Selective control of a subtask of walking in a robotic gait trainer(lopes)

Selective control of a subtask of walking in a robotic gait trainer(lopes) E. H.F. Van Asseldonk, R. Ekkelenkamp, Jan F. Veneman, F. C. T. Van der Helm, H. van der Kooij Abstract Robotic gait trainers are used all over the world for the rehabilitation of stroke patients, despite relatively little is known about how the robots should be controlled to achieve the optimal improvement. Most devices control complete joint trajectories and assume symmetry between both legs by either a position or an impedance control. However we believe that the control should not be on a joint level but on a subtask level (i.e. foot clearance, balance control). To this end we have chosen for virtual model control (VMC) to define a set of controllers that can assist in each of these tasks. Thus enabling the exoskeleton to offer selective support and evaluation of each substask during rehabilitation training. The aim of this explorative pilot study was to assess the performance of a VMC of the step height and to assess if selective control of the step height left the remaining of the walking pattern unaffected. Four young healthy subjects walked on a treadmill with their legs and pelvis attached to the lopes exoskeleton in 3 different conditions: (1) providing minimal resistance, (2) control of the left step height with a low stiffness (3) control of the step height with a large stiffness. We have shown that it is possible to exert a vertical forces for the support of foot clearance during the swing phase. The higher stiffness of the VMC resulted in a greater change of the step height, which was achieved by a larger increase of the maximal hip and knee flexion compared to the low stiffness condition. The control of the step height resulted in minor changes in the cycle time and swing time. The joint angles also showed only minor changes The preliminary results suggest that we were able to control a subtask of walking, while leaving the remaining walking trajectory largely unaffected. In the near future, control of other subtask will be implemented and evaluated in isolation and in conjunction with each other. I. INTRODUCTION The use of robotic gait training devices is getting more and more popular in the rehabilitation of neurological patients like stroke patients and spinal cord injury patients. Still it is largely unknown how these trainers should be controlled to achieve the largest improvements in function. Currently commercially available devices (mechanized gait Manuscript received February 11, 2007. This study was supported by the Netherlands Organisation of Scientific research (Vernieuwings-impuls 2001, 016027011, granted to DR H. van der Kooij).. E. H. F. van Asseldonk, R. Ekkelenkamp, J.F. Veneman, F.C.T. van der Helm and H van der Kooij are with Institute for Biomedical Technology (BMTI), Dept of Biomechanical Engineering, Enschede, The Netherlands (corresponding author to provide phone: +31-53-4982446; fax: +31-53- 4893695; e-mail: e.h.f.vanasseldonk@utwente.nl). F. C. T. van der Helm is with Man-Machine Systems & Control group Biomedical Engineering Group, Delft University of Technology, Delft, The Netherlands trainer [1], Lokomat [2]) are either position controlled or mostly used in the position control mode. In position control, a stiff controller assures that the joint and segment movements coincide with predefined trajectories, cycle after cycle irrespective of the generated activity by the patient. The absence of the need to generate activity limits the learning of new tasks [3]. Different algorithms are now being implemented in the control of the Lokomat, to increase the cooperation of the subjects but the basis is still a predefined gait trajectory based on healthy walking patterns [4, 5]. Recently Cai and colleagues [6] showed that spinal mice trained with a fixed pattern regained less walking abilities than mice trained with an Assist As Needed (AAN) algorithm. In this algorithm, the ankle movements were gently guided along trajectory as long as the ankle position remained within an allowed window. When the ankle position moves out of the window it was forced back. In the AAN algorithm, the mice were triggered to move their limbs with self-generated activity, eventually leading to greater learning gains. Using elegant modeling Emken et al [7]showed that decreasing the assistance during learning of new task is necessary to increase the contribution of the human muscle force in generating the required force to accomplish the task. However the implementation of their proposed "Assist As Needed" controller into the rehabilitation runs up against difficulties. Their controller contains a model of the internal model formation of the individual subject, which can be estimated based on initial responses of the subject to the new task. However, the impairments of a patient might hinder the performance of the task and as such the accuracy of the model. Another