Use of muscle synergies and wavelet transforms to identify fatigue during squatting

Smale, Kenneth B.; Shourijeh, Mohammad S.; Benoît, Daniel

doi:10.1016/j.jelekin.2016.04.008

Cited by 36 publications

(18 citation statements)

References 32 publications

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“…The model indicated that when the cluster-duration was set to 10 ms and the EMG was comprised of only clustered MUAP, there was the largest downward shift in the mean frequency of the EMG from ~82 Hz to ~53 Hz (Figure 5 ; bottom line, diamonds). A downward shift in the EMG power spectrum has been seen experimentally elsewhere in the literature during an isometric vs. dynamic contraction (Merlo et al, 2005 ), eccentric contractions (Linnamo et al, 2002 ), submaximal vs. maximal effort contractions in certain muscles (Pincivero et al, 2002 ), fatiguing dynamic contractions (Komi and Tesch, 1979 ; Smale et al, 2016 ), and purely isometric fatiguing contractions (Bigland-Ritchie et al, 1981 ; Mills, 1982 ; Kuorinka, 1988 ; Krogh-Lund and Jørgensen, 1991 ). Further, the magnitude of the downward shift of the mean frequency was dependent on the size of the cluster duration window and the ratio of clustered to randomly distributed MUAP.…”

Section: Discussionmentioning

confidence: 70%

Motor Unit Action Potential Clustering—Theoretical Consideration for Muscle Activation during a Motor Task

Asmussen

Tscharner

Nigg

2018

Front. Hum. Neurosci.

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During dynamic or sustained isometric contractions, bursts of muscle activity appear in the electromyography (EMG) signal. Theoretically, these bursts of activity likely occur because motor units are constrained to fire temporally close to one another and thus the impulses are “clustered” with short delays to elicit bursts of muscle activity. The purpose of this study was to investigate whether a sequence comprised of “clustered” motor unit action potentials (MUAP) can explain spectral and amplitude changes of the EMG during a simulated motor task. This question would be difficult to answer experimentally and thus, required a model to study this type of muscle activation pattern. To this end, we modeled two EMG signals, whereby a single MUAP was either convolved with a randomly distributed impulse train (EMG-rand) or a “clustered” sequence of impulses (EMG-clust). The clustering occurred in windows lasting 5–100 ms. A final mixed signal of EMG-clust and EMG-rand, with ratios (1:1–1:10), was also modeled. A ratio of 1:1 would indicate that 50% of MUAP were randomly distributed, while 50% of “clustered” MUAP occurred in a given time window (5–100 ms). The results of the model showed that clustering MUAP caused a downshift in the mean power frequency (i.e., ~30 Hz) with the largest shift occurring with a cluster window of 10 ms. The mean frequency shift was largest when the ratio of EMG-clust to EMG-rand was high. Further, the clustering of MUAP also caused a substantial increase in the amplitude of the EMG signal. This model potentially explains an activation pattern that changes the EMG spectra during a motor task and thus, a potential activation pattern of muscles observed experimentally. Changes in EMG measurements during fatiguing conditions are typically attributed to slowing of conduction velocity but could, per this model, also result from changes of the clustering of MUAP. From a clinical standpoint, this type of muscle activation pattern might help describe the pathological movement issues in people with Parkinson’s disease or essential tremor. Based on our model, researchers moving forward should consider how MUAP clustering influences EMG spectral and amplitude measurements and how these changes influence movements.

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Section: Discussionmentioning

confidence: 70%

Motor Unit Action Potential Clustering—Theoretical Consideration for Muscle Activation during a Motor Task

Asmussen

Tscharner

Nigg

2018

Front. Hum. Neurosci.

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“…Our experimental setup presents several advantages over other attempts to characterise synergy adaptations to fatigue ( Turpin et al, 2011 ; Smale et al, 2016 ). First, by performing the fatiguing trials in the directions of synergy PDs, we have isolated the effects of fatigue to a single synergy at a time.…”

Section: Discussionmentioning

confidence: 99%

Fatigue Influences the Recruitment, but Not Structure, of Muscle Synergies

Ortega-Auriol

Besier

Byblow

et al. 2018

Front. Hum. Neurosci.

