Study on Impedance Generation Using an Exoskeleton Device for Upper-Limb Rehabilitation *

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1 0 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) September 3-5, 0. Hamburg, Germany Study on Impedance Generation Using an Exoskeleton Device for Upper-Limb Rehabilitation * Zhibin Song, Shuxiang Guo, Senior Member IEEE Abstract Rehabilitation robotics has been one of the most important branches of robotics. Particularly, the exoskeleton device for the upper limb rehabilitation develops fast in recent years, but most of them are heavy and large. In this paper, we proposed a light and wearable exoskeleton device which is potential to be used in home rehabilitation. It should be able to perform passive and active training. In this paper, we proposed a method to implement the active rehabilitation based on the upper limb exoskeleton rehabilitation device (ULERD). It provides a wide approach for human machine interface (HMI) in which the device is high friction and non-backdrivable, and meanwhile it is difficult to obtain the contact force information directly. The method is to measure the motion of human body other than the motion of device. It is implemented with passive DoFs unlocked during elbow flexion and extension performance. In contrast experiments, three level resistances are generated and provided to the user. The surface electromyography (semg) signals detected from biceps and triceps were recoded and processed to evaluate the effect of this method via wavelet packet transform (WPT). I. INTRODUCTION Stroke is a leading cause of disability in the United States, affecting an estimated 6.4 million Americans []. Traditional rehabilitative therapies depend on the therapists experience and need lots of therapists, which overburdens family and society []. With the development of robotics, some rehabilitation robots appeared to help stroke survivors to recover motor function. Thereunto, the robots used for upper limb rehabilitation mainly differentiated into exoskeleton and end-effector types. In the end-effector category, the user grasps the end-effector (handle) of the robot. The typical robot is MIT-MAUNS. It allows two degrees of freedom for movement of upper limbs including wrist, elbow and shoulder movements by performing task-oriented training [3], [4]. Other upper limb rehabilitation devices of end-effector strategy were developed based on different characters [5]-[9]. This kind of robots can not target specific joints of limbs. Obviously, the exoskeleton strategy can solve this problem. One of the typical devices MEDARM, developed by the Canadian Institutes of Health Research (CIHR), used a cable driven curved track mechanism which provides independent control for all major degrees of freedom (DOFs) [0]. ARMin [] is an exoskeleton device with six independently *This research is supported by Kagawa University Characteristic Prior Research fund 0. Zhibin Song, Hayashi-cho, Takamatsu, , Japan. Dept. of Intelligent Mechanical Systems Eng g, Kagawa University, ( song@eng.kagawa-u.ac.jp). Shuxiang Guo, Hayashi-cho, Takamatsu, , Japan. Dept. of Intelligent Mechanical Systems Eng g, Kagawa University, Japan: ; fax: ; guo@eng.kagawa-u.ac.jp. actuated DoFs and one coupled DoF, which can greatly improve motor function of the paretic arm for some stroke patients, even those patients in a chronic state []. However, most existing rehabilitation robots are heavy and large and not suitable for home-rehabilitation. In our research, a novel light and wearable exoskeleton device was designed and developed aiming at for home-rehabilitation. Rehabilitation robots for upper limb are unlike with other HMI devices due to they should execute training strategies in clinic regardless of end-effectors or exoskeletons following evidence based medicine. According to the outcome of researches in neuro-rehabilitation [3-6], there are three training strategies of physical rehabilitation: passive rehabilitation, active rehabilitation and bilateral rehabilitation [9], [7] and [8]. The rehabilitation robots mentioned should not only perform one or several kinds of strategies, but also adjust train level of these strategies according to individual impairment. Generally speaking, the patients following weak stroke could perform passive rehabilitation strategy reasonably and the mild stroke survivors could obtain better effect to perform active rehabilitation. Hemiparalysis patients tend to perform bilateral rehabilitation. In our previous work, we discussed the implementation of passive rehabilitation and bilateral rehabilitation using the ULERD and we also did some preparation for active rehabilitation focusing on elbow joint [9]. In this paper, we proposed an implementation method to execute the active rehabilitation which is suitable for the ULERD. Considering