disadvantage of position control is that a whole trajectory is imposed and that there is no possibility to focus the training on specific aspects of the walking pattern. Gait can be thought of as consisting of different subtasks that all have to be accomplished successfully to progress without falling[8]. Each of these subtasks can be impaired to some degree in stroke patients, while other might be unaffected. We would expect that greater benefits can be obtained when the patients are left free to perform the non impaired subtasks while the robot provides support in performing the impaired subtasks. Especially in the rehabilitation of stroke patients this might be a crucial element. Stroke patients often have one largely unaffected side. During the course of the rehabilitation the non-paretic side might learn to compensate 1-4244-1320-6/07/$25.00 (c)2007 IEEE 841

for the decreased capabilities of the paretic side [9]. This compensation will cause a clear deviation of the joint kinematics from the normal symmetric walking pattern, while it will still perform the basic subtasks of walking. Consequently, imposing a symmetrical walking pattern during gait rehabilitation might not be beneficial for the paretic leg as it is beyond its capabilities and it might not be beneficial for the non-paretic leg as it limits the possibility to learn to compensate. Virtual Model Control can be used to implement the support of subtasks [10]. The basis of this control method is to define physical interactions with the subject that would aid the patient with gait tasks. These interactions are then translated into a set of Virtual physical Models (VMs) such as springs and dampers that can be switched on and off at appropriate times in the gait cycle. The virtual forces that would be exerted by interaction between the virtual models and the subject are translated to joint torque commands for the joint actuators. The torque for each joint is controlled separately. Ekkelenkamp and colleagues [10] showed that the foot clearance during swing could be easily adapted by using VMC. However they did not quantify if the control of this subtask affected the remaining walking pattern. The goal of this study was twofold. First, we evaluated whether the mechanical construction and control of the first complete prototype of LOPES [11] allowed appropriate control of the required control torques at the different joints. Second, we assessed if control of the height of the ankle during swing phase left the remaining of the walking pattern unaffected. II. MATERIALS AND METHODS A. Subjects Four healthy young male adults, mean age 26.25 years (s.d. 4.8 years) volunteered to be participants for this experiment. All participants provided informed consent before testing began. B. Experimental apparatus and recordings 1) Rehabilitation device For the experiments the prototype of the gait rehabilitation robot LOPES was used. LOPES is an exoskeleton type rehabilitation robot. It is lightweight and actuated by Bowden cable driven series elastic actuators [12]. The springs on the joints of the robot can be used to measure the applied torques directly. This torque measurement is used to cancel out as best as possible the coulomb friction in the Bowden cables as well as to allow the monitoring of patient - exoskeleton interaction during the training. The exoskeleton offers a freely translatable (3D) pelvis, where the sideways and forward/backward motion is actuated. Together with two actuated rotation axes in the hip joints, and actuated knees with one rotation axis Fig 1.A) The LOPES exoskeleton B) the exoskeleton attached to a subject. [11]. The robot is impedance controlled, which implies that the actuators are used as force (torque) sources. This allows implementing both the robot-in-charge mode, that is position control with high impedance, and a patient-incharge mode, that is zero-force control with low impedance on each degree of freedom. The robot will most likely function somewhere between these extremes, guiding the patient much like a physical therapist would do. In order to implement this type of support we have decided to use virtual model control (VMC) [8]. This control method has been used in biped robots but is new for the use in exoskeletons. In this study we want to assess if we can selectively control the subject's step height. We have implemented a VMC to make sure the test subject achieves a required level of foot clearance during the swing phase. (see Fig 2) The VMC model consists of a spring damper system connected to the ankle with a stiffness Ky. The required force Fy is calculated from the virtual spring Ky and the deviation of the reference vertical distance (y_ref) from the vertical distance (y): Fy = Ky( yref y) (1.1) The current position of the ankle is calculated from the hip and knee angles and the segment lengths (see (1.3)). The reference vertical position of the ankle with respect to the hip is calculated from a reference trial (see description of experimental protocol). From this reference trial, the average pattern of the step height as function of the horizontal position of the ankle was determined. A 10th order polynomial function was fitted on the data. A sine function with an amplitude of 10 cm and a period of twice the average step length of the reference trial was added to this polynomial function to get the reference y-position (y_ref). 1-4244-1320-6/07/$25.00 (c)2007 IEEE 842

