Muscle contractions in cyclic movements: Optimization of CIMAP algorithm

The Effect of Number of Gait Cycles on Principal Activation Extraction

To cope with the high intra-subject variability of muscle activation intervals, a large amount of gait cycles is necessary to clearly document physiological or pathological muscle activity patterns during human locomotion. The Clustering for Identification of Muscle Activation Pattern (CIMAP) algorithm has been proposed to help clinicians obtaining a synthetic and clear description of normal and pathological muscle functions in human walking. Moreover, this algorithm allows the extraction of Principal Activations (PAs), defined as those muscle activations that are strictly necessary to perform a specific task and occur in every gait cycle. This contribution aims at assessing the impact of the number of gait cycles on the extraction of the PAs. Results demonstrated no statistically significant differences between PAs extracted considering different numbers of gait cycles, revealing, on average, similarity values higher than 0.88.Clinical Relevance-This contribution demonstrates the potential applicability of the CIMAP algorithm to the analysis of subjects affected by neurological disorders, for whom the assessment of motor functions may be of the uttermost importance and only a reduced number of gait cycles can be acquired.

Evaluation of Muscle Function by Means of a Muscle-Specific and a Global Index

Gait analysis applications in clinics are still uncommon, for three main reasons: (1) the considerable time needed to prepare the subject for the examination; (2) the lack of user-independent tools; (3) the large variability of muscle activation patterns observed in healthy and pathological subjects. Numerical indices quantifying the muscle coordination of a subject could enable clinicians to identify patterns that deviate from those of a reference population and to follow the progress of the subject after surgery or completing a rehabilitation program. In this work, we present two user-independent indices. First, a muscle-specific index (MFI) that quantifies the similarity of the activation pattern of a muscle of a specific subject with that of a reference population. Second, a global index (GFI) that provides a score of the overall activation of a muscle set. These two indices were tested on two groups of healthy and pathological children with encouraging results. Hence, the two indices will allow clinicians to assess the muscle activation, identifying muscles showing an abnormal activation pattern, and associate a functional score to every single muscle as well as to the entire muscle set. These opportunities could contribute to facilitating the diffusion of surface EMG analysis in clinics.

Surface Electromyography Applied to Gait Analysis: How to Improve Its Impact in Clinics?

Surface electromyography (sEMG) is the main non-invasive tool used to record the electrical activity of muscles during dynamic tasks. In clinical gait analysis, a number of techniques have been developed to obtain and interpret the muscle activation patterns of patients showing altered locomotion. However, the body of knowledge described in these studies is very seldom translated into routine clinical practice. The aim of this work is to analyze critically the key factors limiting the extensive use of these powerful techniques among clinicians. A thorough understanding of these limiting factors will provide an important opportunity to overcome limitations through specific actions, and advance toward an evidence-based approach to rehabilitation based on objective findings and measurements.

Anodal transcranial direct current stimulation enhances strength training volume but not the force-velocity profile

PURPOSE

This study aimed to explore the acute effect of transcranial direct current stimulation (tDCS) on the force-velocity relationship, strength training volume, movement velocity, and ratings of perceived exertion.

METHODS

Fourteen healthy men (age 22.8 ± 3.0 years) were randomly stimulated over the dorsolateral prefrontal cortex with either ANODAL, CATHODAL or SHAM tDCS for 15 min at 2 mA. The one-repetition maximum (1RM) and force-velocity relationship parameters were evaluated during the bench press exercise before and after receiving the tDCS. Subsequently, participants completed a resistance training session consisting of sets of five repetitions with 1 min of inter-set rest against the 75%1RM until failure.

RESULTS

No significant changes were observed in the 1RM or in the force-velocity relationship parameters (p ≥ 0.377). The number of repetitions was higher for the ANODAL compared to the CATHODAL (p = 0.025; ES = 0.37) and SHAM (p = 0.009; ES = 0.47) conditions. The reductions of movement velocity across sets were lower for the ANODAL than for the CATHODAL and SHAM condition (p = 0.014). RPE values were lower for the ANODAL compared to the CATHODAL (p = 0.119; ES = 0.33) and SHAM (p = 0.150; ES = 0.44) conditions. No significant differences between the CATHODAL and SHAM conditions were observed for any variable.

CONCLUSION

The application of ANODAL tDCS before a resistance training session increased training volume, enabled the maintenance of higher movement velocities, and reduced RPE values. These results suggest that tDCS could be an effective method to enhance resistance-training performance.

How to Improve Robustness in Muscle Synergy Extraction

The muscle synergy theory was widely used in literature to assess the modular organization of the central nervous system (CNS) during human locomotion. The extraction of muscle synergies may be strongly influenced by the preprocessing techniques applied to surface electromyographic (sEMG) signals. The aim of this contribution is to assess the robustness improvement in muscle synergy extraction obtained using an innovative pre-processing technique with respect to the standard procedure. The new pre-processing technique that we propose is based on the extraction of principal muscle activation intervals (necessary to accomplish a specific biomechanical task during gait) from the original sEMG signals, discarding the secondary muscle activation intervals (activations that occur only in some strides with auxiliary functions). Results suggest that the extraction of the principal activation intervals from sEMG provide a more consistent and stable description of the modular organization of the CNS with respect to the standard pre-processing procedure.

Muscle Synergies Extracted Using Principal Activations: Improvement of Robustness and Interpretability

The muscle synergy theory has been widely used to assess the modular organization of the central nervous system (CNS) during human locomotion. The pre-processing approach applied to the surface electromyographic (sEMG) signals influences the extraction of muscle synergies. The aim of this contribution is to assess the improvements in muscle synergy extraction obtained by using an innovative pre-processing approach. We evaluate the improvement in terms of the possible variation in the number of muscle synergies, of the intra-subject consistency, of the robustness, and of the interpretability of the results. The pre-processing approach presented in this paper is based on the extraction of the muscle principal activations (muscle activations strictly necessary to accomplish a specific biomechanical task) from the original sEMG signals, to then obtain muscle synergies using principal activations only. The results herein presented show that the application of this novel approach for the extraction of the muscle synergies provides a more robust and easily interpretable description of the modular organization of the CNS with respect to the standard pre-processing approach.

Asymmetry Index in Muscle Activations

Gait asymmetry is typically evaluated using spatio-temporal or joint kinematics parameters. Only a few studies addressed the problem of defining an asymmetry index directly based on muscle activity, extracting parameters from surface electromyography (sEMG) signals. Moreover, no studies used the extraction of the muscle principal activations (activations that are necessary for accomplishing a specific motor task) as the base to construct an asymmetry index, less affected by the variability of sEMG patterns. The aim of this paper is to define a robust index to quantitatively assess the asymmetry of muscle activations during locomotion, based on the extraction of the principal activations. SEMG signals were analyzed combining statistical gait analysis (SGA) and a clustering algorithm that allows for obtaining the muscle principal activations. We evaluated the asymmetry levels of four lower limb muscles in: (1) healthy subjects of different ages (children, adults, and elderly); (2) different populations of orthopedic patients (adults with megaprosthesis of the knee after bone tumor resection, elderly subjects after total knee arthroplasty, and elderly subjects after total hip arthroplasty); and (3) neurological patients (children with hemiplegic cerebral palsy and elderly subjects affected by idiopathic normal pressure hydrocephalus). The asymmetry index obtained for each pathological population was then compared to that of age-matched controls. We found asymmetry levels consistent with the expected impact of the different pathologies on muscle activation during gait. This suggests that the proposed index can be successfully used in clinics for an objective assessment of the muscle activation asymmetry during locomotion.