An Exoskeletal Robot for Human Shoulder Joint Motion Assist

Transcription

1 IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 8, NO. 1, MARCH An Exoskeletal Robot for Human Shoulder Joint Motion Assist Kazuo Kiguchi, Member, IEEE, Koya Iwami, Makoto Yasuda, Keigo Watanabe, Member, IEEE, and Toshio Fukuda, Fellow, IEEE Abstract We have been developing exoskeletal robots in order to assist the motion of physically weak persons such as elderly persons or handicapped persons. In our previous research, a prototype of a two degree of freedom exoskeletal robots for shoulder joint motion assist have been developed since the shoulder motion is especially important for people to take care of themselves in everyday life. In this paper, we propose an effective fuzzy-neuro controller, a moving mechanism of the center of rotation (CR) of the shoulder joint of the exoskeletal robot, and intelligent interface in order to realize a practical and effective exoskeletal robot for shoulder joint motion assist. The fuzzy-neuro controller enables the robot to assist any person s shoulder motion. The moving mechanism of the CR of the robot shoulder joint is used to fit the CR of the robot shoulder joint to that of the physiological human shoulder joint during the shoulder motion. The intelligent interface is realized by applying a neural network and used to cancel out the effect the human subject s arm posture change. The effectiveness of the proposed method has been evaluated by experiment. Index Terms Electromyogram (EMG) signals, exoskeleton, human motion assist, soft computing. I. INTRODUCTION RECENT progress in robotics and mechatronics technology brings a lot of benefits not only in industries, but also in welfare and medicine. We have been developing exoskeletal robots [1] [3] in order to assist the motion of physically weak persons such as elderly persons or handicapped persons. It is important that such physically weak people are able to take care of themselves in the aging society. The exoskeletal robots [4] [7], which are sometimes called as exoskeletons, power suits, man amplifiers, man magnifiers, or power assist systems, have been mainly studied for the purpose of military or industry use from the early 1960s. Since the design concept is different, these robots were not suitable for physically weak persons using in everyday life. On the other hand, active orthotic systems [8], [9], which are similar to the exoskeletal robots, also have been studied for the purposed of welfare and medicine from the 1960s. In order to use these systems, however, the users had to learn how to control the systems because of the primitiveness of their controllers. In this paper, we propose a two degrees of freedom (DOF) exoskeletal robot Manuscript received November 1, 2002; revised December 21, Recommended by Technical Editor T. Nakamura. The work was supported by the Tateisi Science and Technology Foundation, Japan under Grant K. Kiguchi, K. Iwami, M. Yasuda, and K. Watanabe are with the Department of Advanced Systems Control Engineering, Saga University, Saga , Japan ( kiguchi@ieee.org). T. Fukuda is with the Department of Micro System Engineering, Nagoya University, Chikusa-ku, Nagoya , Japan. Digital Object Identifier /TMECH and its control method for automatic shoulder motion assist since human shoulder joints are involved in a lot of motion in everyday life. The proposed exoskeletal robot is a modified version of the previously proposed 2-DOF exoskeletal robot prototype [3]. The architecture of the robot and the controller are newly designed in this paper. The proposed exoskeletal robot is automatically activated based on the human subject s electromyogram (EMG) signals which directly reflect the muscle activity levels of human subject. The EMG signals are important information to understand how the human subject intends to move. Consequently, the EMG signals can be used as input information for the robotic systems [10] [12]. For the exoskeletal robot in this study, seven kinds of the EMG signals from the shoulder muscles of the human subject as well as the shoulder joint angles are used as input information. Thus the exoskeletal robot is able to assist the motion of the human subject effectively by applying his/her EMG signals as main input signals to the robot. Even though the EMG signals contain very important information, it is not very easy to predict the shoulder motion from the EMG signals in a short time since many muscles are involved in the motion [13], [14]. Furthermore, it is difficult to obtain the same EMG signal for the same motion even from the same person since the EMG signal is a biologically generated signal. Moreover, the level of the EMG signals might be much different between persons. Therefore, the robot controller must have on-line adaptation ability to the physiological condition of each human subject if we apply the exoskeletal robots to several persons [15], [16]. In order to cope with this problem, a fuzzy-neuro controller, which is able to adapt itself to the physiological condition of each human subject on-line, is proposed for the controller of the exoskeletal robot. The physiological control of the robot can be realized with this control method. On the other hand, the mechanism of the prototype of the exoskeletal robot in our previous study was too simple in comparison with that of the human shoulder. In addition to this, it is impossible to set the center of rotation (CR) of the robot shoulder joint is the same as that of the human shoulder joint (glenohumeral joint) since it is located inside of the human body. Furthermore, human shoulder complex provides 7-DOF for the arm movement since shoulder complex consists of the scapula, clavicle, and humerus and moves conjointly [17]. Consequently, the CR of the human shoulder joint is dislocated according to the shoulder motion. Since the upper arm of the human subject is almost fixed to the arm holder of the exoskeletal robot in our system, the subject must move his/her body instead of his/her upper arm to adjust the location of the CR of the shoulder /03$ IEEE

