Muscle synergies for predicting non-isometric complex hand function for commanding FES neuroprosthetic hand systems

Cole, Natalie M; Ajiboye, A Bolu

doi:10.1088/1741-2552/ab2d47

Cited by 12 publications

(13 citation statements)

References 38 publications

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“…In addition, despite the fact that synergies are task-dependent, the level of reality and the extension of this study (twenty-four healthy subjects performing twenty-four carefully selected ADL) makes it the most representative of functionality to date, providing a novel insight into healthy kinematic characterization in ADL through the underlying functional synergies and their frequency of appearance. The frequency of each kinematic synergy, and the relationship with the muscle synergies obtained in previous works 29,33,34 could be used to achieve a more intuitive control of hand prostheses, but also in hand kinematic dysfunction evaluation, going beyond the current analysis of range of motion 35 . A recent study proposed to define healthy motion patterns 36 for a future clinical application.…”

Section: Discussionmentioning

confidence: 99%

Sharing of hand kinematic synergies across subjects in daily living activities

Gracia-Ibáñez

Sancho-Bru

Jarque-Bou

et al. 2020

Sci Rep

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Roda-Sales the motor system is hypothesised to use kinematic synergies to simplify hand control. Recent studies suggest that there is a large set of synergies, sparse in degrees of freedom, shared across subjects, so that each subject performs each action with a sparse combination of synergies. identifying how synergies are shared across subjects can help in prostheses design, in clinical decision-making or in rehabilitation. Subject-specific synergies of healthy subjects performing a wide number of representative daily living activities were obtained through principal component analysis. to make synergies comparable between subjects and tasks, the hand kinematics data were scaled using normative range of motion data. to obtain synergies sparse in degrees of freedom a rotation method that maximizes the sum of the variances of the squared loadings was applied. Resulting synergies were clustered and each cluster was characterized by a core synergy and different indexes (prevalence, relevance for function and within-cluster synergy similarity), substantiating the sparsity of synergies. The first two core synergies represent finger flexion and were present in all subjects. The remaining core synergies represent coordination of the thumb joints, thumb-index joints, palmar arching or fingers adduction, and were employed by subjects in different combinations, thus revealing different subjectspecific strategies.

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Section: Discussionmentioning

confidence: 99%

Sharing of hand kinematic synergies across subjects in daily living activities

Gracia-Ibáñez

Sancho-Bru

Jarque-Bou

et al. 2020

Sci Rep

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“…However, a feasible alternative to the approach described here might be to use synergies to reduce the dimensionality of the FES control problem. In this way, users would directly control muscle stimulation via a relatively small set of muscle synergies [75,76] or kinematic synergies [77] to enact a wide array of movements [78,79]. Furthermore, adopting a synergy-based approach might aid in identifying optimal interactions among muscles [79].…”

Section: Discussionmentioning

confidence: 99%

Restoration of complex movement in the paralyzed upper limb

et al. 2022

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Objective. Functional electrical stimulation (FES) involves artificial activation of skeletal muscles to reinstate motor function in paralyzed individuals. While FES applied to the upper limb has improved the ability of tetraplegics to perform activities of daily living, there are key shortcomings impeding its widespread use. One major limitation is that the range of motor behaviors that can be generated is restricted to a small set of simple, preprogrammed movements. This limitation stems from the substantial difficulty in determining the patterns of stimulation across many muscles required to produce more complex movements. Therefore, the objective of this study was to use machine learning to flexibly identify patterns of muscle stimulation needed to evoke a wide array of multi-joint arm movements. Approach. Arm kinematics and electromyographic activity from 29 muscles were recorded while a ‘trainer’ monkey made an extensive range of arm movements. Those data were used to train an artificial neural network that predicted patterns of muscle activity associated with a new set of movements. Those patterns were converted into trains of stimulus pulses that were delivered to upper limb muscles in two other temporarily paralyzed monkeys. Results. Machine-learning based prediction of EMG was good for within-subject predictions but appreciably poorer for across-subject predictions. Evoked responses matched the desired movements with good fidelity only in some cases. Means to mitigate errors associated with FES-evoked movements are discussed. Significance. Because the range of movements that can be produced with our approach is virtually unlimited, this system could greatly expand the repertoire of movements available to individuals with high level paralysis.

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“…In this way, the rehabilitation can be focused on the altered synergies, improving motor recovery. Furthermore, modularity of motor control can simplify the control of neuroprosthesis (Cole & Ajiboye, 2019; Piazza et al, 2012), allowing to reproduce the desired movement with a small number of command inputs.…”

Section: Discussionmentioning

confidence: 99%

Spatial and temporal muscle synergies provide a dual characterization of low-dimensional and intermittent control of upper-limb movements

Brambilla

Atzori

Müller

et al. 2022

Preprint

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Muscle synergy analysis is commonly used for investigating the neurophysiological mechanisms that the central nervous system employs to control muscle activations. In the last two decades, several models have been developed to decompose EMG signals into spatial, temporal or spatiotemporal synergies. However, the presence of different approaches complicates the comparison and interpretation of results. Spatial synergies represent invariant activation weights in muscle groups modulated with variant temporal coefficients, while temporal synergies are based on invariant temporal profiles that coordinate variant muscle weights. While non-negative matrix factorization (NMF) allows to extract both spatial and temporal synergies, temporal synergies and the comparison between the two approaches have been barely investigated and so far no study targeted a large set of multi-joint upper limb movements. Here we present several analyses that highlight the duality of spatial and temporal synergies as a characterization of low-dimensional and intermittent motor coordination in the upper limb, allowing high flexibility and dexterity. First, spatial and temporal synergies were extracted from two datasets representing a comprehensive mapping of proximal (REACH PLUS) and distal (NINAPRO) upper limb movements, focusing on their differences in reconstruction accuracy and inter-individual variability. For both models, we extracted synergies achieving a given level of the goodness of reconstruction (R2), and we compared the similarity of the invariant components across participants. The two models provide a compact characterization of motor coordination at spatial or temporal level, respectively. However, a lower number of temporal synergies are needed to achieve the same R2 with a higher inter-subject similarity. Spatial and temporal synergies may thus capture different levels of motor control. Second, we showed the existence of both spatial and temporal structure in the EMG data, extracting spatial and temporal synergies from a surrogate dataset in which the phases were shuffled preserving the same frequency content of the original data. Last, a detailed characterization of the structure of the temporal synergies suggested that they can be related to an intermittent control of the movement. These results may be useful to improve muscle synergy analysis in several fields such as rehabilitation, prosthesis control and motor control studies.

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Muscle synergies for predicting non-isometric complex hand function for commanding FES neuroprosthetic hand systems

Cited by 12 publications

References 38 publications

Sharing of hand kinematic synergies across subjects in daily living activities

Sharing of hand kinematic synergies across subjects in daily living activities

Restoration of complex movement in the paralyzed upper limb

Spatial and temporal muscle synergies provide a dual characterization of low-dimensional and intermittent control of upper-limb movements

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