Alexander Duschau-Wicke

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 patients 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.

A robotic system to train activities of daily living in a virtual environment

In the past decade, several arm rehabilitation robots have been developed to assist neurological patients during therapy. Early devices were limited in their number of degrees of freedom and range of motion, whereas newer robots such as the ARMin robot can support the entire arm. Often, these devices are combined with virtual environments to integrate motivating game-like scenarios. Several studies have shown a positive effect of game-playing on therapy outcome by increasing motivation. In addition, we assume that practicing highly functional movements can further enhance therapy outcome by facilitating the transfer of motor abilities acquired in therapy to daily life. Therefore, we present a rehabilitation system that enables the training of activities of daily living (ADL) with the support of an assistive robot. Important ADL tasks have been identified and implemented in a virtual environment. A patient-cooperative control strategy with adaptable freedom in timing and space was developed to assist the patient during the task. The technical feasibility and usability of the system was evaluated with seven healthy subjects and three chronic stroke patients.

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

BackgroundManual 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).MethodsEleven 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.ResultsPatients 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.ConclusionsPatient-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.

Feasibility and effects of patient-cooperative robot-aided gait training applied in a 4-week pilot trial

BackgroundFunctional training is becoming the state-of-the-art therapy approach for rehabilitation of individuals after stroke and spinal cord injury. Robot-aided treadmill training reduces personnel effort, especially when treating severely affected patients. Improving rehabilitation robots towards more patient-cooperative behavior may further increase the effects of robot-aided training. This pilot study aims at investigating the feasibility of applying patient-cooperative robot-aided gait rehabilitation to stroke and incomplete spinal cord injury during a therapy period of four weeks. Short-term effects within one training session as well as the effects of the training on walking function are evaluated.MethodsTwo individuals with chronic incomplete spinal cord injury and two with chronic stroke trained with the Lokomat gait rehabilitation robot which was operated in a new, patient-cooperative mode for a period of four weeks with four training sessions of 45 min per week. At baseline, after two and after four weeks, walking function was assessed with the ten meter walking test. Additionally, muscle activity of the major leg muscles, heart rate and the Borg scale were measured under different walking conditions including a non-cooperative position control mode to investigate the short-term effects of patient-cooperative versus non-cooperative robot-aided gait training.ResultsPatient-cooperative robot-aided gait training was tolerated well by all subjects and performed without difficulties. The subjects trained more actively and with more physiological muscle activity than in a non-cooperative position-control mode. One subject showed a significant and relevant increase of gait speed after the therapy, the three remaining subjects did not show significant changes.ConclusionsPatient-cooperative robot-aided gait training is feasible in clinical practice and overcomes the main points of criticism against robot-aided gait training: It enables patients to train in an active, variable and more natural way. The limited number of subjects in this pilot trial does not permit valid conclusions on the effect of patient-cooperative robot-aided gait training on walking function. A large, possibly multi-center randomized controlled clinical trial is required to shed more light on this question.

Online learning and adaptation of patient support during ADL training

Neurological patients with impaired upper limbs often receive arm therapy to restore or relearn lost motor functions. During the last years robotic devices were developed to assist the patient during the training. In daily life the diversity of movements is large because the human arm has many degrees of freedom and is used as a manipulandum to interact with the environment. To support a patient during the training the amount of support should be adapted in an assist-as-needed manner. We propose a method to learn the arm support needed during the training of activities of daily living (ADL) with an arm rehabilitation robot. The model learns the performance of the patient and creates an impairment space with a radial basis function network that can be used to assist the patient together with a patient-cooperative control strategy. Together with the arm robot ARMin the learning algorithm was evaluated. The results showed that the proposed model is able to learn the required arm support for different movements during ADL training.

A tendon-based parallel robot applied to motor learning in sports

Research in multimodal motor learning in sports is highly demanding with respect to the equipment, especially when the same equipment has to be reconfigured for different applications. In our multimodal motion synthesis lab (M3-lab) we apply visual, auditory, and haptic displays as well as feedback to enhance human motor learning. The demands on our haptic display, a tendon-based parallel robot (TBPR), are particularly high: a large workspace needs to be covered, the robotic device must be versatile, the visual and auditory modality should not be affected, high velocities and forces have to be realizable to render different sports applications, and user-cooperative control strategies should be applicable.

Optimized passive dynamics improve transparency of haptic devices

For haptic devices, compensation of the robots gravity is a frequent strategy with the aim to reduce interaction forces between robot and human in zero-impedance control. However, a closer look at the composition of these interaction forces may reveal that the net effect of uncompensated gravitational components of the robot actually reduces interaction forces during dynamic movements, because inertial and gravitational components at least partially compensate each other. This is the case in lower extremity exoskeletons, where less user force is necessary to swing the robots leg when gravity helps. Here, we go one step further by shaping optimal passive dynamics for arbitrary haptic devices. The proposed method of Generalized Elasticies uses conservative force fields to improve haptic transparency for certain movements types. In an example realization, these force fields are generated by elasticities spanning multiple joints. Practical experiments with the Lokomat lower extremity exoskeleton show the success of the proposed method in terms of reduced interaction torques and more physiological user motion compared to gravity compensation.

Virtual reality and gait rehabilitation Augmented feedback for the Lokomat

The application of virtual reality in rehabilitation has led to a rapid development of game-like applications. Since the key concept to successful rehabilitation involves the constant repetition of a task that involves the affected part of the body, such applications motivate patients to practice movements over and over again. Driven gait orthoses provide an excellent tool to use such methods for gait training in a meaningful way. In the present paper we give a first impression of how such an application may look like for the driven gait orthosis Lokomat. With a preliminary study we furthermore show, that such applications not only lead to higher motivation during training but also increase the activity of the patient. All patients report to prefer this new way of feedback and want to use it in subsequent training sessions.

A View on VR-Enhanced Rehabilitation Robotics

Robot-assisted gait training can increase the duration and number of training sessions, whilst reducing the number of therapists required per patient. However, training can often be boring for the patient so that the training intensity is low. Furthermore, training is usually limited to a single modality, providing only force feedback to guide the movement. Virtual reality (VR) with multimodal displays has the chance to feedback performance information to the patient, augment the training with additional audiovisual features, thus, making the therapy more exciting and increasing patient motivation. In this paper we present results from the literature and preliminary results from our research about novel VR strategies applied to gait and arm therapy. Broad clinical testing is still required to determine its efficacy or effectiveness on patient motivation