Intel Serv Robotics (2013) 6:181–189 DOI 10.1007/s11370-013-0135-5
ORIGINAL RESEARCH
Implementation of various control algorithms for hand rehabilitation exercise using wearable robotic hand Useok Jeong · Hyun-Ki In · Kyu-Jin Cho
Received: 8 April 2013 / Accepted: 21 August 2013 / Published online: 26 September 2013 © Springer-Verlag Berlin Heidelberg 2013
Abstract In this paper, the control algorithms for strength exercise using wearable robotic hand are reviewed and the experimental results are analyzed and discussed. The SNU Exo-Glove is a soft exoskeleton that actuates motor function in disabled hands. This new type of device comprises a jointless simple mechanical structure and is actuated with wires. The strength exercise algorithms include isotonic, isokinetic, and impedance control exercises. An electromyography (EMG) regulation algorithm is proposed to limit the maximum level of activation of the muscles to prevent injury of the muscles and joints. The tension of the wire and the sEMG signal are analyzed to validate the effectiveness of rehabilitation with SNU Exo-Glove. Keywords Wearable robotic hand · Soft exoskeleton · Rehabilitation robot · Isotonic · Isokinetic · Impedance control
1 Introduction Recently, many types of wearable robots have been developed for various purposes. Theses robots, when worn, can provide additional functions or superior functions to humans. These wearable robots can assist humans performing various tasks because the robots use various types of sensors and actuators to provide diverse information on the surrounding environment and larger power than humans. Wearable robots are mainly developed for assisting humans in their everyday U. Jeong · H.-K In · K.-J. Cho (B) School of Mechanical and Aerospace Engineering/IAMD, Seoul National University, Seoul 151-742, Korea e-mail:
[email protected] URL: http://biorobotics.snu.ac.kr
life and for rehabilitation exercise [1–4]. Particularly, wearable robots for hands have been developed [5–8]. The main disadvantage with the previous designs of the exoskeleton is its bulky mechanical structure. Whether the exoskeleton robots are used for assistance or rehabilitation exercise, bulky structure of the robot can make the user uncomfortable and put on the unit frequently. In addition, the bulky structure can make the exterior of the robot look awkward, and users who do not like to seek unnecessary attention avoid using the device. A soft exoskeleton has been developed to solve this problem [9,10]. The soft exoskeleton is basically made of a flexible cloth and its basic concept is the tendon-driven mechanism. The SNU Exo-Glove (Fig. 1) is developed to assist people who are hand-disabled. It can be used as assistive device for everyday life or as a rehabilitation device. There are three advantages of using SNU Exo-Glove as a rehabilitation device compared with the previously designed rigid-type exoskeletons. First, as a type of soft exoskeleton, the SNU Exo-Glove comprises a simple structure with wires and is easy to wear just like gloves for casual wear. Second, the under-actuated mechanism allows adaptive distribution of torques at each joint with simple control. Third, it has wide range of motion because there are no rigid parts at the palm side of the glove. The purpose of the control in SNU Exo-Glove is for stable grasping and securing safety. Due to the under-actuated mechanism of the SNU Exo-Glove, the exact position control of each wire is not required. Instead, the interaction control is used to prevent abrupt change in contact force and ensure safety of the grasping. Impedance control is well-used control method for the wearable robot. The impedance control method is chosen as the basic control algorithm for grasping in SNU Exo-Glove, because of its safety when the robot physically interacts with human.
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Fig. 1 Soft exoskeleton for hands, SNU Exo-Glove Fig. 2 Schematic of SNU Exo-Glove, structure of index finger in sagittal plane (left), and wire routing mechanism on palmar side (right)
There can be other control method for the purpose of exercise rather than assisting grasping. There are various types of strength exercise programs. Among them, are the methods that use rehabilitation robots, such as isotonic, isokinetic, and impedance control exercises [11–14]. Isotonic and isokinetic contraction exercise is known as effective in strength rehabilitation exercise because this induces eccentric muscle contraction increasing the maximal strength [16]. The rehabilitation exercise using impedance control has its significance that it can provide various stimulations to muscles with different combination of impedance parameters [13– 15]. The intensity of the exercise can be adjusted by varying the control gain of the exercise program controller. In this paper, the control algorithms for strength exercise for hands using wearable robotic hand, SNU Exo-Glove, are reviewed and the results of the experiment are analyzed.
