An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

The Effect of Walking in High Heels on the Activation and Deactivation of Upper Trunk Muscles

The aim of the study was to investigate how high-heeled walking affects the coordination changes of timing of upper trunk muscle activation, and the possible occurrence of health problems in this part of the body of young women. We used surface electromyography (EMG) for data collection. The research group consisted of 30 women. Statistical significance of the changes in muscle coordination was confirmed when evaluating two of the four upper trunk muscles studied. M. trapezius and m. pectoralis major are not subject to changes in gait in high heels (HH) from the point of view of timing on a statistical level, but HH increase the intensity of muscle contraction of all monitored muscles, and therefore we recommend limiting the wearing of HH in case of health problems related to these muscles.

Development of running is not related to time since onset of independent walking, a longitudinal case study

Introduction

Children start to run after they master walking. How running develops, however, is largely unknown.

Methods

We assessed the maturity of running pattern in two very young, typically developing children in a longitudinal design spanning about three years. Leg and trunk 3D kinematics and electromyography collected in six recording sessions, with more than a hundred strides each, entered our analysis. We recorded walking during the first session (the session of the first independent steps of the two toddlers at the age of 11.9 and 10.6 months) and fast walking or running for the subsequent sessions. More than 100 kinematic and neuromuscular parameters were determined for each session and stride. The equivalent data of five young adults served to define mature running. After dimensionality reduction using principal component analysis, hierarchical cluster analysis based on the average pairwise correlation distance to the adult running cluster served as a measure for maturity of the running pattern.

Results

Both children developed running. Yet, in one of them the running pattern did not reach maturity whereas in the other it did. As expected, mature running appeared in later sessions (>13 months after the onset of independent walking). Interestingly, mature running alternated with episodes of immature running within sessions. Our clustering approach separated them.

Discussion

An additional analysis of the accompanying muscle synergies revealed that the participant who did not reach mature running had more differences in muscle contraction when compared to adults than the other. One may speculate that this difference in muscle activity may have caused the difference in running pattern.

Influence of the number of muscles and strides on selective motor control during gait in individuals with cerebral palsy

OBJECTIVE

To evaluate the influence of the number of muscles and strides on estimating motor control accuracy during treadmill-gait, in individuals with cerebral palsy (CP).

METHODS

Bilateral lower limb electromyography data were extracted for 44 children/adolescents with CP. The number of synergy solutions required to explain 90 % of the variance (tVAF-threshold) and the total variance accounted for by one synergy (tVAF₁) were calculated for a different number of strides (between 5 and 50) and muscles both unilaterally (four to seven) and bilaterally (eight to 14). The kappa and intraclass correlation coefficients were used to assess similarities in tVAF-threshold and tVAF₁ between the different number of strides and muscle sets.

RESULTS

In both analyses, the number of muscles influenced the tVAF-threshold. Additionally, using <30 strides led to only substantial-moderate agreement with 50 strides (k < 0.80). In both analyses, the mean tVAF₁ values demonstrated high-agreement between the different number of muscles (intraclass-correlations = 0.88-0.93) and strides (intraclass-correlations = 0.96-0.99); In the group level, it may result in an error of ≤2.3 %. However, in the individual level, using different number of muscles or <40 strides may result in an error of ≥6 %.

CONCLUSION

Differing numbers of muscles and strides did not influence the group mean tVAF₁ value, but it influenced the tVAF-threshold value. In addition, using different number of muscles or strides can lead to a large measurement error in the individual tVAF₁ value.

Evidence for shared neural information between muscle synergies and corticospinal efficacy

Stroke survivors often exhibit gait dysfunction which compromises self-efficacy and quality of life. Muscle Synergy Analysis (MSA), derived from electromyography (EMG), has been argued as a method to quantify the complexity of descending motor commands and serve as a direct correlate of neural function. However, controversy remains regarding this interpretation, specifically attribution of MSA as a neuromarker. Here we sought to determine the relationship between MSA and accepted neurophysiological parameters of motor efficacy in healthy controls, high (HFH), and low (LFH) functioning stroke survivors. Surface EMG was collected from twenty-four participants while walking at their self-selected speed. Concurrently, transcranial magnetic stimulation (TMS) was administered, during walking, to elicit motor evoked potentials (MEPs) in the plantarflexor muscles during the pre-swing phase of gait. MSA was able to differentiate control and LFH individuals. Conversely, motor neurophysiological parameters, including soleus MEP area, revealed that MEP latency differentiated control and HFH individuals. Significant correlations were revealed between MSA and motor neurophysiological parameters adding evidence to our understanding of MSA as a correlate of neural function and highlighting the utility of combining MSA with other relevant outcomes to aid interpretation of this analysis technique.

An Objective, Information-Based Approach for Selecting the Number of Muscle Synergies to be Extracted via Non-Negative Matrix Factorization

Muscle synergy analysis is a useful tool for the evaluation of the motor control strategies and for the quantification of motor performance. Among the parameters that can be extracted, most of the information is included in the rank of the modular control model (i.e. the number of muscle synergies that can be used to describe the overall muscle coordination). Even though different criteria have been proposed in literature, an objective criterion for the model order selection is needed to improve reliability and repeatability of MSA results. In this paper, we propose an Akaike Information Criterion (AIC)-based method for model order selection when extracting muscle synergies via the original Gaussian Non-Negative Matrix Factorization algorithm. The traditional AIC definition has been modified based on a correction of the likelihood term, which includes signal dependent noise on the neural commands, and a Discrete Wavelet decomposition method for the proper estimation of the number of degrees of freedom of the model, reduced on a synergy-by-synergy and event-by-event basis. We tested the performance of our method in comparison with the most widespread ones, proving that our criterion is able to yield good and stable performance in selecting the correct model order in simulated EMG data. We further evaluated the performance of our AIC-based technique on two distinct experimental datasets confirming the results obtained with the synthetic signals, with performances that are stable and independent from the nature of the analysed task, from the signal quality and from the subjective EMG pre-processing steps.