2 126 IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 8, NO. 1, MARCH 2003 Fig. 1. Architecture of the exoskeletal robot. joint. Consequently, the human body in the exoskeletal robot was sometimes forced to move back and forth or left and right according to the shoulder motion since the CR of the robot shoulder joint was fixed and different from that of the human shoulder joint. In this paper, we also propose a mechanism of the moving CR of the shoulder joint of the 2-DOF exoskeletal robot for shoulder motion assist in order to cancel out the ill effects caused by the position difference of the CR between the robot shoulder joint and the human shoulder joint during the shoulder motion. The proposed mechanism makes the CR of the robot shoulder joint mechanically move in accordance with the shoulder joint motion. The linkwork mechanism has been applied to realize the proposed mechanism. By the effect of the proposed shoulder mechanism, the generated shoulder motion of the human subject becomes smooth like the physiological shoulder motion. In our previous study [3], the arm posture of the human subject was supposed to be the same at all times. Even though the human subject tries to perform the same shoulder motion, however, the amount of the EMG signals from the shoulder muscles varies [20] if the arm posture is changed since the disposition of the shoulder muscles are changed [18], [19]. Therefore, the arm posture has to be taken into account in order to carry out the reliable shoulder motion assist. In this paper, we propose intelligent interface between the human subject and the fuzzy-neuro controller to cancel out the effect caused by subject s arm posture difference. In this method, the fuzzy-neuro controller is adjusted instantly by the intelligent interface in accordance with the human subject s arm posture. The intelligent interface is realized by applying a neural network. The effectiveness of the proposed exoskeletal robot and its control method has been evaluated by experiment with human subjects. II. EXOSKELETAL ROBOT The architecture of the exoskeletal robot is shown in Fig. 1. The exoskeletal robot consists of a frame, two main links, an arm holder, two dc motor [Harmonic Drive System Company], drive wires, wire tension sensors (strain gauges), and the mechanism of the moving CR of the shoulder joint. The exoskeletal robot worn by a human subject is supposed to assist the subject s shoulder joint motion (flexion-extension and abduction-adduction motions as shown in Fig. 2) by manipulating the subject s upper arm with the arm holder, which is fixed on the slider on the link-2. The manipulation of the subject s upper arm is carried out by controlling the arm holder motion with dc motors via driving wires. The inside of the arm holder is covered by an air cushion in which air pressure is adjustable to fit any size of upper arm. The flexibility of the air cushion softens the motion difference of the robot and the human subject caused by the difference of the CR of the shoulder joints. Considering the fact that many physically weak persons use a wheel chair, the heavy parts of the proposed exoskeletal robot (i.e., the dc motors) are installed in the chair, and the other parts of the exoskeletal robot are directly attached to the human subject. Human shoulder joint (glenohumeral joint) consists of many muscles such as deltoid, biceps, triceps, pectoralis major, infraspinatus, and teres major, and moves in 3-DOF (flexion-extension, abduction-adduction, and internal-external rotation). However, human shoulder complex provides 7-DOF for the arm movement since shoulder complex consists of the scapula, clavicle, and humerus and moves conjointly [17]. In the case of shoulder flexion motion, the clavicle rotates about an anteroposterior axis at the sternoclavicular joint. The rotation results in the elevation and translation (to medial direction) of the acromioclavicular joint with regard to the sternoclavicular joint [21]. The translation of the sternoclavicular joint results in the translation of the glenohumeral joint to the same direction. Therefore, the CR of the glenohumeral joint is dislocated according to the shoulder motion. Since the human upper arm is almost fixed in the arm holder of the robot, the relative distance between the arm holder and the CR of the human shoulder joint is almost constant. Therefore, the distance between the arm holder and the CR of the robot shoulder joint must be moderately adjusted in accordance with the shoulder motion, in order to cancel out the ill effects caused by the position difference of the CR between the robot shoulder and the human shoulder.

