Asymmetry Index in Muscle Activations

Exploring lower limb muscle activity and performance variations during instrumented Sit-to-Stand-to-Sit in sedentary individuals: Influence of limb dominance and testing modalities

PURPOSE

to explore lower limb muscle activity concerning limb dominance, as well as variations in force and power during the standing up and sitting down phases of the instrumented sit-to-stand-to-sit test in sedentary individuals, across isokinetic and isotonic modalities.

METHODS

33 sedentary individuals underwent testing using a functional electromechanical dynamometer in both isokinetic and isotonic modes, accompanied by surface electromyography.

RESULTS

In the isokinetic mode, the non-dominant gastrocnemius medialis and vastus medialis exhibited significantly (p < 0.05) higher muscle activity values during the standing up and sitting down phase compared to dominant counterparts. In the isotonic mode standing up phase, significant differences in muscle activity were noted for non-dominant gastrocnemius medialis, vastus medialis, and biceps femoris compared to their dominant counterparts. The sitting down phase in isotonic mode showed higher muscle activity for non-dominant vastus medialis compared to dominant vastus medialis. Regard to performance outcomes, significantly lower (p < 0.0001) values were observed for standing up (12.7 ± 5.1 N/kg) compared to sitting down (15.9 ± 6.1 N/kg) peak force, as well as for standing up (18.7 ± 7.8 W/kg) compared to sitting down (25.9 ± 9.7 W/kg) peak power in isokinetic mode. In isotonic mode, lower values were found for sitting down (6.5 (6.3-7.1) N/kg) compared to standing up (7.8 (7.3-8.9) N/kg) peak force and for sitting down (18.5 (13.2-21.7) W/kg) compared to standing up (33.7 (22.8-41.6) W/kg) peak power.

CONCLUSIONS

Limb dominance influences lower-limb muscle activity during the instrumented sit-to-stand-to-sit test, and the choice of testing mode (isokinetic or isotonic) affects muscle engagement and performance outcomes.

A Prospective Randomized Controlled Trial Assessing the Impact of Preoperative Combined with Postoperative Progressive Resistance Training on Muscle Strength, Gait, Balance and Function in Patients Undergoing Total Hip Arthroplasty

Purpose

The aim of this study is to investigate the effects of a preoperative combined with postoperative moderate-intensity progressive resistance training (PRT) of the operative side in patients with hip osteoarthritis (HOA) who are undergoing total hip arthroplasty (THA). The study seeks to evaluate the impact of this combined intervention on muscle strength, gait, balance, and hip joint function in a controlled, measurable, and objective manner. Additionally, the study aims to compare the outcomes of this combined intervention with those of preoperative or postoperative muscle strength training conducted in isolation.

Methods

A total of 90 patients with HOA scheduled for unilateral primary THA were randomly assigned to three groups: Pre group (preoperative PRT), Post group (postoperative PRT), and Pre& Post group (preoperative combined with postoperative PRT) focusing on hip flexion, extension, adduction, and abduction of operated side. Muscle strength, gait parameters, balance, and hip function were assessed at specific time points during a 12-month follow-up period.

Results

All three groups showed significant improvements in muscle strength, with the Pre& Post group demonstrating the most pronounced and sustained gains. Gait velocity and cadence were significantly improved in the Pre& Post group at 1-month and 3-month postoperative follow-ups compared to the other groups. Similarly, the Pre& Post group exhibited superior balance performance at 3-month and 12-month postoperative follow-ups. The Harris Hip Score also showed better outcomes in the Pre& Post group at all follow-up intervals.

Conclusion

Preoperative combined with postoperative moderate-intensity PRT in HOA patients undergoing THA led to superior improvements in muscle strength, gait, balance, and hip joint function compared to preoperative or postoperative PRT alone. This intervention shows significant promise in optimizing postoperative rehabilitation and enhancing patient outcomes following THA.

Adverse effects of unilateral transfemoral amputation on para-alpine sit skiers and mitigation methods

OBJECTIVES

This study aimed to evaluate the adverse effects of unilateral transfemoral amputation on neuromuscular and kinematic parameters in alpine sit skiers, and to determine if additional restraints on the human-bucket interface could help mitigate the effects.

DESIGN

Cross-sectional, repeated measures study.

METHODS

Simulated skiing tests were conducted indoors involving 10 skiers with unilateral transfemoral amputation and 10 able-bodied participants. A Paralympic silver medalist performed slalom skiing tests on snow. These tests were conducted with and without additional strapping on the residual limb. Surface electromyography of trunk muscles and athletic performance was measured, and the asymmetry index was calculated.

