Normalisation of a biarticular muscle EMG signal using a submaximal voluntary contraction: Choice of the standardised isometric task for the rectus femoris, a pilot study

Comparison of kinematic parameters, muscle activity and movement timing between "good" and "poor" performers of a single-leg squat

INTRODUCTION

Visual scales offer a cost-effective alternative to complex biomechanical analysis for single-leg squat (SLS) performance. There is a lack of consensus on the relationship between visual rating and kinematic measurements in physical therapy assessments. The study aimed to compare kinematic parameters, muscle activity and timing between good and poor performers of SLS, selected based on visual evaluation of movement.

METHODS

Forty asymptomatic male volunteers were classified based on a visual assessment of their SLS movements. For participants rated as good performers (n = 19) and poor performers (n = 8), electromyographic activity, kinematic parameters and movement timing were compared.

RESULTS

SLS execution was significantly different in groups in terms of the hip frontal plane and spine transversal plane of movement and the timing of the hip and knee motion. There were no statistically significant differences between groups in multifidus muscle activity during SLS, frontal plane movement of the knee and squat depth.

CONCLUSIONS

The findings of this study indicate that coordination and timing of movements should be carefully considered during SLS examinations to obtain a comprehensive evaluation for planning exercise rehabilitation.

Analysis of induced dynamic biceps EMG signal complexity using Markov transition networks

Purpose

Surface electromyography (sEMG) is a non-invasive technique to characterize muscle electrical activity. The analysis of sEMG signals under muscle fatigue play a crucial part in the branch of neurorehabilitation, sports medicine, biomechanics, and monitoring neuromuscular pathologies. In this work, a method to transform sEMG signals to complex networks under muscle fatigue conditions using Markov transition field (MTF) is proposed. The importance of normalization to a constant Maximum voluntary contraction (MVC) is also considered.

Methods

For this, dynamic signals are recorded using two different experimental protocols one under constant load and another referenced to 50% MVC from Biceps brachii of 50 and 45 healthy subjects respectively. MTF is generated and network graph is constructed from preprocesses signals. Features such as average self-transition probability, average clustering coefficient and modularity are extracted.

Results

All the extracted features showed statistical significance for the recorded signals. It is found that during the transition from non-fatigue to fatigue, average clustering coefficient decreases while average self-transition probability and modularity increases.

Conclusion

The results indicate higher degree of signal complexity during non-fatigue condition. Thus, the MTF approach may be used to indicate the complexity of sEMG signals. Although both datasets showed same trend in results, sEMG signals under 50% MVC exhibited higher separability for the features. The inter individual variations of the MTF features is found to be more for the signals recorded using constant load. The proposed study can be adopted to study the complex nature of muscles under various neuromuscular conditions.