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 DOF. 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.

MUNDUS project: MUltimodal neuroprosthesis for daily upper limb support.

BACKGROUND: MUNDUS is an assistive framework for recovering direct interaction capability of severely motor impaired people based on arm reaching and hand functions. It aims at achieving personalization, modularity and maximization of the user's direct involvement in assistive systems. To this, MUNDUS exploits any residual control of the end-user and can be adapted to the level of severity or to the progression of the disease allowing the user to voluntarily interact with the environment. MUNDUS target pathologies are high-level spinal cord injury (SCI) and neurodegenerative and genetic neuromuscular diseases, such as amyotrophic lateral sclerosis, Friedreich ataxia, and multiple sclerosis (MS). The system can be alternatively driven by residual voluntary muscular activation, head/eye motion, and brain signals. MUNDUS modularly combines an antigravity lightweight and non-cumbersome exoskeleton, closed-loop controlled Neuromuscular Electrical Stimulation for arm and hand motion, and potentially a motorized hand orthosis, for grasping interactive objects. METHODS: The definition of the requirements and of the interaction tasks were designed by a focus group with experts and a questionnaire with 36 potential end-users. RESULTS: The functionality of all modules has been successfully demonstrated. User's intention was detected with a 100% success. Averaging all subjects and tasks, the minimum evaluation score obtained was 1.13 ± 0.99 for the release of the handle during the drinking task, whilst all the other sub-actions achieved a mean value above 1.6. All users, but one, subjectively perceived the usefulness of the assistance and could easily control the system. Donning time ranged from 6 to 65 minutes, scaled on the configuration complexity. CONCLUSIONS: The MUNDUS platform provides functional assistance to daily life activities; the modules integration depends on the user's need, the functionality of the system have been demonstrated for all the possible configurations, and preliminary assessment of usability and acceptance is promising.

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

BACKGROUND: Functional 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. METHODS: Two 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. RESULTS: Patient-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. CONCLUSIONS: Patient-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.

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.

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.

Assessment of lower extremity motor adaptation via an extension of the force field adaptation paradigm.

Lower extremity rehabilitation has seen recent increased interest. New tools are available to improve gait retraining in both adults and children. However, it remains difficult to determine optimal ways to plan interventions due to difficulties in continuously monitoring outcomes in patients undergoing rehabilitation. In this paper, we introduce an extension of the Force Field Adaptation Paradigm, used to quantitatively assess upper extremity motor adaptation, to the lower extremity. The algorithm is implemented on the Lokomat lower extremity gait orthosis and utilized to assess short-term motor adaptation. Establishing an understanding of how healthy adults' motor systems adapt to external perturbations will be important to understanding how the adaptive mechanisms involved in gait are altered by disease.

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.

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

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.

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.

Obstacle crossing in a virtual environment with the rehabilitation gait robot LOKOMAT.

The rehabilitation robot LOKOMAT has been developed at the Balgrist University Hospital to automate treadmill training of spinal cord injury and stroke patients. A virtual environment setup was implemented to increase patient's motivation and provide biofeedback, consisting of visual, acoustic and haptic modalities. Based on the knee and hip angles of the orthosis, an animated figurine moves through a virtual environment. This contribution describes the setup of the system and selected technical performance parameters. We focused on delay times caused by the setup, stability of the haptic obstacle rendering and on the level of immersion as judged by four healthy subjects. Results show that subjects judged the system's performance well (questionnaire scores over 80%). Problems exist though for obstacle rendering (questionnaire scores of 55%).