2 SNU Exo-Glove The joint structures are avoided and the actuators are spaced through wires to avoid the complex and bulky structure of the device. Anatomically, the fingers of the hand consist of bones and tendons to actuate the joints. There is no need for additional collinear joints unlike in previously developed exoskeletons, because of the presence of joints between the bones. Wearable artificial tendons that are controlled with a specific algorithm form the concept of the SNU Exo-Glove. Figure 2 shows the routing mechanism of the tendons in the SNU Exo-Glove. It comprises an antagonistically actuated mechanism with an extension wire and a flexion wire. The flexion of the thumb is actuated with motor 1, while the flexion of the index and middle fingers is actuated with motor 2. There is a differential mechanism between the index finger and the middle finger, which aids in grasping an object adaptively. The extension of the thumb and the index and middle fingers is actuated with motor 3. The system mainly consists of an Exo-Glove unit, an actuation module, and a controller, as shown in Fig. 3. The control algorithm is implemented in a Single-Board RIO (National Instruments) real-time controller with a loop time
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of 3 ms. The input and output signals are preprocessed on an field-programmable gate array (FPGA) board. The actuation module comprises brushless motors (Faulhaber), velocity controlled drivers (Faulhaber) and custom-made tension sensors with force sensors (Kyoto).
3 Control algorithm In this chapter, three exercise control algorithms are proposed: isotonic, isokinetic and impedance control algorithms. The EMG regulation algorithm for each of the three exercise control algorithms is proposed. This algorithm is required when it is necessary to limit the intensity of the exercise for the safety. Although the tension of the wire can be used as the index of the intensity of the exercise, from the physiological point of view, EMG can provide more information on the intensity of the exercise. 3.1 Isotonic exercise Figure 4 shows the control algorithm and schematic of isotonic exercise. The concept of the isotonic exercise is to allow the muscles to exert constant forces regardless of the speed of the motion by controlling the tension of the extension wire. The constant wire tension cannot guarantee constant torques at each joint; however, the under-actuated mechanism enables adaptive distribution of the moments and avoids the need to precisely control each joint. The EMG regulation algorithm for the isotonic exercise is described in (1). When the root mean square (RMS) of the EMG, ERMS, is lower than the threshold, E thresh , the reference force of the controller is equal to the desired force, Fd , which is determined by the user. When ERMS is higher than E thresh , the desired force of the controller decreases proportionally with respect to the difference between E RMS and E thresh . This EMG-based reference force reshaping algorithm can prevent excessive activation of the muscles and can prevent muscular injury due to overtraining.
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Fig. 3 Block diagram of control system
Fig. 4 Control algorithm and schematic of isotonic exercise
Fref =
(E RMS < E thresh ) Fd Fd − kF (E RMS − E thresh ) (E RMS ≥ E thresh )
(1)
The resultant reference force as described in (1) is controlled by the velocity-based PI controller, which is described in (2). vref = kP (Fref − F) + kI ∫(Fref − F) dt
(2)
3.2 Isokinetic exercise Figure 5 shows the control algorithm and schematic of the isokinetic exercise. The objective of this exercise is to allow the muscles to exert forces at constant speed with a wide range of motion (ROM). The speed of the extension wire is controlled to be constant to allow flexion of the fingers at constant speed. The EMG regulation algorithm for the isokinetic exercise is described in (3). When the RMS of the EMG exceeds the threshold value, the wire is released at high speed, which is proportional to the difference between E RMS and E thresh .
