The effect of assist-as-needed support on metabolic cost during gait training of chronic stroke patients in LOPESII

Effectiveness of robotic gait training in rehabilitation of stroke patients remains inconclusive. A reason could be that the current robotic gait trainers do not initiate motor learning principles enough. To encourage active participation of the patient and therefore motor learning, assist-as-needed (AAN) support strategies have been implemented in the robotic gait trainer LOPESII. Aim of the current study was to examine the effect of assist-as-needed support on metabolic cost. Ten chronic stroke patients completed three 6-min walking trials in LOPESII, with zero support, AAN-support for stiff knee gait and complete-support. Metabolic parameters were measured and compared between support conditions. No significant differences in net metabolic power were observed between zero-support, AAN-support and complete support. No evidence was found that AAN-support asks a higher metabolic cost of the participant.</p

Feedback control of arm movements using Neuro-Muscular Electrical Stimulation (NMES) combined with a lockable, passive exoskeleton for gravity compensation.

Within the European project MUNDUS, an assistive framework was developed for the support of arm and hand functions during daily life activities in severely impaired people. This contribution aims at designing a feedback control system for Neuro-Muscular Electrical Stimulation (NMES) to enable reaching functions in people with no residual voluntary control of the arm and shoulder due to high level spinal cord injury. NMES is applied to the deltoids and the biceps muscles and integrated with a three degrees of freedom (DoFs) passive exoskeleton, which partially compensates gravitational forces and allows to lock each DOE The user is able to choose the target hand position and to trigger actions using an eyetracker system. The target position is selected by using the eyetracker and determined by a marker-based tracking system using Microsoft Kinect. A central controller, i.e., a finite state machine, issues a sequence of basic movement commands to the real-time arm controller. The NMES control algorithm sequentially controls each joint angle while locking the other DoFs. Daily activities, such as drinking, brushing hair, pushing an alarm button, etc., can be supported by the system. The robust and easily tunable control approach was evaluated with five healthy subjects during a drinking task. Subjects were asked to remain passive and to allow NMES to induce the movements. In all of them, the controller was able to perform the task, and a mean hand positioning error of less than five centimeters was achieved. The average total time duration for moving the hand from a rest position to a drinking cup, for moving the cup to the mouth and back, and for finally returning the arm to the rest position was 71 s

Patient-cooperative control increases active participation of individuals with SCI during robot-aided gait training

ABSTRACT: BACKGROUND: Manual body weight supported treadmill training and robot-aided treadmill training are frequently used techniques for the gait rehabilitation of individuals after stroke and spinal cord injury. Current evidence suggests that robot-aided gait training may be improved by making robotic behavior more patient-cooperative. In this study, we have investigated the immediate effects of patient-cooperative versus non-cooperative robot-aided gait training on individuals with incomplete spinal cord injury (iSCI). METHODS: Eleven patients with iSCI participated in a single training session with the gait rehabilitation robot Lokomat. The patients were exposed to four different training modes in random order: During both non-cooperative position control and compliant impedance control, fixed timing of movements was provided. During two variants of the patient-cooperative path control approach, free timing of movements was enabled and the robot provided only spatial guidance. The two variants of the path control approach differed in the amount of additional support, which was either individually adjusted or exaggerated. Joint angles and torques of the robot as well as muscle activity and heart rate of the patients were recorded. Kinematic variability, interaction torques, heart rate and muscle activity were compared between the different conditions. RESULTS: Patients showed more spatial and temporal kinematic variability, reduced interaction torques, a higher increase of heart rate and more muscle activity in the patient-cooperative path control mode with individually adjusted support than in the non-cooperative position control mode. In the compliant impedance control mode, spatial kinematic variability was increased and interaction torques were reduced, but temporal kinematic variability, heart rate and muscle activity were not significantly higher than in the position control mode. CONCLUSIONS: Patient-cooperative robot-aided gait training with free timing of movements made individuals with iSCI participate more actively and with larger kinematic variability than non-cooperative, position-controlled robot-aided gait training

Generalized elasticities improve patient-cooperative control of rehabilitation robots

In the effort to make rehabilitation robots patient-cooperative, two prerequisites have to be met: One is providing the necessary amount of guidance and safety for the patient. Just as important is transparency, i.e. minimum interaction between robot and human when it is not needed. Recently, we suggested the method of generalized elasticities, which reduce undesired interaction forces due to robot dynamics by shaping optimal conservative force fields to compensate these dynamics. We now show that these conservative force fields can not only be used to minimize undesired interaction, but that they can also support and guide the patient during therapy when needed. Thus, the patient is given maximum freedom within a safe training environment, with the aim to maximize training efficacy

Hiding robot inertia using resonance

To enable compliant training modes with a rehabilitation robot, an important prerequisite is that any undesired human-robot interaction forces caused by robot dynamics must be avoided, either by an appropriate mechanical design or by compensating control strategies. Our recently proposed control scheme of "Generalized Elasticities" employs potential fields to compensate for robot dynamics, including inertia, beyond what can be done using closed-loop force control. In this paper, we give a simple mechanical equivalent using the example of the gait rehabilitation robot Lokomat. The robot consists of an exoskeleton that is attached to a frame around the patient's pelvis. This frame is suspended by a springloaded parallelogram structure. The mechanism allows vertical displacement while providing almost constant robot gravity compensation. However, inertia of the device when the patient's pelvis moves up and down remains a source of large interaction forces, which are reflected in increased ground reaction forces. Here, we investigate an alternative suspension: To hide not only gravity, but also robot inertia during vertical pelvis motion, we suspend the robot frame by a stiff linear spring that allows the robot to oscillate vertically at an eigenfrequency close to the natural gait frequency. This mechanism reduces human-robot interaction forces, which is demonstrated in pilot experimental results

Adaptive support for patient-cooperative gait rehabilitation with the Lokomat

The rehabilitation robot Lokomat allows automated treadmill training for patients with neurological gait disorders. The basic position control approach for the robot has been extended to patient-cooperative strategies. These strategies provide more freedom and allow patients to actively influence their training. However, patients are likely to need additional support during patient-cooperative training. In this paper, we propose an algorithm based on iterative learning control that shapes a supportive torque field. The torque field is supposed to assist the patient as much as needed in performing the desired task. We evaluated the algorithm in a proof-of-concept experiment with 3 healthy subjects. Results showed that the amount of support was automatically adapted to the activity and the individual needs of the subjects. Furthermore, the support improved the performance of the subjects

Path control: a method for patient-cooperative robot-aided gait rehabilitation

Gait rehabilitation robots are of increasing importance in neurorehabilitation. Conventional devices are often criticized because they are limited to reproducing predefined movement patterns. Research on patient-cooperative control strategies aims at improving robotic behavior. Robots should support patients only as much as needed and stimulate them to produce maximal voluntary efforts. This paper presents a patient-cooperative strategy that allows patients to influence the timing of their leg movements along a physiologically meaningful path. In this "path control" strategy, compliant virtual walls keep the patient's legs within a "tunnel" around the desired spatial path. Additional supportive torques enable patients to move along the path with reduced effort. Graphical feedback provides visual training instructions. The path control strategy was evaluated with 10 healthy subjects and 15 subjects with incomplete spinal cord injury. The spatio-temporal characteristics of recorded kinematic data showed that subjects walked with larger temporal variability with the new strategy. Electromyographic data indicated that subjects were training more actively. A majority of iSCI subjects was able to actively control their gait timing. Thus, the strategy allows patients to train walking while being helped rather than controlled by the robot