RESULTS

Athletes were significantly dependent on muscle activation on the dominant side (asymmetry index = 7.8 %-28.3 %, p < 0.05). Worse athletic performance to the dominant side was found based on inclination angles of the indoor board (asymmetry index = -9.8 %, p = 0.014) and outdoor sit ski (-11.1 %, p = 0.006), and distance to the gate poles during skiing turns (18.6 %, p < 0.001). After using additional restraints, the above asymmetry index declined significantly (asymmetry index < 4.5 %, p < 0.05). Furthermore, athletic performance was significantly improved on both body sides by 11.1 %-30.7 % (p < 0.05).

CONCLUSIONS

Unilateral transfemoral amputation caused the dependence on the trunk muscles of the dominant side and the corresponding unilateral poor performance in athletes. Adjusting restraints in the human-equipment interface by additional strapping could mitigate the asymmetry issues and improve athletic performance.

Influence of Amputation on Kinetic Chain Musculature Activity During Basic and Modified Core Exercises

PURPOSE

Core strength is vital for athletic performance, and many more exercises that involve the kinetic chain have been designed for able-bodied athletes. Disabilities that impair the kinetic chain can reduce the effectiveness of strength training. However, the impact of amputation on core strength training of people with disabilities and its underlying mechanism remains unclear. This study aimed to evaluate the muscle activation patterns and levels in athletes with amputation during 4 basic and modified weight-bearing core strength-training exercises.

METHODS

Fifteen elite athletes with unilateral amputation (170.6 [7.3] cm; 63.9 [11.9] kg; 25.9 [5.3] y) volunteered for this study. Surface electromyography was used to measure the muscle activity mainly in the lumbopelvic-hip complex-stabilizing muscles during 4 kinetic chain trunk exercises with and without modifications.

RESULTS

The significance level was set at α = .05. The results showed a significant difference in muscle activation between different body sides (P < .05). Specifically, amputation on the support position resulted in a diagonal pattern of muscle activation, and amputation on the free distal segments resulted in a unilateral dominant pattern with higher activation in muscles on the nonamputated side (P < .05). Modifications led to significant decreases in muscle activation asymmetry index (P < .05).

CONCLUSIONS

Amputation caused muscle activation asymmetry and 2 activation patterns. Modifications by enhancing proximal stability and adjusting distal loading effectively reduced the asymmetry of muscle activation. Coaches and clinicians can use these results to tailor exercises for athletes with disabilities in training and rehabilitation.

Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models

Gait analysis has been studied over the last few decades as the best way to objectively assess the technical outcome of a procedure designed to improve gait. The treating physician can understand the type of gait problem, gain insight into the etiology, and find the best treatment with gait analysis. The gait parameters are the kinematics, including the temporal and spatial parameters, and lack the activity information of skeletal muscles. Thus, the gait analysis measures not only the three-dimensional temporal and spatial graphs of kinematics but also the surface electromyograms (sEMGs) of the lower limbs. Now, the shoe-worn GaitUp Physilog® wearable inertial sensors can easily measure the gait parameters when subjects are walking on the general ground. However, it cannot measure muscle activity. The aim of this study is to measure the gait parameters using the sEMGs of the lower limbs. A self-made wireless device was used to measure the sEMGs from the vastus lateralis and gastrocnemius muscles of the left and right feet. Twenty young female subjects with a skeletal muscle index (SMI) below 5.7 kg/m2 were recruited for this study and examined by the InBody 270 instrument. Four parameters of sEMG were used to estimate 23 gait parameters. They were measured using the GaitUp Physilog® wearable inertial sensors with three machine learning models, including random forest (RF), decision tree (DT), and XGBoost. The results show that 14 gait parameters could be well-estimated, and their correlation coefficients are above 0.800. This study signifies a step towards a more comprehensive analysis of gait with only sEMGs.

Classification of gait phases based on a machine learning approach using muscle synergy

The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.

Individuals differ in muscle activation patterns during early adaptation to a powered ankle exoskeleton

Exoskeletons have the potential to assist users and augment physical ability. To achieve these goals across users, individual variation in muscle activation patterns when using an exoskeleton need to be evaluated. This study examined individual muscle activation patterns during walking with a powered ankle exoskeleton. 60% of the participants were observed to reduce medial gastrocnemius activation with exoskeleton powered and increase with the exoskeleton unpowered during stance. 80% of the participants showed a significant increase in tibialis anterior activation upon power addition, with inconsistent changes upon power removal during swing. 60% of the participants that were able to adapt to the system, did not de-adapt after 5 min. Muscle activity patterns differ between individuals in response to the exoskeleton power state, and affected the antagonist muscle behavior during this early adaptation. It is important to understand these different individual behaviors to inform the design of exoskeleton controllers and training protocols.

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.