The high releasing speed of the wire can reduce the activation level of the muscles to prevent muscular injury. The relationship between the releasing speed of the wire and the activation level of the muscles is described in detail in Sect. 5. (E RMS < E thresh ) vd (3) vref = vd + kv (E RMS − E thresh ) (E RMS ≥ E thresh ) The device operates only when the user exerts forces higher than a certain threshold force, thereby preventing the device operation when the user does not intend to do work. This mechanism is described in (4). 0 (F < Fthresh ) ∗ = (4) vref vref (F ≥ Fthresh ) 3.3 Impedance control exercise Figure 6 shows the control algorithm and schematic of the impedance control exercise. The impedance controller [17] enables the robot to respond with desired dynamics
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Fig. 5 Control algorithm and schematics of isokinetic exercise
Fig. 6 Control algorithm and schematic of impedance control exercise
when it interacts with the environment. The method that is adopted in this algorithm is the admittance control method [18], the working principle of which is the same as that of the impedance control method; however, uses a positionor velocity-based low-level controller. With this admittance controller, the robot acts as a virtual mass–spring–damper system, as illustrated in the schematic diagram in Fig. 6. The level of the exercise can be controlled with the combination of different values of mass, stiffness of the spring, damping constant, and equilibrium position of the spring. The admittance controller is described in (5).
tem. Iref denotes above three variables and xref denotes reference position of the PD controller. The concept is similar to the concepts of the isotonic and isokinetic algorithms. When the RMS of the EMG exceeds the threshold value, the impedance mass, spring and damping decrease proportionally with respect to the difference between the RMS value and the threshold value to decrease the force resistance of the device in order to lower the muscular activation level. (E RMS < E thresh ) I (6) Iref = d Id − kI (E RMS − E thresh ) (E RMS ≥ E thresh )
Mref x¨ref + Cref x¨ + K ref (xref − xeq ) = F
4 Experimental setup
(5)
The EMG regulation algorithm for the impedance control exercise is described in (6). Here, Mref , Cref and K ref denote mass, damping and spring coefficient of the impedance sys-
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Experiments are conducted to validate the usefulness of the proposed control algorithms with the SNU Exo-Glove. Figure 7 shows the overall experimental setup of the system.
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Fig. 9 Comparison of range of motion obtained with hand gripper and with Exo-Glove Fig. 7 Experimental setup for EMG measurement
Only the extension wire on the index and middle fingers is used to provide the resistance force while grip strength exercise. The sEMG of the flexion muscles (brachioradialis and flexor digitorum superficialis) are measured to measure the muscle effort of grasping. The tendons from flexor digitorum superficialis are connected to the fingers of palm side and responsible for the flexion of the fingers. The EMG of brachioradialis is referenced to consider the signal cross-talk between muscles and range of activation level. The raw data are collected using PolyG-I (Laxtha), with a sampling frequency of 256 Hz. In this experiment, the RMS of the EMG signal is calculated with 50 samples of the raw signal. The markers are attached to the joints and a video is recorded with 24 FPS, which is sufficient to measure the motion of the finger, to measure the joint angle of each finger. The video is analyzed with ProAnalyst (Xcitex) to calculate the angle between the markers (Fig. 8).
5 Results 5.1 Range of motion A hand strength gripper is used to compare the ROM obtained with the traditional device and with the SNU Exo-Glove. Figure 9 and Table 1 show the experimental results of ROM.