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The development of fatigue elicits multiple adaptations from the neuromuscular system. Muscle synergies are common patterns of neuromuscular activation that have been proposed as the building blocks of human movement. We wanted to identify possible adaptations of muscle synergies to the development of fatigue in the upper limb. Recent studies have reported that synergy structure remains invariant during the development of fatigue, but these studies did not examine isolated synergies. We propose a novel approach to characterise synergy adaptations to fatigue by taking advantage of the spatial tuning of synergies. This approach allows improved identification of changes to individual synergies that might otherwise be confounded by changing contributions of overlapping synergies. To analyse upper limb synergies, we applied non-negative matrix factorization to 14 EMG signals from muscles of 11 participants performing isometric contractions. A preliminary multidirectional task was used to identify synergy directional tuning. A subsequent fatiguing task was designed to fatigue the participants in their synergies’ preferred directions. Both tasks provided virtual reality feedback of the applied force direction and magnitude, and were performed at 40% of each participant’s maximal voluntary force. Five epochs were analysed throughout the fatiguing task to identify progressive changes of EMG amplitude, median frequency, synergy structure, and activation coefficients. Three to four synergies were sufficient to account for the variability contained in the original data. Synergy structure was conserved with fatigue, but interestingly synergy activation coefficients decreased on average by 24.5% with fatigue development. EMG amplitude did not change systematically with fatigue, whereas EMG median frequency consistently decreased across all muscles. These results support the notion of a neuromuscular modular organisation as the building blocks of human movement, with adaptations to synergy recruitment occurring with fatigue. When synergy tuning properties are considered, the reduction of activation of muscle synergies may be a reliable marker to identify fatigue.

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“…According to this theory, the nervous system builds the muscle activation commands, by combining a few sets of activation (called modules, muscle synergies, or motor primitives). Such a low-dimensionality in muscle activation signals has been observed in a variety of cases, e.g., healthy humans movements (Kutch et al, 2008; Meyer et al, 2016; Sharif Shourijeh et al, 2016; Smale et al, 2016), spinal cord injury (Zariffa et al, 2012), stroke (Cheung et al, 2009, 2012; Clark et al, 2010; Roh et al, 2013; Scano et al, 2017), and cerebral palsy patients (Steele et al, 2015; Tang et al, 2015), frogs (Cheung et al, 2005; Bizzi et al, 2008), and cats (Ting and McKay, 2007; Sohn and Ting, 2016). Muscle synergies are especially appealing from a computational point of view, as the dimension reduction in the control space contributes to the computational efficiency of the control algorithms for musculoskeletal systems.…”

Section: Introductionmentioning

confidence: 89%

On the Relationship Between Muscle Synergies and Redundant Degrees of Freedom in Musculoskeletal Systems

Razavian

Ghannadi

McPhee

2019

Front. Comput. Neurosci.

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It has been suggested that the human nervous system controls motions in the task (or operational) space. However, little attention has been given to the separation of the control of the task-related and task-irrelevant degrees of freedom. Aim: We investigate how muscle synergies may be used to separately control the task-related and redundant degrees of freedom in a computational model. Approach: We generalize an existing motor control model, and assume that the task and redundant spaces have orthogonal basis vectors. This assumption originates from observations that the human nervous system tightly controls the task-related variables, and leaves the rest uncontrolled. In other words, controlling the variables in one space does not affect the other space; thus, the actuations must be orthogonal in the two spaces. We implemented this assumption in the model by selecting muscle synergies that produce force vectors with orthogonal directions in the task and redundant spaces. Findings: Our experimental results show that the orthogonality assumption performs well in reconstructing the muscle activities from the measured kinematics/dynamics in the task and redundant spaces. Specifically, we found that approximately 70% of the variation in the measured muscle activity can be captured with the orthogonality assumption, while allowing efficient separation of the control in the two spaces. Implications: The developed motor control model is a viable tool in real-time simulations of musculoskeletal systems, as well as model-based control of bio-mechatronic systems, where a computationally efficient representation of the human motion controller is needed.

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Use of muscle synergies and wavelet transforms to identify fatigue during squatting

Cited by 36 publications

References 32 publications

Motor Unit Action Potential Clustering—Theoretical Consideration for Muscle Activation during a Motor Task

Motor Unit Action Potential Clustering—Theoretical Consideration for Muscle Activation during a Motor Task

Fatigue Influences the Recruitment, but Not Structure, of Muscle Synergies

On the Relationship Between Muscle Synergies and Redundant Degrees of Freedom in Musculoskeletal Systems

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