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

BACKGROUND

The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.

METHODS

First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager-Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis.

RESULTS

The proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR.

CONCLUSIONS

The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/distinction of muscle activity from background noise in sEMG signals.

Walking on a Vertically Oscillating Platform with Simulated Gait Asymmetry

Asymmetric gait is associated with pain, injury, and reduced stability in patient populations. Data from side by side walking suggest that unintentional synchronization with an external cue may reduce gait asymmetry. Two types of asymmetric gait were examined here: (1) mass imbalance between limbs to simulate single limb amputation and (2) restriction of plantarflexion during toe-off to simulate reduced propulsion from neurological impairment. Twenty-five healthy participants walked normally and with simulated gait asymmetry on a custom-designed treadmill that oscillated in the vertical direction via pneumatic actuation (amplitude: 2 cm, frequency: participant’s preferred step frequency). Swing Time Asymmetry (STA) and Phase Coordination Index (PCI) both increased significantly with the application of unilateral mass and plantarflexion restriction (p < 0.001). However, walking with simulated asymmetry did not alter unintentional synchronization with the treadmill motion. Further, oscillation of the treadmill did not improve STA or PCI while walking with simulated asymmetry. Analysis of synchronized step clusters using the Weibull survival function revealed that synchronization with the platform persisted for longer durations when compared with data from side by side walking. These results suggest that walking on a vertically oscillating surface may not be an effective approach for improving gait asymmetry.

Critical Issues and Imminent Challenges in the Use of sEMG in Return-To-Work Rehabilitation of Patients Affected by Neurological Disorders in the Epoch of Human-Robot Collaborative Technologies

Patients affected by neurological pathologies with motor disorders when they are of working age have to cope with problems related to employability, difficulties in working, and premature work interruption. It has been demonstrated that suitable job accommodation plans play a beneficial role in the overall quality of life of pathological subjects. A well-designed return-to-work program should consider several recent innovations in the clinical and ergonomic fields. One of the instrument-based methods used to monitor the effectiveness of ergonomic interventions is surface electromyography (sEMG), a multi-channel, non-invasive, wireless, wearable tool, which allows in-depth analysis of motor coordination mechanisms. Although the scientific literature in this field is extensive, its use remains significantly underexploited and the state-of-the-art technology lags expectations. This is mainly attributable to technical and methodological (electrode-skin impedance, noise, electrode location, size, configuration and distance, presence of crosstalk signals, comfort issues, selection of appropriate sensor setup, sEMG amplitude normalization, definition of correct sEMG-related outcomes and normative data) and cultural limitations. The technical and methodological problems are being resolved or minimized also thanks to the possibility of using reference books and tutorials. Cultural limitations are identified in the traditional use of qualitative approaches at the expense of quantitative measurement-based monitoring methods to design and assess ergonomic interventions and train operators. To bridge the gap between the return-to-work rehabilitation and other disciplines, several teaching courses, accompanied by further electrodes and instrumentations development, should be designed at all Bachelor, Master and PhD of Science levels to enhance the best skills available among physiotherapists, occupational health and safety technicians and ergonomists.

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.

Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals

BACKGROUND

Machine learning models were satisfactorily implemented for estimating gait events from surface electromyographic (sEMG) signals during walking. Most of them are based on inter-subject approaches for data preparation. Aim of the study is to propose an intra-subject approach for binary classifying gait phases and predicting gait events based on neural network interpretation of sEMG signals and to test the hypothesis that the intra-subject approach is able to achieve better performances compared to an inter-subject one. To this aim, sEMG signals were acquired from 10 leg muscles in about 10.000 strides from 23 healthy adults, during ground walking, and a multi-layer perceptron (MLP) architecture was implemented.

RESULTS

Classification/prediction accuracy was tested vs. the ground truth, represented by the foot-floor-contact signal provided by three foot-switches, through samples not used during training phase. Average classification accuracy of 96.1 ± 1.9% and mean absolute value (MAE) of 14.4 ± 4.7 ms and 23.7 ± 11.3 ms in predicting heel-strike (HS) and toe-off (TO) timing were provided. Performances of the proposed approach were tested by a direct comparison with performances provided by the inter-subject approach in the same population. Comparison results showed 1.4% improvement of mean classification accuracy and a significant (p < 0.05) decrease of MAE in predicting HS and TO timing (23% and 33% reduction, respectively).

CONCLUSIONS

The study developed an accurate methodology for classification and prediction of gait events, based on neural network interpretation of intra-subject sEMG data, able to outperform more typical inter-subject approaches. The clinically useful contribution consists in predicting gait events from only EMG signals from a single subject, contributing to remove the need of further sensors for the direct measurement of temporal data.

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.