As expected, the ROM of each joint obtained with the ExoGlove is wider when compared with the traditional strength gripper. Table 1 summarizes the minimum and maximum joint angles of each joint and its ROM. In the case of ExoGlove, the maximum angles of the MCP and PIP joints are greater and the minimum angles of the MCP and PIP joints are lower, when compared with the gripper. Although the angle of the DIP joint of the glove is greater because the glove is thicker on the palmar side of the finger, the ROM is wider compared with the gripper. With the Exo-Glove, all the three joints of the finger have wider ROM and can provide a wider range of exercise motion, which leads to more effective strength exercise. 5.2 Isotonic exercise The results of the isotonic exercise without and with the EMG regulation algorithm are shown in Fig. 10 for one cycle (flexion and extension) with a desired wire tension of 20 N. The first and second rows of the graph correspond to the position and tension of the extension wire, respectively. The third row of the graph corresponds to the measured raw sEMG signal of the muscles and it’s RMS for 50 samples. As the user tries to flex the finger, the isotonic controller releases the wire to maintain the tension of the wire (0–1 s). At this stage, we can observe from the EMG data an increase in the muscle activation level. After the hand is fully flexed, the user releases the grasp and the wire attains its original position and the muscle activation level decreases after completing one cycle of exercise (1–3 s).
Fig. 8 Experiments for comparison of the range of motion, Gripper (left) and Exo-Glove (right)
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Table 1 Minimum and maximum angles of each joint and its range of motion Joint/value
Gripper
Exo-Glove
MCP joint Min.
134 deg
123 deg
Max.
155 deg
178 deg
Diff.
21 deg
55 deg
PIP joint Min.
105 deg
101 deg
Max.
123 deg
167 deg
Diff.
18 deg
66 deg
DIP joint
5.3 Isokinetic exercise
Min.
116 deg
159 deg
Max.
120 deg
219 deg
Diff.
4 deg
60 deg
Figure 10b shows the working principle of the EMG regulation algorithm. When the RMS of the EMG signal exceeds the threshold value (indicated in the third row of the graph), the reference tension in the tension controller decreases (the second row of the graph). As the results show, the tension of the wire decreases (the second row of the graph) and the muscular activation level decreases (the third row of the graph), thereby preventing excessive contraction of the muscles. The possibility of an injury to fragile muscles can be prevented in Fig. 10 Results of isotonic exercise: a without EMG limit algorithm, b with EMG limit algorithm
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patients with stroke and neuromuscular diseases. In Fig. 10b, the EMG signal does not increase with the increment of wire tension, because of the mechanical limitation of the structure; the obstruction from the environment can disturb the tension of the wire. The relationship between the tension of the wire and the muscular activation level is summarized in Table 2. The increased tension results in increased intensity of the exercise. By varying the desired tension of the wire, the intensity of the exercise can be varied to meet the objective of the strength exercise program.
The results of the isokinetic exercise with a desired wire velocity of 2.0 mm/s are shown in Fig. 11. Compared with the isotonic exercise, in which the tension of the wire is maintained constant, in the isokinetic exercise, the speed of the Table 2 RMS of EMG signals from isotonic exercise Tension of the wire (N)
RMS of EMG signals
5
0.0568
10
0.0586
15
0.0959
20
0.1189
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Fig. 11 Results of isokinetic exercise: a without EMG limit algorithm, b with EMG limit algorithm
wire is maintained constant, leading to constant flexion speed of the finger. The user exerts grasping forces as the wire releases at constant speed. The tension of the wire reaches the highest value at the start of the grasping motion and decreases as the wire releases and the fingers flex. Further, the RMS of the EMG shows constant activation of the muscles until the midpoint of the grasping motion, and the activation gradually increases until the end of flexion. The effectiveness of the EMG limit algorithm is described in Fig. 11b. As the RMS of the EMG exceeds the threshold value of 0.034 at 2.5 s, the velocity of the wire increases to decrease the activation level of the muscles. From this result, we can observe that the RMS value of the EMG is not as high as that obtained without the EMG regulation algorithm. The threshold of the limit algorithm can be set based on the condition of the muscles of the user. The correlation between the speed of the wire and the muscular activation level is summarized in Table 3. The results show that the lower the speed of the grasping motion, the higher is the muscular activation level. We can vary the speed of the wire to vary the intensity of the isotonic exercise. 5.4 Impedance control exercise The experimental results of the impedance control exercise with a desired mass of 20 kg, damping coefficient of
