Effect of Robotic Exoskeleton Motion Constraints on Upper Limb Muscle Synergies: A Case Study

Flexor Synergy Assessment and Therapy for Persons With Stroke Using the ULIX Low Impedance Robot

The flexor synergy after stroke results in involuntary activation of distal muscles when lifting the shoulder against gravity. This contributes to impaired ability to perform activities of daily living. Robotic exoskeletons can be useful in assessing the strength of the synergy and applying therapy modes that promote improved movement patterns. In this study, we evaluated 16 chronic stroke patients using the Ultra Low Impedance eXoskelton (ULIX). The subjects performed two synergy assessment tasks, elbow extension and hand opening while holding shoulder flexion at greater than 70 degrees with various gravity support levels. Joints not part of the tasks were locked in place. During the assessment tasks, increasing gravity support resulted in more elbow extension and reduced grip force; however, EMG contraction ratios of distal muscles compared to deltoids increased when gravity support was increased. A free cup reaching task was performed using several proposed therapy modes. During the cup reaching task, the therapy modes increased range of motion and improved the shoulder-elbow kinematic coordination compared with gravity support alone. There was a strong correlation between synergy expression in the elbow extension task and performance of the cup reaching task. Isolating movement to shoulder flexion and elbow extension during the assessment task resulted in better elbow extension than in the cup reaching task where all joints were free to rotate.

sEMG-Based Adaptive Cooperative Multi-Mode Control of a Soft Elbow Exoskeleton Using Neural Network Compensation

Soft rehabilitation exoskeletons have gained much attention in recent years, striving to assist the paralyzed individuals restore motor functions. However, it is a challenge to promote human-robot interaction property and satisfy personalized training requirements. This article proposes a soft elbow rehabilitation exoskeleton for the multi-mode training of disabled patients. An adaptive cooperative admittance backstepping control strategy combined with surface electromyography (sEMG)-based joint torque estimation and neural network compensation is developed, which can induce the active participation of patients and guarantee the accomplishment and safety of training. The proposed control scheme can be transformed into four rehabilitation training modes to optimize the cooperative training performance. Experimental studies involving four healthy subjects and four paralyzed subjects are carried out. The average root mean square error and peak error in trajectory tracking test are 3.18° and 5.68°. The active cooperation level can be adjusted via admittance model, ranging from 4.51 °/Nm to 10.99 °/Nm. In cooperative training test, the average training mode value and effort score of healthy subjects (i.e., 1.58 and 1.50) are lower than those of paralyzed subjects (i.e., 2.42 and 3.38), while the average smoothness score and stability score of healthy subjects (i.e., 3.25 and 3.42) are higher than those of paralyzed subjects (i.e., 1.67 and 1.71). The experimental results verify the superiority of proposed control strategy in improving position control performance and satisfying the training requirements of the patients with different hemiplegia degrees and training objectives.

A Robotic System to Deliver Multiple Physically Bimanual Tasks via Varying Force Fields

Individuals with physical limb disabilities are often restricted to perform activities of daily life (ADLs). While efficacy of bilateral training has been demonstrated in improving physical coordination of human limbs, few robots have been developed in simulating people's ADLs integrated with task-specific force field control. This study sought to develop a bilateral robot for better task rendering of general ADLs (gADLs), where gADL-consistent workspace is achieved by setting linear motors in series, and haptic rendering of multiple bimanual tasks (coupled, uncoupled and semi-coupled) is enabled by regulating force fields between robotic handles. Experiments were conducted with human users, and our results present a viable method of a single robotic system in simulating multiple physically bimanual tasks. In future, the proposed robotic system is expected to be serving as a coordination training device, and its clinical efficacy will be also investigated.