2) Recordings Gait phases were detected with footswitches, taped directly to the subject's heel and fore foot of both feet. The integrated sensors of the lopes exoskeleton, measuring the angles of the exoskeleton are used to get an estimation of the joint angles of the subjects. Fig 2 Schematic representation of the VMC and the exoskeleton. Ky is the virtual spring stiffness, the vertical distance between the hip and ankle, y_ref the reference trajectory height, lu the upperleg length, ll the lower leg length and Theta_k and Theta_h respectively the knee and hip angles. y = f( x) for x x & x x + steplength (1.2) ref start start where x start is the average x position at which swing starts, and steplength is the average step length, both determined from the reference trajectory. The required vertical force is mapped to the torques at the hip and knee joint. The upper (lu) and lower leg length (ll) and knee and hip angles are used to determine the relation between the applied moments and the required force to be exerted by the VMC. The forward kinematic map from the hip frame to the knee frame can be written as follows: h k X x lusin( θ ) +llsin( θ - θ ) h h k = = y -lucos( θh) -llcos( θh- θk) (1.3) where the h and k stand for hip and knee and θ is the joint angle. By differentiation to the generalized coordinates (θ h,θ k ), we get the Jacobian: lucos( h) +llcos( h- k) -llcos( h- k) h J θ θ θ θ θ k = (1.4) lusin( θh) +llsin( θh- θk) -llsin( θh- θk) The Jacobian relates the VMC force to the joint torques : h h T τ = J F (1.5) k k y As we were only concerned in lifting up the left foot, we have limited Fy to upward forces and the VMC was only active during the swing phase. The joints of the right leg and the left leg ab/adduction joints were all controlled to zero impedance. C. Experimental protocol The LOPES exoskeleton was attached to the subject's leg and pelvis. The subject's joint axes were aligned with the joint axes of the exoskeleton by adjusting the pelvis width of the exoskeleton and the length of exoskeleton linkages. As the exoskeleton did not encompass an ankle joint, the ankle was left free to move. Subjects were given 3 minutes of walking in LOPES in the zero impedance mode to get used to walking in LOPES. Subsequently, the subject's reference step height was determined from one minute of walking. The reference trajectory was used to determine the target left ankle trajectory of the VMC controller (see paragraph 2B1). The effect of VMC controller was tested in two trials. In these trials, the subjects first walked for about 1 minute in the zero impedance control (baseline), before the VMC controller was turned on. The two trials differed in the stiffness of the VMC controller, 1000 N/m (VMClow) and 3000 N/m (VMChigh). Subjects where only told that LOPES was going to try to influence part of their walking trajectory and did not receive any instructions about how to respond to the exerted force by the VMC. All trials were performed with a walking velocity of 0.75 m/s D. Data Analysis The data of the foot switches were used to define the different steps. All data (kinematics and kinetics) were split up into individual stride cycles on basis of the heel strikes of the left foot. To assess the influence of the VMC controller on the walking pattern, basic temporal variables and kinematic parameters were calculated from the footswitch data and integrated joint sensors, respectively. The duration of a step cycle was taken as the time between two consecutive heel strikes of the left foot, and the swing time was the time between heel off of the left foot and the subsequent heel strike. From the joint sensors we determined the maximal hip and knee flexion angles during every step. As we were not interested in the initial adaptation of the subject to the VMC controller, we calculated an average value of the aforementioned parameters over 10 cycles (20 th -11 th step before end of either baseline or VMC exposure), resulting in two baseline value and one value for the different implementations of the VMC controller. As the values for both baselines were very similar we will only present the baseline of the trial with the VMC controller with a low stiffness. 1-4244-1320-6/07/$25.00 (c)2007 IEEE 843

