role to enrich human daily life in these days. Especially, power-assist robots or robotic limbs are used to assist daily activities of physically weak persons or disabled persons. Although those robotic systems must be activated based on the user’s motion intention, it is not easy to estimate that in real-time. Therefore, many studies have been carried out to control the human assist robot based on the user’s motion intention. User’s biological signals such as EMG (Electromyogram) and EEG (Electroencephalogram) are important signals to understand the user’s motion intention. The motion intention can be directly estimated with the EMG in real time since the EMG directly reflects the muscle activity level. However, it is sometimes difficult to obtain the EMG from paralyzed persons or amputees. On the other hand, EEG can be obtained even from the paralyzed persons or amputees, although it does not correlate with the motion intention directly. Soft computing such as fuzzy reasoning, artificial neural networks, or genetic algorithm is one of the key technologies to extract the user’s motion intention from the EMG or EEG of the user.
In the presentation, several soft computing methods, which are used to extract the user’s motion intention from the EMG or EEG of the user to activate the power-assist robots or the robotic limbs in real-time, are explained. The flexibility of the soft computing effectively works in those methods. Control methods for the power-assist robots and the robotic limbs are also explained. Furthermore, the latest studies on the human assist robots are discussed..
7. Kazuo Kiguchi, Design and Control of Human Assistive Robots, 招待講演, 2013.11.
8. Kazuo Kiguchi, Yutaka Yokomine, Nobuhiro Okada, Yoshiaki Hayashi, Control of Foot Trajectory in Perception‐Assist with a Power‐Assist Robot, Second International Conference on Robot, Vision and Signal Processing (RVSP-2013), 2013.12.
9. Kazuo Kiguchi, Yoshiaki Hayashi, Task Estimation of Upper-Limb Using EEG and EMG Signals, AIM2014, 2014.07.
10. Kazuo Kiguchi, Kaori Tamura, Yoshiaki Hayashi, Estimation of User's Hand Motion based on EMG and EEG Signals, WAC2014, 2014.08.
11. Kazuo Kiguchi, Design and Control of Human Assist Robots, WAC2014, 2014.08.
12. Kazuo Kiguchi, Yoshiaki Hayashi, A Study on Effect of Muscle Tension on Artificial Hip Joint Dislocation, The 15th IUMRS-International Conference in Asia (IUMRS-ICA 2014), 2014.08.
13. Kazuo Kiguchi, Yutaka Yokomine, Walking Assist for a Stroke Survivor with a Power-Assist
Exoskeleton, SMC2014, 2014.10.
14. Kazuo Kiguchi, Human Assist Robot Systems, The Seventh Kyushu University-KAIST Joint Workshop on Frontiers in Mechanical Engineering, 2014.09, Human assist robots are expected to play an important role to enrich human daily life in
these days. Especially, power-assist robots or robotic limbs are used to assist daily
activities of physically weak persons or disabled persons. User’s biological signals such
as EMG (Electromyogram) and EEG (Electroencephalogram) are important signals to
understand the user’s motion intention. The motion intention can be directly estimated
with the EMG in real time since the EMG directly reflects the muscle activity level.
However, it is sometimes difficult to obtain the EMG from paralyzed persons or
amputees. On the other hand, EEG can be obtained even from the paralyzed persons or
amputees, although it does not correlate with the motion intention directly. In the
presentation, the EMG-based power-assist and the EEG-based power-assist are
explained. In the case of power-assist, not only motor ability of the user, but also
sensory ability of the user must be assisted sometimes. The concept of perception-assist
is introduced to assist the sensory ability of the user as well as the motor ability of the
user in the presentation. A method of tremor suppression is also explained for the
power-assist..
15. Kazuo Kiguchi, Atsushi Komori, Taiki Kouno, A Study on Motion Modification Force in
Perception-Assist for a Lower-Limb Power-Assist
Exoskeleton
, SCIS & ISIS 2014, 2014.12.
16. Kazuo Kiguchi, Tremor Suppression with an Upper-Limb Power-Assist Robot (I), The 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’16), 2016.08.
17. Kazuo Kiguchi, Fusaomi Nagata, Shingo Yoshimoto, Keigo Watanabe, Maki Habib, Design of 3D Printer-Like Data Interface for a Robotic Removable Machining, ICIRA 2016 , 2016.08.
18. Kazuo Kiguchi, Future Directions of Human Assist Robot Systems, MERCon2018, 2018.05.
19. Kazuo Kiguchi, Robotic Technology to Assist Human Activities of Daily Living, HTC2018, 2018.12.
Membership in Academic Society
  • The Japanese Society for Regenerative Medicine and Rehabilitation
  • Japanese Council of IFToMM (Jc-IFToMM)
  • Society of Biomechanisms Japan
  • Institute of Electrical and Electronics Engineers
  • The Japan Society of Mechanical Engineers
  • Robotics Society of Japan
  • The Society of Instrument and Control Engineers
  • Japan Society of Computer Aided Surgery
  • Japanese Society for Medical and Biological Engineering
Awards
  • JSME Education Award,
    The Japan Society of Mechanical Engineers
  • 2012 Information, Intelligence and Precision Equipment Division Award2012,
    The Japan Society of Mechanical Engineers
  • JSME Robotics and Mechatronics Division Contribution Award,
    JSME Robotics and Mechatronics Division
  • JSME Funai Award, The Japan Society of Mechanical Engineers
Social
Professional and Outreach Activities
IEEE/RSJ IROS2020, Editor
IEEE ICRA2020, Associate Editor
IEEE SMC2020, Program Co-Chair, Editor
.