Chronic wrist impairment is frequent following stroke and negatively impacts everyday life. Rehabilitation of the dysfunctional limb is possible but requires extensive training and motivation. Wearable training devices might offer new opportunities for rehabilitation. However, few devices are available to train wrist extension even though this movement is highly relevant for many upper limb activities of daily living. As a proof of concept, we developed the eWrist, a wearable one degree-of-freedom powered exoskeleton which supports wrist extension training. Conceptually one might think of an electric bike which provides mechanical support only when the rider moves the pedals, i.e. it enhances motor activity but does not replace it. Stroke patients may not have the ability to produce overt movements, but they might still be able to produce weak muscle activation that can be measured via surface electromyography (sEMG). By combining force and sEMG-based control in an assist-as-needed support strategy, we aim at providing a training device which enhances activity of the wrist extensor muscles in the context of daily life activities, thereby, driving cortical reorganization and recovery. Preliminary results show that the integration of sEMG signals in the control strategy allow for adjustable assistance with respect to a proxy measurement of corticomotor drive.
As a leading cause of loss of functional movement, stroke often makes it difficult for patients to walk. Interventions to aid motor recovery in stroke patients should be carried out as a matter of urgency. However, muscle activity in the knee is usually too weak to generate overt movements, which poses a challenge for early post-stroke rehabilitation training. Although electromyography (EMG)-controlled exoskeletons have the potential to solve this problem, most existing robotic devices in rehabilitation centers are expensive, technologically complex, and allow only low training intensity. To address these problems, we have developed an EMG-controlled knee exoskeleton for use at home to assist stroke patients in their rehabilitation. EMG signals of the subject are acquired by an easy-to-don EMG sensor and then processed by a Kalman filter to control the exoskeleton autonomously. A newly-designed game is introduced to improve rehabilitation by encouraging patients' involvement in the training process. Six healthy subjects took part in an initial test of this new training tool. The test showed that subjects could use their EMG signals to control the exoskeleton to assist them in playing the game. Subjects found the rehabilitation process interesting, and they improved their control performance through 20-block training, with game scores increasing from 41.3 ± 15.19 to 78.5 ± 25.2. The setup process was simplified compared to traditional studies and took only 72 s according to test on one healthy subject. The time lag of EMG signal processing, which is an important aspect for real-time control, was significantly reduced to about 64 ms by employing a Kalman filter, while the delay caused by the exoskeleton was about 110 ms. This easy-to-use rehabilitation tool has a greatly simplified training process and allows patients to undergo rehabilitation in a home environment without the need for a therapist to be present. It has the potential to improve the intensity of rehabilitation and the outcomes for stroke patients in the initial phase of rehabilitation.
This paper proposes a novel bionic model of the human leg according to the theory of physiology. Based on this model, we present a biologically inspired 3-degree of freedom (DOF) lower limb exoskeleton for human gait rehabilitation, showing that the lower limb exoskeleton is fully compatible with the human knee joint. The exoskeleton has a hybrid serial-parallel kinematic structure consisting of a 1-DOF hip joint module and a 2-DOF knee joint module in the sagittal plane. A planar 2-DOF parallel mechanism is introduced in the design to fully accommodate the motion of the human knee joint, which features not only rotation but also relative sliding. Therefore, the design is consistent with the requirements of bionics. The forward and inverse kinematic analysis is studied and the workspace of the exoskeleton is analyzed. The structural parameters are optimized to obtain a larger workspace. The results using MATLAB-ADAMS co-simulation are shown in this paper to demonstrate the feasibility of our design. A prototype of the exoskeleton is also developed and an experiment performed to verify the kinematic analysis. Compared with existing lower limb exoskeletons, the designed mechanism has a large workspace, while allowing knee joint rotation and small amount of sliding.
Stroke patients often suffer from severe upper limb paresis. Rehabilitation treatment typically targets motor impairments as early as possible, however, muscular contractions, particularly in the wrist and fingers, are often too weak to produce overt movements, making the initial phase of rehabilitation training difficult. Here we propose a new training tool whereby electromyographic (EMG) activity is measured in the wrist extensors and serves as a proxy of voluntary corticomotor drive. We used the Myo armband to develop a proportional EMG controller which allowed volunteers to perform a simple visuomotor task by modulating wrist extensor activity. In this preliminary study six healthy participants practiced the task for one session (144 trials), which resulted in a significant reduction of the average trial time required to move and hold a cursor in different target zones (p < 0.001, ANOVA), indicating skill learning. Additionally, we implemented an EMG based classifier to distinguish between the desired movement strategy and unwanted alternatives. Validation of the classifier indicated that accuracy for detecting rest, wrist extension and unwanted strategies was 92.5 + 6.9% (M + SD) across all participants. When performing the motor task the classification algorithm flagged 4.3 + 3.5% of the trials as 'unwanted strategies', even in healthy subjects. We also report initial feedback from a survey submitted to two chronic stroke patients to inquire about feasibility and acceptance of the general setup by patients.
Robotic therapy is a useful method applied during rehabilitation of stroke patients (to regain motor functions). To ensure active participation of the patient, assistance-as-needed is provided during robotic training. However, most existing studies are based on a predetermined desired trajectory, which significantly limits the use of this method for more complex scenarios. In this paper, artificial intelligence (AI) agents are introduced to enhance the robot so that a knee exoskeleton can be autonomously controlled. A new assist-as-needed (AAN) method is proposed, where the subjects and agents cooperatively control movements. An electromyographic (EMG)-controlled knee exoskeleton with an interesting screen game is developed. Two different AI agents, modular pipeline and deep Q-network, are introduced; both can control the exoskeleton to play the screen game independently. The human-robot cooperative control is studied with two different assistant strategies, i.e., fixed assistant ratio and AAN. Eight healthy subjects participated in the initial experiment, and four assistant modes were studied. The game scores obtained by the two agents were significantly higher than those obtained by healthy subjects (EMG control), indicating that using the agents to assist stroke rehabilitation is possible. The AAN method demonstrated a better performance than the fixed assistant ratio method, indicated by the higher integral muscle activation level and participant score. Compared to a fully active control (EMG control) and fully fixed guidance (AI control), human-robot cooperative control had significantly higher integral muscle activation levels, i.e., the subjects were more involved and motivated during training. Using AI agents to power rehabilitation robots is a promising way to realize AAN rehabilitation.
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