3 KIGUCHI et al.: EXOSKELETAL ROBOT FOR HUMAN SHOULDER MOTION ASSIST 127 Fig. 3. Proposed linkwork mechanism (abducted position). (c) Fig. 2. Assisted shoulder joint motion. The proposed mechanism of the moving CR of the robot shoulder, which consists of links and a slider as shown in Fig. 3, is installed between the link-1 and the link-2 of the robot. The motion of the proposed mechanism is depicted in Fig. 4. The joint between the link-1 and the link-2 (i.e., the shoulder joint of the exoskeletal robot) is supposed to be located at just behind the armpit of the human subject. The proposed mechanism makes the CR of the robot shoulder joint move behind (farther position from the arm holder) in accordance with the shoulder vertical flexion angle in the case of vertical flexion motion, and move inward (closer position to the arm holder) in accordance with the shoulder horizontal extension Fig. 4. Motion of the proposed linkwork mechanism. angle in the case of horizontal extension motion. The linkwork mechanism has been applied to realize the proposed mechanism. In the case of shoulder vertical flexion-extension motion, the link-2 is vertically rotated with respect to the joint between the link-1 and link-2. As the link-2 rotates vertically, the additional link (the link for the slider) is rotated with respect to another joint (joint-2). Note that the joint-2 is a universal joint. The other end of the link for the slider is attached on the slider on the link-2. Since the radius of the link-2 and the link for the

4 128 IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 8, NO. 1, MARCH 2003 Fig. 5. Location of electrodes. slider are different, the slider moves along the link-2 according to the shoulder flexion angle. In the case of shoulder horizontal flexion-extension motion, the link-1 is rotated about its axis according to the horizontal flexion-extension angle. As the link-1 rotates, joint-3 is rotated with respect to the axis of the link-1. The rotation of the joint-3 causes the movement of the position of the joint-2 along the lateral-medial direction as shown in Fig. 4. As the position of the joint-2 moves along the lateral-medial direction, the slider moves along the link-2 since the link for the slider is connected to the joint-2. Usually, the limitation of human shoulder movable range is 180 in flexion, 60 in extension, 180 in abduction, and 75 in adduction. Considering the practical application to everyday life, the shoulder motion limitation of the proposed robot is 0 in extension and adduction, 90 in flexion, and 90 in abduction this system. The maximum angular velocity of the motor is limited by the hardware for safety. The maximum torque of the robot (i.e., the maximum current of the motor) is also limited by both the hardware and software for safety. Furthermore, there is an emergency stop switch beside the robot. Fig. 6. Architecture of the fuzzy-neuro controller. III. CONTROL OF THE ROBOT By adjusting the amount of force generated by the shoulder muscles, the shoulder motion can be moderately controlled. The muscle activity level can be described by the EMG signal. Consequently, human intention of shoulder motion can be estimated by observing the EMG signals of the shoulder muscles. Fuzzy-neuro control, combination of fuzzy control and adaptive neuro control, is applied to control the exoskeletal robot. The initial fuzzy IF-THEN control rules of the fuzzy-neuro control are designed based on the analyzed human subject s shoulder motion patterns in the experiment [3] and the experimental results in another research [13], [14] assuming that the arm posture of the human subject is in standard posture (i.e., shoulder rotation angle is neutral [0 ], elbow flexion/extension angle is neutral [0 ], and arm pronation/supination angle is neutral [0 ]). The skin surface EMG signals of shoulder muscles, which imply the human subject s intention, and the shoulder joint angles are used as input signals of the robot controller in order to control the robot as the human subject intended. The location of electrodes on shoulder muscles is shown in Fig. 5. The electrodes are located on the anterior, posterior and middle part Fig. 7. Teaching equipment. of