Fig 3. Average step height over the 4 subject for the two different VMC controllers. The standard deviations are indicated with the shading around the average trajectories. Apart of parameters we also calculated average trajectories of the joint angles, step height and reference forces and exerted forces of the VMC controller over the same 10 cycles as defined above. Before averaging, the different trajectories were normalized with respect to time. The average trajectories of the joint angles were used to calculate an RMS value between the average trajectories of VMClow and VMChigh with the average trajectory of the baseline. The average trajectory of step height was used to calculate the change in step height of VMClow and VMChigh with respect to the baseline trajectorie. For the evaluation of the control of the joint actuators we compared the reference forces of the VMC controller with the reconstructed y-forces. For this we will use the inverse of the relationship between F and τ 1 ( h T F ) h y = kj kτ (1.6) In this relationship we will use the measured angles and moments to show the quality of the exerted forces. III. RESULTS The step height of the subjects was differently affected by the two implementations of the VMC controller (see Fig 3). As could be expected VMChigh resulted in a greater change of the step height as VMClow. Still the observed changes for VMChigh were smaller as the set 10 cm. In the following paragraphs, we will first describe the results concerning the accuracy of the controller and second we will describe how the control of the step height affected the walking pattern. A. Evaluation of performance of VMC The required vertical force at the ankle is generated by the torques at the hip and knee. The torques are controlled on a lower level by separate torque controllers. Fig 4 shows an example of the performance of the torque controller during Fig 4. A typical example of the required and the measured knee torque (upper panel) and the knee angle (lower panel) during the swing phase. Flexion is defined as positive. operation. It shows some noisy operation with a band of roughly 5 Nm around the desired trajectory with a slight overshoot at the peak. The time delay of the system is roughly 50 ms. The system shows adequate performance for low angular velocities at the knee but at higher angular velocities the performance degrades. During flexion at the start of the swing phase this is caused by the fact that the knee is moving along with the desired trajectory. The DC motors are not capable of both following the desired trajectory and offer the force required (maximum angular velocity DC motor). The opposite happens when the subject extends his knee. The person moves against the given direction making the system unable to reduce the exerted force fast enough. Fig 5 shows the generated and the reference VMC forces for two subjects. In both subjects the y-force has an overshoot. This overshoot shows the same pattern as the knee angle overshoot. The subjects show a different response to the robotic forces. The first (left panels) shows that the higher gain is coupled with higher forces. This subject does hardly adapt his foot trajectory in response to the exerted forces. The second subject (right panels) shows that the system delivers less force in the stiffer version. Apparently this subject has felt that the robot wanted him to lift up his foot higher and did generate the torques to accomplish this himself. 1-4244-1320-6/07/$25.00 (c)2007 IEEE 844

Fig 5. Upper panels show the reference and the exerted force y-force at the ankle under the low impedance conditions (Ky=100N/m) and the bottom two show the same under the high impedance condition (Ky=3000 N/m). The different columns show the average profiles for different subjects. The shaded areas indicate the standard deviation. A. Selectivity of control To test if the VMC controller could provide selective control, we assessed the changes in temporal and kinematic parameters. The number of subjects in this pilot experiment was too small to perform meaningful statistical analysis. The changes in step height were accompanied by small changes of the swing time and cycle time (Table 1). The increase in step height was accomplished by increasing the maximal knee flexion as well as hip flexion angle (Fig 7 and 8). Apart of the increase in flexion angles during swing, the angles of the left leg during control of the step height resembled the angles during baseline walking. The angles of the right leg only showed small deviations of the trajectories during baseline (Fig 7). The resemblance between the trajectories was expressed in the small RMS value for all joints, which did not exceed 3º for all joints. In conjunction with the joints of the left leg, the joints of the right leg showed a subtle increase of maximal flexion angles during VMChigh. This was mainly caused by two of the four subjects who copied the pattern of their left leg to their right leg and also walked with an increased step height of the right leg. Afterwards these subjects reported that it feld more natural to them to keep a symmetrical walking pattern. TABLE I. TEMPORAL PARAMETERS OF WALKING FOR THE DIFFERENT CONDITIONS. Baseline Low gain High gain Fig 6. The average horizontal forces exerted by the VMC controller with a high and low stiffness. The depicted forces are averages for one subject. The standard deviations are indicated with the shaded area. A positive force is directed forward. The reference horizontal force is always equal to zero. Cycle time 1.43 ± 0.06 1.41 ± 0.07 1.45 ± 0.09 Swing time 0.54 ± 0.04 0.54 ± 0.06 0.58 ± 0.07 1-4244-1320-6/07/$25.00 (c)2007 IEEE 845

