Background The majority of stroke survivors have to cope with deficits in arm function, which is often measured with subjective clinical scales. The objective of this study is to examine whether circle drawing metrics are suitable objective outcome measures for measuring upper extremity function of stroke survivors. Methods Stroke survivors (n = 16) and healthy subjects (n = 20) drew circles, as big and as round as possible, above a table top. Joint angles and positions were measured. Circle area and roundness were calculated, and synergistic movement patterns were identified based on simultaneous changes of the elevation angle and elbow angle. Results Stroke survivors had statistically significant lower values for circle area, roundness and joint excursions, compar-ed to healthy subjects. Stroke survivors moved significantly more within synergistic movement patterns, compared to healthy sub-jects. Strong correlations between the proximal upper extremity part of the Fugl-Meyer scale and circle area, roundness, joint ex-cursions and the use of synergistic movement patterns were found. Conclusions The present study showed statistically significant differences in circle area, roundness and the use of synergistic movement patterns between healthy subjects and stroke survivors. These circle metrics are strongly correlated to stroke severity, as indicated by the proximal upper extremity part of the FM score.

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This paper addresses the problem of modelling, control, and simulation of a mechanical system actuated by an agonist-antagonist musculotendon subsystem. Contraction dynamics is given by case I of Zajac’s model. Saturated semi positive proportional-derivative-type controllers with switching as neural excitation inputs are proposed. Stability theory of switched system and SOSTOOLS, which is a sum of squares optimization toolbox of Matlab, are used to determine the stability of the obtained closed-loop system. To corroborate the obtained theoretical results numerical simulations are carried out. As additional contribution, the discussed ideas are applied to the biomimetic control of a DC motor, i.e., the position control is addressed assuming the presence of musculotendon actuators. Real-experiments corroborate the expected results.

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A common feature in biological neuromuscular systems is the redundancy in joint actuation. Understanding how these redundancies are resolved in typical joint movements has been a long-standing problem in biomechanics, neuroscience and prosthetics. Many empirical studies have uncovered neural, mechanical and energetic aspects of how humans resolve these degrees of freedom to actuate leg joints for common tasks like walking. However, a unifying theoretical framework that explains the many independent empirical observations and predicts individual muscle and tendon contributions to joint actuation is yet to be established. Here we develop a computational framework to address how the ankle joint actuation problem is resolved by the neuromuscular system in walking. Our framework is founded upon the proposal that a consideration of both neural control and leg muscle-tendon morphology is critical to obtain predictive, mechanistic insight into individual muscle and tendon contributions to joint actua-tion. We examine kinetic, kinematic and electromyographic data from healthy walking subjects to find that human leg muscle-tendon morphology and neural activations enable a metabolically optimal realization of biological ankle mechanics in walking. This optimal realization corresponds to independent empirical observations of operation and performance of the soleus and gastrocnemius muscles, gives rise to an efficient load-sharing amongst ankle muscle-tendon units and causes soleus and gastrocnemius muscle fibers to take on distinct mechanical roles of force generation and power production at the end of stance phase in walking. The framework outlined here suggests that the dynamical interplay between leg structure and neural control may be key to the high walking economy of humans, and has implications as a means to obtain insight into empirically inaccessible features of individual muscle and tendons in biomechanical tasks.

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We propose a novel method for movement assistance that is based on adaptive oscillators, i.e., mathematical tools that are capable of extracting the high-level features (amplitude, frequency, and offset) of a periodic signal. Such an oscillator acts like a filter on these features, but keeps its output in phase with respect to the input signal. Using a simple inverse model, we predicted the torque produced by human participants during rhythmic flexion extension of the elbow. Feeding back a fraction of this estimated torque to the participant through an elbow exoskeleton, we were able to prove the assistance efficiency through a marked decrease of the biceps and triceps electromyography. Importantly, since the oscillator adapted to the movement imposed by the user, the method flexibly allowed us to change the movement pattern and was still efficient during the nonstationary epochs. This method holds promise for the development of new robot-assisted rehabilitation protocols because it does not require prespecifying a reference trajectory and does not require complex signal sensing or single-user calibration: the only signal that is measured is the position of the augmented joint. In this paper, we further demonstrate that this assistance was very intuitive for the participants who adapted almost instantaneously.

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This study examines the feasibility of using electroencephalograms (EEGs) to rapidly detect the intent to open one’s hand in individuals with complete hand paralysis following a subcortical ischemic stroke. If detectable, this motor planning activity could be used in real time to trigger a motorized hand exoskeleton or an electrical stimulation device that opens/closes the hand. While EEG-triggered movement-assist devices could restore function, they may also promote recovery by reinfor-cing the use of remaining cortical circuits. EEGs were recorded while participants were cued to either relax or attempt to extend their fingers. Linear discriminant analysis was used to detect onset of finger extension from the EEGs in a leave-onetrial-out cross-validation process. In each testing trial, the classifier was applied in pseudo real time starting from an initial hand-relaxed phase, through movement planning, and into the initial attempted finger extension phase (finger extension phase estimated from typical time-to-movement-onset measured in the unaffected hand). The classifiers detected attempted finger-extension at a significantly higher rate during both motor planning and early attempted execution compared to rest.

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