deltoid, biceps, triceps, pectoralis major (lateral part), teres major, pectoralis major (clavicular part), and trapezius and those are connected to ch.1, ch.2, ch.3, ch.4, ch.5, ch.6, and ch.7, respectively. The input variables of the fuzzy-neuro control are the mean absolute value (MAV) [22] of EMG of seven kinds of muscles. The equation of the MAV is written as where is the voltage value at th sampling and is the number of samples in a segment. The number of samples is set to be 100 and the sampling time is set to be 0.5 ms in this study. Four kinds of fuzzy linguistic variables (ZO: zero, PS: positive small, PM: positive medium, and PB: positive big) are prepared for each MAV of EMG of main muscles (ch. 2, 4-6). Three kinds of fuzzy linguistic variables (ZO: zero, PS: positive small, (1)

5 KIGUCHI et al.: EXOSKELETAL ROBOT FOR HUMAN SHOULDER MOTION ASSIST 129 Fig. 8. Change of membership function. and PB: positive big) are prepared for each MAV of EMG of the other muscles (ch. 1, 3, and 7). Another three kinds of fuzzy linguistic variables (LA: low angle, MA: medium angle, HA: high angle) are prepared for each shoulder joint angle. The outputs of the fuzzy-neuro control are the torque command to generate the desired shoulder motion of the exoskeletal robot. The torque command for the exoskeletal robot joints is then transferred to the force command for each driving wire. The relation between the torque command for the exoskeletal robot joints and the force command for driving wires is written as the following equation: (2) where is the torque command vector for the exoskeletal robot joints, is the force command vector for the driving wires, and is the Jacobian which relates the exoskeletal robot joint velocity to the driving wire velocity. Force control is carried out to realize the desired force ( ) in driving wires by the driving motors. In the fuzzy-neuro controller, 32 kinds of fuzzy IF-THEN rules are prepared to generate the desired torque of the exoskeletal robot. The architecture of the fuzzy-neuro controller is depicted in Fig. 6. Here means sum of the inputs, means multiplication of the inputs. Two kinds of nonlinear functions ( and ) are applied to express the membership function of the fuzzy-neuro controller. (3) (4) (5) (6) Fig. 9. Effect of the arm posture change. where is a threshold value and is a weight. The process of the fuzzy-neuro controller is the same as that of ordinal simplified fuzzy controllers. Consequently, the output of the fuzzyneuro controller is calculated with the following equation: where represents the output vector, denotes the degree of fitness of the rule, and is the weight for the rule. When the exoskeletal robot is attached to another human subject, or when physiological condition of the human subject is changed a lot, on-line adaptation of fuzzy-neuro controller is carried out by adjusting each weight of the fuzzy-neuro to minimize the amount of muscle activity and motion error which is given by the teaching equipment shown in Fig. 7. The angle of link-1 of the teaching equipment is supposed to correspond to (7)

6 130 IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 8, NO. 1, MARCH 2003 Fig. 10. Architecture of the intelligent interface. that of link-1 of the exoskeletal robot, and the angle of link-2 of the teaching equipment is supposed to correspond to that of link-2 of the exoskeletal robot. In the adaptation process, the human subject indicates his/her desired shoulder motion by demonstrating the same motion with the teaching equipment using his/her wrist. In this study, both the antecedent part and the consequent part of the fuzzy IF-THEN control rules are supposed to be adjusted to fit physiological condition of each human subject by using the back-propagation learning algorithm in online manner. The evaluation function for the fuzzy-neuro controller training is written as: (8) where is the desired shoulder angle indicated by the teaching equipment, is the measured shoulder angle, is a coefficient which changes the degree of consideration of the muscle activity minimization, is the desired muscle activity level in ch.i, and is the measured muscle activity level in ch.i. The assistance level of