Fig 7. Average trajectories of the joint angels for the different conditions for subject one. The shaded areas indicate the standard deviation. Hip and knee flexion are defined as positive. I. DISCUSSION In this pilot experiment, we have shown the feasibility of VMC in the control of a subtask of walking. The results showed that the step height could be influenced by setting the stiffness to different values, while the remaining of the walking was hardly affected. In the near future we will also implement and evaluate VMCs for the other subtasks of walking, like weight support, control of step length, knee stabilization. Apart of the individual evaluation, we will also assess how the different controllers work together in realizing essential support during walking. Fig 8. Upper panel shows the average RMS of the joint angles over all subjects. For the joint angles of the right leg the RMS values were calculated over the whole trajectory, while the RMS for the joints of the left leg were only calculated over the duration of the stance phase. The lower panel shows the average maximal flexion angles for the hip and knee joint. In both panels the error bars indicate the standard deviation. A. Evaluation of VMC During the generation of the peak torques, the exerted torques showed an overshoot. This overshoot is partly caused by incomplete compensation for the static friction and motor mass. Due to a large dependency of the coulomb friction in the Bowden cables on the orientation of the cable it is not possible to compensate entirely for this friction. Attempts have been made to identify and measure this friction but these have not given any results that can be used for adequate control. Normal solutions such as adding jitter to the control signal would not work well in this environment as it is noisy and the vibrations would be uncomfortable and distracting to the subjects. Another cause for the overshoot is that we were not adequately compensating for motor mass, as we did not have 1-4244-1320-6/07/$25.00 (c)2007 IEEE 846

acceleration data of the motor. We are now working on reducing friction in the cables by optimizing cable length and motor placement. Furthermore we are currently working on compensation for the friction that will work for the entire range of motion allowed in LOPES. Fig 4 shows that the system was unable to exert forces when the angular velocity had the same direction as the required torque. Combined with the amount of power lost in the Bowden cables it would be beneficially to replace the current actuators with actuators that can deliver more power at the higher velocities. The system is capable of offering the required assistance but it would be beneficial for the predictability and the accuracy of support offered if the torque control could be improved. The system showed that it was possible to selectively offer a force in the vertical direction without offering a force in the horizontal direction. This implies that it is physically possible to influence the foot clearance without influencing the forward movement of the ankle. movements inside the exoskeleton will have some effect on the exact quantitative differences between the different conditions we believe it will keep the qualitative differences intact. The pattern that was determined for the ankle height in this experiment had a dependency on the horizontal position of the ankle with respect to the hip (1.2). Consequently the control of the step height was not completely independent of the control of step length. If the subject would try to make a smaller step, they would feel an upward force of the VMC. The subject were free to take larger steps, as the upward force was only provided during the average step length of reference trial. It is more likely that subjects would increase their step length if they made a higher step than a smaller step. However the results showed that neither was the case. In future controllers a predictive element should be implemented to determine the step length based on the hip velocity during the initiation of the swing phase. This way the step length will still be determined by the subject rather than the robot. B. General discussion The force of the VMC can be used as feedback for the patient. Biofeedback is an important aspect in neurological rehabilitation. In addition to the intrinsic feedback from their afferent sensors, patients receive externally provided information about their actual performance. The extra feedback is provided to make patients more aware about the consequences of their self-generated