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001) of the force field; however, the control group (no coupling) only improved their solo performance in the absence of the force field (p less then ; 0.001) but not in the presence of the field (p = 0.81). This suggests that dyadic motor learning can outperform solo learning for two-dimensional tracking motions in the presence of a simple force field, though the mechanism by which learning is improved is not yet clear.Clinical Relevance-As motor learning is critical for applications such as motor rehabilitation, dyadic training could be used to achieve a better overall outcome and a faster learning speed in these applications compared to solo training.Surface electromyography (EMG) decomposition techniques have been applied for human-machine interfacing by decoding neural information, while most of decomposition approaches work offline. Here, we apply an online decomposition scheme to decode motor unit activities during three motor tasks, and measure the recognition accuracy of motor type and activation level using the decomposition results. High-density surface EMG signal were recorded from forearm muscles of six able-bodied subjects. The EMG signals were decomposed into motor unit spike trains (MUST) with a sliding window of 100 ms. The computation complexity had time consumption 99%. The discharge rate of motor units was highly correlated with the activation level of each motion with an average correlation coefficient of 0.94 ± 0.04. These results indicate the feasibility of an online, multi-motion, and proportional control scheme based on neural decoding in a non-invasive way.Control schemes that rely on electromyography (EMG) pattern classification have shown to improve their accuracy when coupled with an increasing number of electrodes. In this study, HD-EMG signals from the hand and forearm of volunteers performing a series of movements were recorded. Different amounts of input EMG channels were selected and time-domain features were extracted to train several SVM classifiers. Detailed comparisons were made to evaluate the impact of electrode count and feature selection over the overall classification accuracy of 17 different movements. The increased resolution achieved from higher electrode counts yielded significant improvements in classification accuracy; however, these improvements were marginal when the number of channels utilized surpassed 100 electrodes.Clinical relevance- Pattern-based EMG classification is a widely used control method for a range of prosthetic devices and robotic interfaces. This work studies the optimal number of simultaneous HD-EMG channels and features that must be considered for accurate myoelectric control using this method.Children with hypotonia of the muscles near the cervical spine have reduced head control and are unable to maintain an upright head posture. These children often use an external head support to hold their heads upright. With their head held in the proper position, they often develop more functional head movements. Previous studies have measured functional changes to subjects using the head support but have not studied the forces exerted on the head support. This study observes subjects with GMFCS Level V and their functional skills alongside the forces exerted on the head support over a 4-month period. A force sensor attached to the base of the head support was used to collect force data to compare with classroom observations of the child's functional performance by occupational and physical therapists. PF-9366 Subjects showed an increase of up to 67% in quadrants where they previously had ¡1% activity at the beginning of the study. Each subject had increased time exerting forces greater than the weight of the head in later weeks of data recording as well as increased peak forces magnitude. Studying the functional impacts of subjects using a head support with measured forces can highlight important aspects of skill development and progress towards milestones for children with hypotonia.Clinical Relevance- While using a head support, children with GMFCS Level V are able to maximize their head movement which helps them develop functional skills.Upper extremity impairments are common among stroke survivors. Robotic devices enable a high-dose of repetitive training for patients, but most systems are confined to the laboratory settings due to their complexity and power requirements. Previously we developed a passive elbow device that can counteract the angle-dependent tone of flexor muscles with hypertonia, but its efficacy was found limited as the increase in passive assistance during elbow extension was found not sufficient to provide assistance to those with more severe impairments. Therefore, in this study, we developed a 'self-adaptable' passive device that adjusts its assistance level based on the movements of patients. In addition to the morphological design to adjust moment arms of the elastic components, we incorporated a self-adaptation mechanism, in which the lengths of the elastic bands were adjusted by a pair of miniature linear motors based on the joint position feedback signals. The capacity of the device was then tested in a pilot testing with two healthy subjects, for whom angle-dependent flexion torque was implemented to simulate flexor hypertonia. The additional adjustment of passive component lengths was found to further increase the elbow extension assistance as the elbow joint extended. The proposed self-adapting mechanism, which does not require any complex control input from the experimenters, can be incorporated with the existing passive device to improve its functional efficacy in home-based training.The performance and safety of human robot interaction (HRI) can be improved by using subject-specific movement prediction. Typical models include biomechanical (parametric) or black-box (non-parametric) models. The current work aims to investigate the benefits and drawbacks of these approaches by comparing elbow-joint torque predictions based on electromyography signals of the elbow flexors and extensors. To this end, a parameterized biomechanical model is compared to a non-parametric (Gaussian-process) approach. Both models showed adequate results in predicting the elbow-joint torques. While the non-parametric model requires minimal modeling effort, the parameterized biomechanical model can lead to deeper insight of the underlying subject specific musculoskeletal system.
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