the robot can be moderately adjusted by changing the desired muscle activity levels. Note that certain desired muscle activity levels are prepared for each shoulder motion (i.e., vertical shoulder flexion/extension motion and horizontal shoulder flexion/extension motion) considering physiological muscle allocation. The inputs to the fuzzy-neuro controller are instantly adjusted by the modification coefficients outputted from the intelligent interface, which is explained in the next section, in accordance with the human subject s arm posture. The definition of membership functions of input variables to the fuzzy-neuro controller is adjusted immediately by multiplying the input variables by the modification coefficients. This operation makes the same Fig. 11. Experimental setup. effect as changing the membership functions wider or narrower [23] as shown in Fig. 8. IV. INTELLIGENT INTERFACE Human shoulder joint consists of many muscles and moves in 3-DOF (flexion-extension, abduction-adduction, and internal-external rotation). The muscle activity level can be described by the EMG signal. Even though the human subject tries to perform the same shoulder motion, the amount of the EMG signals from the shoulder muscles varies if the arm posture is changed because of physiological reason [20]. The displacement of the shoulder muscles is geometrically changed if the arm posture is changed.

7 KIGUCHI et al.: EXOSKELETAL ROBOT FOR HUMAN SHOULDER MOTION ASSIST 131 Fig. 12. Experimental results for Trajectory 1 with assist of the exoskeletal robot (standard arm posture). Fig. 13. Experimental results for Trajectory 1 without assist of the exoskeletal robot (standard arm posture). In this study, intelligent interface is proposed to take into account the subject s arm posture. The intelligent interface modifies the controller inputs by multiplying modification coefficients according to the subject s arm posture. A neural network is used to realize the intelligent interface. Preliminary experiment, which investigates the effect of subject s arm posture (the effect of shoulder vertical flexion/extension angle, shoulder horizontal flexion/extension angle, shoulder internal rotation angle, elbow flexion/extension angle, and forearm pronation/supination angle) with respect to the amount of EMG signals of shoulder muscles, was performed to prepare the training data of the neural network. An example of the effect the arm posture change is shown in Fig. 9. The neural network makes a nonlinear mapping between subject s arm posture and modification coefficients for the controller inputs by off-line learning. The architecture of the neural network is depicted in Fig. 10. This neural network realizes the intelligent interface between the human subject and the fuzzy-neuro controller. The neural network consists of three layers (input layer, hidden layer, and output layer). The input layer consists of five neurons, the hidden layer 50 neurons, and the output layer eight neurons. Sigmoid function is used for neurons in the hidden layer and the output layer. The input variables to the neural network are shoulder flexion/extension angle, shoulder horizontal flexion/extension angle, shoulder internal rotation angle, elbow flexion/extension angle, and forearm pronation/supination angle. The outputs from the neural network are the modification coefficients for input variables to the fuzzy-neuro controller (i.e., EMG signals of the shoulder muscles). The neural network has been trained with the training data (91,000 data set) obtained from the preliminary experimental