activity on (aspects of) the task execution, which facilitates motor (re)learning and motivates the patient. Recently, biofeedback has been implemented for gait training in the Lokomat [13]. The provided feedback showed good correlations with the generated activity during the swing phase, however the correlation with the activity during stance was low. The provided feedback motivated the subjects to generate more activity in a comparable degree as verbal feedback of a therapist did. In controlling subtasks the performance of the patient on the important aspects of gait is monitored for an appropriate control. This information can easily be processed online and used for feedback to the patient on step-by-step basis. Several aspects of this study merit further discussion. We used the integrated sensors of the exoskeleton to get an estimation of the joint angles of the subjects. There might be an error in this estimation because of misalignment of the subject's joint axes with those of the exoskeleton. Furthermore, the subject can move inside the exoskeleton. This movement is probably larger when the exoskeleton is used to transmit torques to the subject as is the case during control of the step height compared to walking in zero impedance. Recently, Neckel and colleagues [14] showed that the misalignment of the subject's joints with an exoskeleton was on anverage 12 mm for the knee and 18 mm for the hips. Although this misalignment and the II. CONCLUSION These preliminary results show that we are able to selectively control subtasks of walking. We aim to implement a controller for each of the essential subtasks of walking, so we will be able to provide a safe rehabilitation environment in which the subtasks can be practiced separately or in combination with each other. The control of subtasks allows for more flexibility in the eventual joint movements, which will provide stroke patients with the possibility to make compensatory movements with their non-paretic leg, which might result in a larger functional gain. III. REFERENCES [1] S. Hesse and D. Uhlenbrock, "A mechanized gait trainer for restoration of gait," J Rehabil Res Dev, vol. 37, pp. 701-8., 2000. [2] G. Colombo, M. Joerg, R. Schreier, and V. Dietz, "Treadmill training of paraplegic patients using a robotic orthosis," J Rehabil Res Dev, vol. 37, pp. 693-700., 2000. [3] J. F. Israel, D. D. Campbell, J. H. Kahn, and T. G. Hornby, "Metabolic costs and muscle activity patterns during robotic- and therapist-assisted treadmill walking in individuals with incomplete spinal cord injury," Phys Ther, vol. 86, pp. 1466-78, 2006. [4] M. Bernhardt, M. Frey, G. Colombo, and R. Riener, "Hybrid force-position control yields cooperative behaviour of the rehabilitation robot LOKOMAT," 2005. [5] R. Riener, L. Lunenburger, S. Jezernik, M. Anderschitz, G. Colombo, and V. Dietz, "Patientcooperative strategies for robot-aided treadmill training: first experimental results," Neural Systems 1-4244-1320-6/07/$25.00 (c)2007 IEEE 847

and Rehabilitation Engineering, IEEE Transactions on [see also IEEE Trans. on Rehabilitation Engineering], vol. 13, pp. 380-394, 2005. [6] L. L. Cai, A. J. Fong, C. K. Otoshi, Y. Liang, J. W. Burdick, R. R. Roy, and V. R. Edgerton, "Implications of assist-as-needed robotic step training after a complete spinal cord injury on intrinsic strategies of motor learning," J Neurosci, vol. 26, pp. 10564-8, 2006. [7] J. L. Emken, J. E. Bobrow, and D. J. Reinkensmeyer, "Robotic movement training as an optimization problem: designing a controller that assists only as needed," 2005. [8] J. E. Pratt, "Virtual model control of a biped walking robot," vol. PhD. Cambridge: MIT, 1995. [9] G. Kwakkel, B. Kollen, and E. Lindeman, "Understanding the pattern of functional recovery after stroke: facts and theories," Restor Neurol Neurosci, vol. 22, pp. 281-99, 2004. [10] R. Ekkelenkamp, J. Veneman, and H. van der Kooij, "LOPES: selective control of gait functions during the gait rehabilitation of CVA patients," presented at Rehabilitation Robotics, 2005. ICORR 2005. 9th International Conference on, Chicago, 2005. [11] H. Van der kooij, J. Veneman, and R. Ekkelenkamp, "Design of a compliantly actuated exo-skeleton for an impedance controlled gait trainer robot.," presented at Engineering in Medicine and Biology Society, 2006. Proceedings of the 28th Annual International Conference of the IEEE, New York City, USA, 2006. [12] J. F. Veneman, R. Ekkelenkamp, R. Kruidhof, F. C. T. van der Helm, and H. van der Kooij, "A Series Elasticand Bowden-Cable-Based Actuation System for Use as Torque Actuator in Exoskeleton-Type Robots," The International Journal of Robotics Research, vol. 25, pp. 261-281, 2006. [13] L. Lunenburger, G. Colombo, and R. Riener, "Biofeedback for robotic gait rehabilitation," J Neuroengineering Rehabil, vol. 4, pp. 1, 2007. [14] N. Neckel, W. Wisman, and J. Hidler, "Limb Alignment and Kinematics Inside a Lokomat Robotic Orthosis," 2006. 1-4244-1320-6/07/$25.00 (c)2007 IEEE 848