the special characteristic of wearing a portable exoskeleton device, it is difficult to obtain the contact force between the user and device using the force sensor or other sensors, therefore, semg signals derived from relative muscles are acquired and analyzed to evaluate the execution of active rehabilitation which is performed by unimpaired subjects. To implement this kind of rehabilitation strategy using an electro-mechanical system, there are two fundamental control methods categorized by different inputs and outputs [0]. One is impedance control, in which motion input by the user is measured and force is fed back to the user. Alternative method, in which forces exerted by a user are measured and the device will react with the proper displacement, is called admittance control. Impedance control requires nature lightly built and highly backdrivability, which was adapted by many commercial haptic devices. Its performance is lacking in the region of higher forces, high mass and high stiffness and it difficult to add complex end effectors [0]. On the other hand, admittance control devices allow considerable freedom in the mechanical design of the device, because backlash and tip inertia can be eliminated. Admittance control can be adapted where the contact force between human and device can be detected accurately. In our study, the ULERD adapts high ratio gearhead, so it is not //$ IEEE 8

2 backdrivable. Meanwhile, it is also of multi-dofs to assist or resist multi motions of upper limb with mounted on the upper limb, so accurate contact force between human and device can not be obtained easily by force sensors. Therefore, these two methods are not suitable for the ULERD. Therefore, in this paper, a novel method was proposed to detect the motion of human upper limb related to the ULERD by creating elastic contact condition between them, which can be considered as an elastic extension of human muscles. The similar method was adapted in Serial Elastic Actuator (SEA), which provides a linear motion detection using serial elastic springs []. In this paper, we focused on generating resistance to human forearm around elbow joint by detecting its rotation motion. To demonstrate the feature of the system, experiments were carried out with a healthy subject. Testing with real patients in medical center is being planned. In experiments, evaluation of methodology is performed by detecting and processing the semg signals from biceps and triceps muscles because semg signals can assess the motion of human body veritably. In the next section, we first introduce overview of the developed ULERD. The control methodology is presented in the Section III. Section IV presents the conducted contrast experiments of different structural setting and evaluation of control methodology by processing semg signal derived from relative muscles. Finally the paper ends with the discussion and conclusions in Section V and Section VI respectively. application for some patients. For example, the gravity force on the forearm and wrist parts cannot be compensated for completely, so the device must be supported by the human forearm to some degree during active training. Considering that patients who actively train have a certain ability to adjust their upper limbs, passive DoFs are locked during active training, which enhances the resistance stiffness generated and improves the active rehabilitation effect. A group of contrasting experiments was conducted to show the effect of active training with and without passive DoFs. Passive DoFs can correct misalignment between the human body and ULERD, particularly during passive training. Although some components of the gravity force on the forearm part of the ULERD cannot be compensated for completely, the ULERD can also assist patients to perform required tasks. The typical integration structure of passive and active DoFs in the elbow joint will be analyzed in detail in the following section. II.OVER VIEW OF ULERD DEVELOPMENT A. System description The system developed to implement upper-limb active training includes a HMI and a robot manipulator (ULERD). The predefined task is shown on a computer screen using a graphical user interface (GUI). The developed GUI guides a user to effectively perform the tasks. The description of the hardware for the system is shown in Figure. An inertia sensor mounted on the user s wrist was used to detect motion of the forearm, and semg signals from the biceps and triceps muscles were used to evaluate the proposed method. B. ULERD design The motivation for the ULERD design was to provide passive and active training for patients with motor dysfunction to recover upper-limb motor function including the elbow and wrist joints. We also aimed to make the device wearable and portable for home rehabilitation. The basic design structure of the ULERD