8 132 IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 8, NO. 1, MARCH 2003 Fig. 14. Experimental results for Trajectory 2 with assist of the exoskeletal robot (standard arm posture). Fig. 15. Experimental results for Trajectory 2 without assist of the exoskeletal robot (standard arm posture). results using the back-propagation learning algorithm in off-line manner. V. EXPERIMENT Experiment has been carried out with a health human male subject (22 years old) to evaluate the effectiveness of the proposed exoskeletal robot and its control method. The experimental setup is depicted in Fig. 11. The amplified EMG signals are sampled at a rate of 2 khz and the signals from the wire tension sensors are also sampled at a rate of 2 khz and low-pass filtered at 8 Hz. For the first experiment, the target following experiments have been carried out with and without assist of the exoskeletal robot in order to verify the controllability of the exoskeletal robot. When the experiment without assist of the exoskeletal robot was performed, the human subject wore the exoskeletal robot to measure the shoulder angle. We had experimentally verified that wearing the robot did not affect the EMG signals of the human subject. This experiment has been performed with the standard arm posture of the human subject (without changing the arm posture of the human subject). The initial control rules of the fuzzy-neuro controller were designed assuming the arm posture of the human subject was in standard. In the experiment, the target trajectory (Trajectory 1: both vertical flexion-extension and horizontal flexion-extension trajectory, Trajectory 2: vertical flexion-extension trajectory at the horizontal flexion angle 30 ) of shoulder is displayed on the monitor, and a human subject is supposed to make his shoulder angles follow it. The generated shoulder trajectory is supposed to be very close to the target trajectory if the exoskeletal robot is well controlled, and the EMG levels of shoulder muscles are supposed to be lower if the exoskeletal robot effectively assists the shoulder motion of the human subject. The experimental results (EMG signals at the anterior and middle part of deltoid) of the human subject for the Trajectory 1 with and without assist of the exoskeletal robot are shown in Figs. 12 and 13, and those for the Trajectory 2 with and without assist of the exoskeletal

9 KIGUCHI et al.: EXOSKELETAL ROBOT FOR HUMAN SHOULDER MOTION ASSIST 133 Fig. 16. Experimental results for Trajectory 2 without assist of the exoskeletal robot (changed arm posture). Fig. 17. Experimental results for Trajectory 2 with assist of the exoskeletal robot (changed arm posture) without the proposed interface. robot are shown in Figs. 14 and 15, respectively. These results show that the human subject can follow the target trajectories with and without support of the exoskeletal robot. This means the exoskeletal robot does not constrain the subject s shoulder motion. One can also see that the EMG levels of shoulder muscles become lower when the shoulder motion of the subject is assisted by the exoskeletal robot. Here, muscle activity level (MAV) at the anterior part of deltoid was reduced to 18% and 15% for the Trajectory 1 and 2, respectively. Muscle activity level (MAV) at the middle part of deltoid was reduced to 15% and 60% for the Trajectory 1 and 2, respectively. These results show the effectiveness of the proposed robot in human shoulder motion assist in the case when the arm posture of the human subject is in standard. When the arm posture of the human subject is changed (shoulder horizontal flexion/extension angle: 30, shoulder internal rotation angle: 0, elbow flexion angle: 90, and forearm pronation/supination angle: 0 ) from the standard posture, the EMG levels of shoulder muscles during the shoulder vertical flexion-extension motion at the horizontal flexion angle 30 (Trajectory 2) are changed as shown in Fig. 16. If we apply the exoskeletal robot for motion assist with the fuzzy-neuro controller designed for the standard arm posture without the proposed interface (neural network) to this case, the exoskeletal robot does not work very efficiently as shown in Fig. 17. In this case, the EMG levels of shoulder muscles are not so much improved and the target following becomes worse than those without assist of the exoskeletal robot. Here, muscle activity levels (MAV) at the anterior and middle part of deltoid were reduced to only 57% and 58%, respectively. However, if the fuzzy-neuro controller is modified by the proposed interface, the results become better as well as those in Fig. 14 as shown in Fig. 18. Here, muscle activity levels (MAV) at the anterior and middle part of deltoid were reduced to 18% and 31%, respectively. These results show the importance and effectiveness of the proposed interface.

