Assessment of Motor Control during Three-Dimensional Movements Tracking with Position-Varying Gravity Compensation

Alteration of muscle activity during voluntary rehabilitation training with single-joint Hybrid Assistive Limb (HAL) in patients with shoulder elevation dysfunction from cervical origin

Shoulder elevation, defined here as arm raising, being essential for activities of daily living, dysfunctions represent a substantial burden in patients' lives. Owing to the complexity of the shoulder joint, the tightly coordinated muscular activity is a fundamental component, and neuromuscular impairments have devastating effects. A single-joint shoulder type version of the Hybrid Assistive Limb (HAL) allowing motion assistance based on the intention of the user via myoelectric activation has recently been developed, and its safety was demonstrated for shoulder rehabilitation. Yet, little is known about the physiological effects of the device. This study aims to monitor the changes in muscle activity and motion during shoulder HAL rehabilitation in several patients suffering from shoulder elevation dysfunction from cervical radicular origin. 8 patients (6 males, 2 females, mean age 62.4 ± 9.3 years old) with weakness of the deltoid muscle resulting from a damage to the C5 nerve root underwent HAL-assisted rehabilitation. We combined surface electromyography and three-dimensional motion capture to record muscular activity and kinematics. All participants showed functional recovery, with improvements in their Manual Muscle Testing (MMT) scores and range of motion (ROM). During training, HAL decreased the activity of deltoid and trapezius, significantly more for the latter, as well as the coactivation of both muscles. We also report a reduction of the characteristic shrugging compensatory motion which is an obstacle to functional recovery. This reduction was notably demonstrated by a stronger reliance on the deltoid rather than the trapezius, indicating a muscle coordination tending toward a pattern similar to healthy individuals. Altogether, the results of the evaluation of motion and muscular changes hint toward a functional recovery in acute, and chronic shoulder impairments from cervical radicular origin following shoulder HAL rehabilitation training and provide information on the physiological effect of the device.

Human Motion Intent Description Based on Bumpless Switching Mechanism for Rehabilitation Robot

This paper aims to improve the performance of an electromyography (EMG) decoder based on a switching mechanism in controlling a rehabilitation robot for assisting human-robot cooperation arm movements. For a complex arm movement, the major difficulty of the EMG decoder modeling is to decode EMG signals with high accuracy in real-time. Our recent study presented a switching mechanism for carving up a complex task into simple subtasks and trained different submodels with low nonlinearity. However, it was observed that a "bump" behavior of decoder output (i.e., the discontinuity) occurred during the switching between two submodels. The bumps might cause unexpected impacts on the affected limb and thus potentially injure patients. To improve this undesired transient behavior on decoder outputs, we attempt to maintain the continuity of the outputs during the switching between multiple submodels. A bumpless switching mechanism is proposed by parameterizing submodels with all shared states and applied in the construction of the EMG decoder. Numerical simulation and real-time experiments demonstrated that the bumpless decoder shows high estimation accuracy in both offline and online EMG decoding. Furthermore, the outputs achieved by the proposed bumpless decoder in both testing and verification phases are significantly smoother than the ones obtained by a multimodel decoder without a bumpless switching mechanism. Therefore, the bumpless switching approach can be used to provide a smooth and accurate motion intent prediction from multi-channel EMG signals. Indeed, the method can actually prevent participants from being exposed to the risk of unpredictable loads.

The effects of different tracking tasks on muscle synergy through visual feedback

By recruiting a modular organization of muscle with relative activities, the arm motion can be indicated by the neural system and generated for performing a variety of motor tasks. In this study, a Non-negative Matrix Factorization with initial estimation is applied to identify and extract primary muscle synergies and their activation patterns from the processed EMG recordings during three multidirectional tracking tasks with visual feedback interaction. The effects of task variety and tracking accuracy by visual feedback on muscle synergies and their activation patterns are explored by statistic analysis. The results showed that only the task variety affected what synergies were indicated by the neural system, but both task variety and visual feedback affected the duration and magnitude of the primary synergies. Thus, for active rehabilitation application, it is advised that if the purpose is to enhance the synergy indication from the neural system, the task completion accuracy should be emphasized, but if the purpose is to expand the motion area, the task variety should be diversified.