from the upper view is depicted in Figure. Three active DoFs were designed into the elbow and wrist, including elbow flexion/extension, forearm pronation/supination, and wrist flexion/extension. These three DoFs are both actuated and sensorized. Four passive DoFs were added, including two DoFs (one for rotation and the other for translation) in the elbow joint and two in the wrist joint after considering many factors, e.g., variation in the flexion/extension axis [3], personalized otherness in the physical dimension of the joint, and correlation between the wrist and elbow joint during elbow flexion and extension. Two passive rotational DoFs were sensorized with potentiometers, and they can be set to be effective and ineffective based on the case. C. ULERD kinematics The aim of the passive DoF design was to correct misalignment between the human upper limbs and ULERD. It also compensates for faulty rotation of human joints. However, it is not an ideal Figure. Schematic diagram of the system hardware. Figure. Upper view of the ULERD. Figure 3. A user wearing the ULERD. 8

3 Figure 4. Schematic model of the ULERD elbow joint. Figure 4 shows a schematic model of the ULRED elbow joint with passive DoFs. In this figure, a rotational DoF located at O is actuated, and two passive DoFs are rotational and translational DoFs, respectively, located at point A. We suppose that the upper arm is fixed in the y-axis. O is the rotational axis of the device, O is the rotational axis of the user s elbow joint, and the forearm is fixed on AB. In this figure, a crank and rocker mechanism is formed where point O does not overlap O. CD is the center disparity of the elbow joint axis on the sagittal plane during elbow flexion and extension. From Figure 6, we obtain basic relationships between the device and the human elbow joint as the following (Eq. and Eq. ). l l l () O A AO OO 3 () where is the angle from AO to the x-axis; is the angle from AO to the x-axis, which can be obtained through the inertia sensor; and 3 is the angle from AO to AO ; its detection method is presented below. Eq. is shown as a complex function in Eq. 3, l exp( i ) lao exp( i ) x yi (3) O A and then we can obtain Eq. 4 and Eq. 5, lo A cos l AO cos x (4) lo A sin lao sin y (5) III. CONTROL METHODOLOGY In the ULERD, the high ratio gearhead results in no backdrivability, but it covers human limbs closely, which results in difficulties obtaining an accurate contact force. A similar circumstance was mentioned in ref. [4], in which the exoskeleton device can follow human motion by forcing the motor to rotate over low angles, which is detected by hall sensors. It could perform tracking with human motion but the transparency of the system is low. In this study, elastic components were used to provide for detecting human motion in a HMI system in which the device has no backdrivability. Similar components were proposed and used in a serial elastic actuator [5], in which linear elastic springs were used to detect motion of the load. In this study, elastic materials were used to detect the relative rotation of the user s forearm with respect to the upper arm. Based on the proposed methods, variable resistance can be provided to the user s upper forearm, such as a spring model, damper mode, or integration mode, and even water resistance. The experiments conducted using the proposed method focused on the elbow joint. The control system scheme is shown in Figure 5. Human limb motion information was obtained from an inertia sensor, including position, velocity, and acceleration. These parameters were calculated based on the motion of the virtual model and were then sent to motor controllers as input. Because the proposed method mainly refers the relative displacement between the human limb and the device, the motors were driven in position with a closed-loop velocity control and an inertia sensor and encoders using a proportional integral derivative (PID) control algorithm. To evaluate the effect of the proposed method, semg signals from the biceps and triceps muscles were recorded during elbow flexion and extension. Figure 6 shows the schematic model of resistance with passive DoFs unlocked, in which not only spring models are used but also dampers. Although no damper components were used in this system, the resistance effect of dampers was obtained by adjusting the elastic belts. A. Control methodology with passive DoFs unlocked Flexion motion. During flexion motion, the force exerted on the user s forearm can be calculated with respect to spring S and damper D using Eq. 6. Because the motor is driven in closed-loop velocity and position control, the output torque was adjusted automatically, F k ) l c ( (6) where k is the coefficient of spring S ; c is the coefficient of damper D, which can be set in different virtual environments; is the angle