10 134 IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 8, NO. 1, MARCH 2003 posture and modification coefficients for the fuzzy-neuro controller inputs was examined in the preliminary experiment and then off-line trained by a neural network to realize the intelligent interface. The effectiveness of the proposed method has been verified by the experiment with healthy human subjects. We would like to apply the proposed system to elderly persons and handicapped persons for the future research. REFERENCES Fig. 18. Experimental results for Trajectory 2 with assist of the exoskeletal robot (changed arm posture) with the proposed interface. VI. CONCLUSION In order to realize a practical and effective exoskeletal robot for shoulder joint motion assist, an effective fuzzy-neuro controller, a moving mechanism of the CR of the shoulder joint of the exoskeletal robot, and intelligent interface have been proposed in this paper. A moving mechanism of the CR of the shoulder joint of the exoskeletal robot has been proposed to fit the CR of the robot shoulder joint to that of the physiological human shoulder joint during the shoulder motion. An effective fuzzy-neuro controller has been also proposed to automatically control the robot with EMG signals of the human shoulder muscles. Furthermore, an intelligent interface between the human subject and the fuzzy-neuro controller has been proposed to cancel out the ill effects caused by subject s arm posture in this paper. The effective control of the exoskeletal robot for human shoulder motion assist can be expected by realizing the intelligent interface. A nonlinear map between subject s arm [1] K. Kiguchi, S. Kariya, K. Watanabe, K. Izumi, and T. Fukuda, An exoskeletal robot for human elbow motion support Sensor fusion, adaptation, and control, IEEE Trans. Syst., Man, Cybern., pt. 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11 KIGUCHI et al.: EXOSKELETAL ROBOT FOR HUMAN SHOULDER MOTION ASSIST 135 [22] B. Hudgins, P. Parker, and R. N. Scott, A new strategy for multifunction myoelectric control, IEEE Trans. Biomed. Eng., vol. 40, pp , Jan [23] K. Kiguchi and T. Fukuda, Intelligent position/force controller for industrial robot manipulators application of fuzzy neural networks, IEEE Trans. Ind. Electron., vol. 44, pp , Dec Kazuo Kiguchi (S 92 M 93) received the B.E. degree in mechanical engineering from Niigata University, Niigata, Japan in 1986, the Master of Applied Science degree in mechanical engineering from the University of Ottawa, Ottawa, ON, Canada, in 1993, and the Ph.D. degree in engineering from Nagoya University, Nagoya, Japan, in From 1986 to 1989, he was a Research Engineer with Mazda Motor Company and from 1989 to 1991, with MHI Aerospace Systems Company. From 1994 to 1999, he was with the Department of Industrial and Systems Engineering, Niigata College of Technology, Niigata, Japan. Currently, he is an Associate Professor in the Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University, Saga, Japan. His research interests include biorobotics, intelligent robots, machine learning, application of soft computing for robot control, and application of robotics in medicine. Dr. Kiguchi is a Member of the Robotics Society of Japan, IEEE Robotics and Automation Society, IEEE Systems, Man, and Cybernetics Society, IEEE Engineering in Medicine and Biology Society, IEEE Industrial Electronics Society and IEEE Computer Society, the Japan Society of Mechanical Engineers, the Society of Instrument and Control Engineers, the Japan Society of Computer Aided Surgery, International Neural Network Society, Japan Neuroscience Society, the Virtual Reality Society of Japan, the Japanese Society of Prosthetics and Orthotics, and the Japanese Society for Clinical Biomechanics and Related Research. He received the J. F. Engelberger Best Paper Award at WAC2000. Koya Iwami was born on November 23, He received the B.E. degree from Saga University, Saga, Japan, in 2001, where he is currently pursuing the M.E. degree in the Department of Advanced Systems Control Engineering. Makoto