Table 3 RMS of EMG signals from isokinetic exercise Speed of the wire (mm/s)
RMS of EMG signals
2.5
0.0984
5.0
0.0585
7.5
0.0561
10.0
0.0539
150 N s/m, and stiffness coefficient of 150 N/m are plotted in Fig. 12. In the impedance control exercise mode, the dynamics of the virtual system is controlled. The results show that at the flexion stage, the RMS value of the EMG increases compared with the extension stage, and at the end of flexion, there is a greater increase in the EMG level, as observed from the previous results. The EMG limit algorithm for the impedance control exercise is described in Fig. 12b. In the same manner as that of the previous EMG regulation algorithms of the isotonic and isokinetic exercise, as the EMG level exceeds a certain threshold value, the control parameter varies to decrease the intensity of the exercise. The mass, stiffness coefficient and damping coefficient are decreased proportional to the excessive RMS value of the EMG signal. The relationship between the intensity of the impedance control exercise and the admittance controller parameters is
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Fig. 12 Results of impedance control exercise: a without EMG limit algorithm, b with EMG limit algorithm
Table 4 RMS of EMG signals from impedance control exercise Impedance of the wire
RMS of EMG signals
Case 1 Mass
5 kg
Spring
50 N/m
Damping
50 N s/m
0.0474
Case 2 Mass
10 kg
Spring
100 N/m
Damping
100 N s/m
0.0852
summarized in Table 4. As expected intuitively, for higher stiffness of the wire, a higher force is required for the same displacement. Likewise, a higher damping coefficient requires a higher force to vary the displacement of the wire. In case of the mass, generally, higher forces are required to accelerate and decelerate the object that has a higher mass value. However, because of the dynamic effect, the higher mass value decreases the intensity of the exercise at some point. The correlation between the impedance and the intensity of the exercise is described in detail in [15].
Case 3 Mass
20 kg
Spring
150 N/m
Damping
150 N s/m
0.1041
6 Conclusion
0.1383
In this paper, we have proposed a control algorithm for hand strength exercise using a new type of soft exoskeleton, SNU Exo-Glove. There are three advantages of using Exo-Glove in rehabilitation exercise. The first advantage is its simple design. The flexible structure and simple mechanism of the glove allows comfortable use of the rehabilitation device. The
Case 4 Mass
30 kg
Spring
200 N/m
Damping
200 N s/m
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second advantage is its adaptive mechanism using the underactuated tendon-driven mechanism. By routing the wire on the back of the hand, the tension of the wire can be adaptively distributed to the joints of the fingers, without requiring any effort to control each joint with a rigid-body robot. The third advantage is its wide ROM. The joints of the fingers can exert forces with a wide ROM, because there is no structure on the palm, which leads to effective strength exercise. Control algorithms for isotonic, isokinetic and impedance control exercises have been proposed. We adopted the EMG regulation algorithm to decrease the intensity of the exercise when the muscle activation level is high. The intensity of the strength exercise can be varied by adjusting both the control gains of the exercise program controller and the threshold value of EMG. At high threshold values, the control gains mainly affect the intensity of the exercise and at low threshold values, the EMG threshold mainly affect the intensity of the exercise. Further, signal processing of EMG including normalization with MVC (maximum voluntary contraction) can help setting proper criteria for the threshold value of EMG limit algorithm. The limitation of the current research is that it does not take into account the deformation of the glove. The tension of the wire and the forces exerted by the user can cause deformation of the glove, perturbing its original kinematics, because the Exo-Glove is basically made of a flexible fabric. Also, the wire tension does not exactly reflect the summation of the torque of the joints due to the limitation of the structure of the Exo-Glove; the movement of the wrist and the arm can cause disturbance in the wire tension (the example is the Fig. 10b). The main issues with this type of soft exoskeleton are fabric deformation and mechanical design for the reduced disturbance to the wire tension even with the undesired motion. Future works include modeling of the deformation and mechanical design optimization of the ExoGlove. Acknowledgments This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0013470) and the Technology Innovation Program (100036459, 10036492) funded by the MKE/KEIT, Korea.
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