Sit-to-stand (STS) motion is an important daily activity, and many post-stroke patients have difficulty performing STS motion. Previous studies found that there are four muscle synergies (synchronized muscle activations) in the STS motion of healthy adults. However, for post-stroke patients, it is unclear whether muscle synergies change and which features primarily reflect motor impairment. Here, we use a machine learning method to demonstrate that temporal features in two muscle synergies that contribute to hip rising and balance maintenance motion reflect the motor impairment of post-stroke patients. Analyzing the muscle synergies of age-matched healthy elderly people (n = 12) and post-stroke patients (n = 33), we found that the same four muscle synergies could account for the muscle activity of post-stroke patients. Also, we were able to distinguish post-stroke patients from healthy people on the basis of the temporal features of these muscle synergies. Furthermore, these temporal features were found to correlate with motor impairment of post-stroke patients. We conclude that poststroke patients can still utilize the same number of muscle synergies as healthy people, but the temporal structure of muscle synergies changes as a result of motor impairment. This could lead to a new rehabilitation strategy for poststroke patients that focuses on activation timing of muscle synergies.

The development of a method to feed proper environmental inputs back to the central nervous system (CNS) remains one of the challenges in achieving natural movement when part of the body is replaced with an artificial device. Muscle synergies are widely accepted as a biologically plausible interpretation of the neural dynamics between the CNS and the muscular system. Yet the sensorineural dynamics of environmental feedback to the CNS has not been investigated in detail. In this study, we address this issue by exploring the concept of sensory synergy. In contrast to muscle synergy, we hypothesize that sensory synergy plays an essential role in integrating the overall environmental inputs to provide low-dimensional information to the CNS. We assume that sensor synergy and muscle synergy communicate using these low-dimensional signals. To examine our hypothesis, we conducted posture control experiments involving lateral disturbance with nine healthy participants. Proprioceptive information represented by the changes on muscle lengths were estimated by using the musculoskeletal model analysis software SIMM. Changes on muscles lengths were then used to compute sensory synergies. The experimental results indicate that the environmental inputs were translated into the two dimensional signals and used to move the upper limb to the desired position immediately after the lateral disturbance. Participants who showed high skill in posture control were found to be likely to have a strong correlation between sensory and muscle signaling as well as high coordination between the utilized sensory synergies. These results suggest the importance of integrating environmental inputs into suitable low-dimensional signals before providing them to the CNS. This mechanism should be essential when designing the prosthesis' sensory system to make the controller simpler.

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Current work highlights the importance of muscle selection to evaluate paralysis and recovery level of stroke patients when comparing synergies of affected and non-affected side of the body. The proposed method allows the selection of important muscles that highly contribute to the specific movements according to the power and frequency distribution of the electromyographic signals.. Users participating performed steering-wheel-based therapy focused on upper limb rehabilitation. Final results show that with the appropriate muscles selection, it is possible to compute a Similarity Index between right and left arms (during symmetric motion) associated to the level of paralysis and potential recovery of a given subject.

Standing-up motion is an important daily activity. It has been known that elderly and post-stroke patients have difficulty in performing standing-up motion. The standing-up motion is retrained by therapists to maximize independence of the elderly and post-stroke patients, but it is not clear how the elderly and post-stroke patients control their redundant muscles to achieve standing-up motion. This study employed the concept of muscle synergy to analyze how healthy young adults, healthy elderly people and post-stroke patients control their muscles. Experimental result verified that four muscle synergies can represent human standing-up motion. In addition, it indicated that the post-stroke patients shift the weights of muscle synergies to finish standing-up motion comparing to healthy subjects. Moreover, different muscle synergy structures were associated with the CoM and joint kinematics.

Unilateral arm paralysis is a common symptom of stroke. In stroke patients, we observed that self-guided biomechanical support by the nonparetic arm unexpectedly triggered electromyographic activity with normal muscle synergies in the paretic arm. The muscle activities on the paretic arm became similar to the muscle activities on the nonparetic arm with self-supported exercises that were quantified by the similarity index (SI). Electromyogram (EMG) signals and functional near-infrared spectroscopy (fNIRS) of the patients (n=54) showed that self-supported exercise can have an immediate effect of improving the muscle activities by 40-80% according to SI quantification, and the muscle activities became much more similar to the muscle activities of the age-matched healthy subjects. Using this self-supported exercise, we investigated whether the recruitment of a patient's contralesional nervous system could reactivate their ipsilesional neural circuits and stimulate functional recovery. We proposed biofeedback training with self-supported exercise where the muscle activities were visualized to encourage the appropriate neural pathways for activating the muscles of the paretic arm. We developed the biofeedback system and tested the recovery speed with the patients (n=27) for 2 months. The clinical tests showed that self-support-based biofeedback training improved SI approximately by 40%, Stroke Impairment Assessment Set (SIAS) by 35%, and Functional Independence Measure (FIM) by 20%. INDEX TERMS Stroke rehabilitation, muscle synergy, brain imagining, biofeedback training.

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Brain damage due to stroke often leaves survivors with lateral functional motor deficits. Bimanual rehabilitation of the paretic arm is an active field of research aimed at restoring normal functionality by making use of the complex neural bindings that exist between the arms. In search of an effective rehabilitation method, we introduced a group of poststroke rehabilitation patients to a set of bimanual motion tasks with inter-manual coupling and phasing. The surface EMG profiles of the patients were compared in order to understand the effect of the motion conditions. The paretic arms of the patients were more strongly affected by the task conditions compared with the non-paretic arms. These results suggest that in-phase motion may activate neural circuits that trigger recovery. Coupling also had an effect on behavior, but the response of patients was divided between those whom coupling helped or hindered. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.