between the user s forearm and horizontal plane, which is detected via an inertia sensor; is the angle between the forearm frame and horizontal plane; is the angle velocity of the user s forearm; and F is the virtual resistance to the user when the user moves his or her arm. Because the damper effect is generated by adjusting elastic belts, F c is the actual resistance to the user and l is the length from the elastic belt to the elbow axis. The angle (Eq.7) between the forearm frame and horizontal plane is obtained with Eq. 6. ( c F ), (7) k l According to the elbow joint mechanism of the device (Figure 4), the rotational angle of the elbow joint was obtained with Eq. 8., (8) where is the angle of the elbow joint of the device and is the angle of the passive rotational joint that can be detected by a 83

4 potentiometer. The motor can be controlled based on Eq. 9, which was derived from Eq. 7 and 8, ( c F ), (9) kl Because the relative displacement of the user s forearm to the frame of the device is the main factor according to Eq. 7, traditional PID control was adapted based on closed-loop displacement. Extension motion. The force analysis during flexion motion is not the same or the reverse of that during extension motion due to the passive DoFs. During extension motion, the force exerted on the user s forearm can be calculated with respect to spring S and damper D according to Eq. 0, F k ( ) l c, (0) where is the initial angle of the passive rotated joint, l is the length from the elastic belt to the axis of the passive joint, c is the coefficient of damper D, and k is the coefficient of spring S. During extension motion, the user s forearm contacts with the frame of the device, so the following equation can be obtained:, () Eq. can be obtained according to Eqs. 8, 0, and, ( F c ), () k l where F c is the real resistance on the user s limb and the target of control is to find the (or ) to meet the resistance by using the closed-loop position control. Because is detected by a potentiometer that is not actuated, is discussed as one variation in the following section. Figure 5. Control scheme for the system. Figure 6. Schematic model of resistance in the elbow joint with passive degrees of freedom (DoFs) unlocked. Figure 7. The virtual environment of the experiment. Figure. 8. Experimental setup for surface electromyographic (semg) signal acquisition. IV. CONTRAST EXPERIMENTS AND RESULTS A. Guide-user interface based on virtual reality A three-dimensional interface was created using OpenGL. Two virtual upper limbs were created in the virtual environment (Figure 7). One limb was a tracked virtual arm that moves at a programmable speed within the range of motion of the user s limb, and the other was a manipulated virtual arm that showed the motion of the user s limb. The experiment required the user to manipulate the ULERD to make the manipulated virtual arm follow the tracked virtual arm during elbow flexion and extension. During this experiment, the virtual force was programmed and a certain resistance was exerted on the user. B. semg signal acquisition and processing Surface EMG, which has a relationship with muscle activation, is widely used to estimate muscle torque and to drive exoskeletal devices or limb prostheses [7] [9]. In this study, the skin semg was adapted to detect activation of muscles to evaluate performance of the user s limbs during the experiment. Because distinguishing muscle activation using raw semg signals is difficult, the semg data were processed. SEMG signals from the biceps and triceps muscles were acquired to assess elbow flexion and extension. These signals were recorded using -mm-diameter bipolar surface electrodes located 8 mm apart at a sampling rate of 000 Hz (Figure. 8). A reference electrode was attached to the body where no muscles exist as a ground signal. Sampled data were preprocessed with a commercial semg acquisition and filter device (Osaka Electronic Device Ltd., Osaka, Japan) with eight channels read to the processing program at a sampling rate of 000 Hz (as most EMG signal frequency power is 0 50 Hz) through an AD sampling board (PCI365; Interface Co., Chiba, Japan). The subject s skin was shaved and cleaned with an alcohol swab to ensure good contact. Hardware and software filters were used due to noise generated by the machine. A hardware filter can be implemented via a filter box to eliminate electrical interference noise. The 84

