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BackgroundMuscle synergies are now widely discussed as a method for evaluating the existence of redundant neural networks that can be activated to enhance stroke rehabilitation. However, this approach was initially conceived to study muscle coordination during learned motions in healthy individuals. After brain damage, there are several neural adaptations that contribute to the recovery of motor strength, with muscle coordination being one of them. In this study, a model is proposed that assesses motion based on surface electromyography (sEMG) according to two main factors closely related to the neural adaptations underlying motor recovery: (1) the correct coordination of the muscles involved in a particular motion and (2) the ability to tune the effective strength of each muscle through muscle fiber contractions. These two factors are hypothesized to be affected differently by brain damage. Therefore, their independent evaluation will play an important role in understanding the origin of stroke-related motor impairments.ResultsThe model proposed was validated by analyzing sEMG data from 18 stroke patients with different paralysis levels and 30 healthy subjects. While the factors necessary to describe motion were stable across heathy subjects, there was an increasing disassociation for stroke patients with severe motor impairment.ConclusionsThe clear dissociation between the coordination of muscles and the tuning of their strength demonstrates the importance of evaluating these factors in order to choose appropriate rehabilitation therapies. The model described in this research provides an efficient approach to promptly evaluate these factors through the use of two intuitive indexes.

The muscle-synergy hypothesis is a widely used method for describing simple motion patterns and how the central nervous system deals with the complexity of controlling a large set of muscles in parallel. However, the physiological interpretation of synergies and the mathematical techniques behind their computation are still far from being properly standardized. This letter proposes a novel approach to obtaining detailed and accurate information about how muscular synergies are triggered during different stages of a given motion. In that regard, a way to find the number of muscle synergies working in parallel is introduced based on the appearance of unexpected high-frequencies in the time series of electromyographic (EMG) signals during synergy extraction. This phenomenon allows the definition of a robust threshold for the number of synergies. The proposed methods are tested on muscle synergies computed from superficial EMG signals recorded during wheel steering. The results show the advantage of the proposed approach in relation to the currently used methods that require segmenting the signal according to the stages of the movement, and suggest future increases in the understanding of motor control from a neurological point of view.

Estimation of muscle activity using surface electromyography (sEMG) is an important non-invasive method that can lead to a deeper understanding of motor-control strategies in humans. Measurement using multiple active electrodes is necessary to estimate not only surface muscle activity but also deep muscle activity in dynamic motion. In this paper, we propose a method for estimating muscle activity of dynamic motions based on anatomical knowledge of muscle structures. To estimate muscle activity, a large number of signal sources are set in the muscle model, and connections between the signal sources are defined a

Averaging electromyographic activity prior to muscle synergy computation is a common method employed to compensate for the inter-repetition variability usually associated with this kind of physiological recording. Capturing muscle synergies requires the preservation of accurate temporal and spatial information for muscle activity. The natural variation in electromyography data across consecutive repetitions of the same task raises several related challenges that make averaging a non-trivial process. Duration and triggering times of muscle activity generally vary across different repetitions of the same task. Therefore, it is necessary to define a robust methodology to segment and average muscle activity that deals with these issues. Emerging from this need, the present work proposes a standard protocol for segmenting and averaging muscle activations from periodic motions in a way that accurately preserves the temporal and spatial information contained in the original data and enables the isolation of a single averaged motion period. This protocol has been validated with muscle activity data recorded from 15 participants performing elbow flexion/extension motions, a series of actions driven by well-established muscle synergies. Using the averaged data, muscle synergies were computed, permitting their behavior to be compared with previous results related to the evaluated task. The comparison between the method proposed and a widely used methodology based on motion flags, shown the benefits of our system maintaining the consistency of muscle activation timings and synergies.