Yasuda was born on February 3, He received the B.E. degree from Saga University, Saga, Japan, in 2002, where he is currently pursuing the M.E. degree in the Department of Advanced Systems Control Engineering. Keigo Watanabe (S 83 M 90) received the B.E. and M.E. degrees in mechanical engineering from the University of Tokushima, Tokushima, Japan, in 1976 and 1978, respectively, and the D.E. degree in aeronautical engineering from Kyushu University, Fukuoka, Japan, in From 1980 to March 1985, he was a Research Associate at Kyushu University. From April 1985 to March 1990, he was an Associate Professor at the College of Engineering, Shizuoka University, Shizuoka, Japan. From April 1990 to March 1993, he was an Associate Professor, and from April 1993 to March 1998, he was a Full Professor in the Department of Mechanical Engineering, Saga University, Saga, Japan. Since April 1998, he has been with the Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering, Saga University. He has published more than 360 technical papers in transactions, journals, and international conference proceedings, and is the author or editor of 18 books, including Adaptive Estimation and Control (Englewood Cliffs, NJ: Prentice-Hall, 1991), Stochastic Large-Scale Engineering Systems (New York: Marcel Dekker, 1992) and Intelligent Control Based on Flexible Neural Networks (Norwell, MA: Kluwer, 1999). He is an Active Reviewer of many journals and transactions, and an Editor-in-Chief of Machine Intelligence and Robotic Control, and an Editorial Board Member of the Journal of Intelligent and Robotic Systems and the Journal of Knowledge-Based Intelligent Engineering Systems. His research interests are in stochastic adaptive estimation and control, robust control, neural network control, fuzzy control, and genetic algorithms and their applications to machine intelligence and robotic control. Dr. Watanabe is a Member of the Society of Instrument and Control Engineers, Japan Society of Mechanical Engineers, Japan Society for Precision Engineering, Institute of Systems, Control and Information Engineers, the Japan Society for Aeronautical and Space Sciences, Robotics Society of Japan, and Japan Society for Fuzzy Theory and Systems. Toshio Fukuda (M 83 SM 93 F 95) graduated from Waseda University, Tokyo, Japan, in 1971 and received the M.S. and Dr. Eng. degrees from the University of Tokyo, Tokyo, Japan, in 1973 and 1977, respectively. He also studied at the Graduate School of Yale University, New Haven, CT, from 1973 to In 1977, he joined the National Mechanical Engineering Laboratory, Tsukuba, Japan, and from 1979 to 1980, became a Visiting Research Fellow at the University of Stuttgart, Stuttgart, Germany. In 1982, he joined the Science University of Tokyo, Tokyo, Japan, and in 1989, joined Nagoya University where he is currently a Professor in the Department of Micro System Engineering, Department of Mechano-Informatics and Systems, engaging in the research of intelligent robotic systems, cellular robotic systems, mechatronics and micro- and nanorobotics. He is an author of six books, editor of five books, and has published over 1,000 technical papers in micro- and nanosystems, robotics, mechatronics, and automation. Dr. Fukuda has been a Fellow of the Society of Instrument and Control Engineers (SICE), since He received the IEEE Eugene Mittlemann Award in 1997, the Banki Donat Medal from the Polytechnic University of Budapest, Budapest, Hungary in 1997, the Medal from the City of Sartillo, Sartillo, Mexico in 1998, and the IEEE Millennium Medal, in He was the Vice President of the IEEE Industrial Electronics Society (IES) from 1990 to 1999, IEEE Neural Network Council Secretary since 1992, Vice President of the International Fuzzy Systems Association (IFSA) since 1997, President of IEEE Robotics and Automation Society President from 1998 to 1999, Editor-in-Chief of the IEEE/ASME TRANSACTIONS ON MECHATRONICS from 2000 to 2002, Director of the IEEE Division X from 2001 to 2002, and the Founding President of the IEEE Nanotechnology Council since 2002.