5 software filter was implemented using wavelet transform packet (WTP) via Matlab (Natick, MA, USA) because WTP has some advantages compared to a noise filter and can also be used as a feature extraction tool. Daubechies was selected as the mother wavelet, and the detail coefficients at the fourth decomposition level were used from refs. [30] [3] to exact semg signal features. Different from other studies, the wavelet entropy was not adapted in this study because a statistical method (integrated value) was used. The WTP coefficients were integrated with 00 samples. C. Experiments and results One healthy subject (8 years old) participated in this experiment. Elbow flexion and extension motion are typically required to evaluate the proposed method using ULERD. The subject was required to perform three levels of experiments with passive DoFs unlocked. The displacement of the user s forearm and the forearm frame of the ULERD were based on and. And it included three levels of experiments. The first level (L) was elbow flexion and extension with no resistance ( 0 ); the second level (L) was elbow flexion and extension with lower resistance, which was set as 3.0. The last level (L3) was conducted to provide higher resistance, which was set as 6. 0 resistance. During the performance at each level, two semg signal channels from the elbow joint (e.g., biceps and triceps) of the user were monitored and used as performance indicators. Because of the tremble in velocity, the damper coefficients c and c were set to.0 Ns/rad. Each level was performed five times. Figure 9 shows typical results of the contrast experiments L. In this figure, we show that (a) is the motion of the user s forearm and the ULERD during L and that (b) is the integrated value of the WPT coefficients of the semg signals derived from the triceps and biceps muscles. The values at about the fourth second are higher because the forearm was moving to the horizontal plane, and the biceps were activated. (a) (b) Figure 9. Typical results of L contrast experiments. (a) (b) Figure 0. Typical results of L contrast experiments. (a) (b) Figure. Typical results of L3 contrast experiments. Figure. The mean wavelet transform packet (WTP) coefficients of the surface electromyographic (semg) signals. Figure 0 shows the typical results of the L experiments. In this figure, low resistance was provided to the user s forearm by setting the relative displacement between the user s forearm and the ULERD ( 3. 0 ). From Figure 0(a), a bulge occurred in the motion trajectory of the ULERD because the displacement between the user s forearm and the ULERD changed, in which flexion motion changed to extension motion. In (b), a peak appeared at the fourth second, which was perhaps induced by misalignment between the user s forearm and the ULERD. The magnitude of the WPT coefficients of the semg signals derived from the biceps in (Fig. 0-b) was higher than that in (Figure.9-b), particularly during flexion motion. In contrast, the magnitude of the WPT coefficients of the semg signals from the triceps in (Figure. 0-b) was not higher than that in (Figure.9-b) during extension motion obviously. Figure shows the typical results of the L3 contrast experiments. In this figure, high resistance was provided to the user s forearm by setting the relative displacement between the user s forearm and the ULERD ( 6. 0 ). Similar to activation of the biceps in the previous figures, the magnitude of the WPT coefficients of the semg signals from the biceps during flexion increased and then decreased during extension motion in (b). The magnitude of the WPT coefficients of the semg signals from the triceps in (Figure. -b) was higher than that in (Figure. 0-b) during extension, which indicates that the higher resistance is put on the forearm during extension. Figure shows the mean WPT coefficients from processing the biceps and triceps semg signals in the three level experiments. The value of the biceps enlarged obviously from L to L3 during flexion. And the value of the triceps enlarged obviously from L to L3 during extension. The reason that the value of the triceps from L to L did not increase is caused by the passive DoFs according to the observation of experiments. 85

6 V.CONCLUSION An upper-limb exoskeleton rehabilitation device was proposed and developed for home rehabilitation. Due to the lack of backdrivability and accurate detection of contact force between the human and the device, a method that detects the motion of the human forearm instead of the device was proposed and implemented with DoFs unlocked. The structure of the passive DoFs was designed to correct misalignment between the human and the device to allow more comfort to the user; however, it reduced device stiffness so that active training could not be implemented completely according to the experimental results, particularly with the high resistance required. Contrast experimental results indicated that the proposed method of exerting resistance could be implemented in three different levels. This method was effective to generate variable resistance for active training by using the ULERD and will be further tested for home rehabilitation in future work. 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