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Purpose: To evaluate the post-exercise time course for muscle relaxation and recovery following aerobic exercise in different postural resting positions between the dominant and non-dominant vastus lateralis muscles. Methods: Subjects exercised on an upright cycle ergometer, using only their dominate leg, for 2min at 30% VO₂ peak. Following this warm-up, subjects then cycled for 30min at 60% VO₂ peak. After the aerobic phase, subjects cooled down for 2min at 30% VO₂ peak. Resting mechanomyographic amplitude was measured prior to and following aerobic exercise. Results: There was an approximate $12.6\pm 14.8\%$ and $14.2\pm 15.9\%$ decrease (upright sitting position with the subject’s knee joint angle fixed at 180${}^{\circ }$) and an approximate $6.7\pm 14.5\%$ and $13.8\pm 14.3\%$ decrease (upright sitting position with the subject’s knee joint angle fixed at 90${}^{\circ }$) in normalized mechanomyographic amplitude after aerobic exercise for the dominant and non-dominate vastus lateralis muscles, respectfully. Conclusion: There appears to be a potential cross-over relaxation effect during the sitting postural positions, but not during the lying supine postural positions. Furthermore, it appears that the relaxation from the dominant vastus lateralis muscle potentially influenced the increased relaxation of the non-dominant vastus lateralis muscle.

Surface electromyography (sEMG) is a non-invasive technique to assess the electrical activity of contracting skeletal muscles. sEMG-based muscle fatigue detection plays a key role in sports medicine, ergonomics and rehabilitation. These signals are random, multicomponent, nonlinear and the degree of fluctuations is higher in dynamic contractions. Hence, the extraction of reliable biomarkers remains a challenging task. In this work, an attempt has been made to differentiate non-fatigue, and fatigue conditions using nonlinear techniques, namely, binary and weighted Visibility Graph (VG) features. For this, signals are recorded from the biceps brachii muscle of 52 healthy adult volunteers. These signals are preprocessed, and the contractions associated with the non-fatigue and fatigue conditions are segmented. The graph transformation is performed, and first-order and second-order statistics, along with entropy measures, are extracted from the degree distribution. Parametric and non-parametric machine learning methods are applied for the classification. The results show that the proposed VG approach is able to capture the fluctuations of the signals in non-fatigue and fatigue conditions. Further, all extracted features exhibit a significant difference with $p$$<0.05$. Maximum accuracy of 89.1% is achieved with information gain selected features and extreme learning machines classifier. Additionally, weighted VG features perform better than the binary version with a difference in the accuracy of 5%. It appears that the proposed approach could be used in real-time implementation for the monitoring of muscle fatigue conditions.

The analysis of surface electromyography (sEMG) signals is significant in the detection of muscle fatigue. These signals exhibit a great degree of complexity, nonlinearity, and chaos. Also, presence of high degree of fluctuations in the signal makes its analysis a difficult task. This study aims to analyze the nonlinear dynamics of muscle fatigue conditions using Fuzzy recurrence networks (FRN). Dynamic sEMG signals are measured from biceps brachii muscle of 45 normal subjects referenced to 50% of maximal voluntary contractions (MVC) for this. Recorded signals are then pre-processed and divided into ten equal parts. FRNs are transformed from the signals. The network features, namely average weighted degree (AWD) and Closeness centrality (CC) are extracted to analyze the muscle dynamics during fatiguing conditions. The decrease in these features during fatigue indicates a reduction in signal complexity and an increase in complex network stiffness. Both AWD and CC features are statistically significant with $p<0.05$. Further, these features are classified using Naïve Bayes (NB), k nearest neighbor (kNN) and random forest (RF) algorithms. Maximum accuracy of 96.90% is achieved using kNN classifier for combined FRN features. Thus, the proposed features provide high-quality inputs to the neural networks that may be helpful in analyzing the complexity and stiffness of neuromuscular system under various myoneural conditions.

Background and aim: Fatigue of internal or external rotators of the glenohumeral may alter proprioception in the shoulder joint. Fatigue of shoulder muscles can affect the three-dimentional kinematics of the scapula, and may also alter the glenohumeral and scapular movement pattern, with changes in the scapulohumeral rhythm. Previous studies have shown that with arm elevation, there is a decreased upward rotation of the scapula as well as reduced posterior tilt and external rotation movements with shoulder rotator cuff muscle fatigue. Our aim is to examine the effect of internal rotator fatigue on the proprioception of glenohumeral and scapular active repositioning. Methods: Twenty young healthy subjects with an average age of 20 years were recruited. Each subject performed repetitive concentric exercise (internal rotation) to induce muscles fatigue, which was confirmed by a muscle strength testing using a hand-held dynamometer. Measurement of active repositioning with glenohumeral and scapula repositioning were examined before and after internal rotator fatigue via the three-dimensional (3D) electromagnetic motion analysis system. Results: Fatigue of internal rotators did not affect the glenohumeral and thoracoscapluar joint proprioception (P > 0.05). Conclusion: The findings showed that fatigue of shoulder internal rotators did not contribute to alteration in glenohumeral and scapular proprioception.

In recent years, a robust increasing interest has been observed in wearable devices featuring smart health, smart fitness, and human–machine interaction applications. While we gained some advances on use of surface electromyography (sEMG) signals recorded from upper extremities for controlling external devices, only limited attempt has been made to track the status of targeted muscles and forecast muscle fatigue onset. In this study, we address use of sEMG signals acquired from upper extremities to predict onset of muscle fatigue using deep belief networks (DBNs) as a learning mechanism. We demonstrate that a deep architecture can learn from raw data and provide comparable performance to feature-based approaches. Experimental results show that the DBNs model investigated in this study achieves an average classification accuracy of 85.3% without any subject-oriented calibration and achieves a best case accuracy of 97.60%. A transient-to-fatigue state is introduced before the fatigue onsets as an early warning state. The aim of this paper is to evaluate the performance of the popular deep models in real fatigue detection applications. The model provides a promising result compared with state-of-art works without any feature selection process, which could potentially generate better features while reducing the requirement for expertise in data.

The aim of this study is to investigate the effect of knee extensors fatigue on joint position sense. Fifteen healthy participants, all males, with no history of previous musculoskeletal lesions were recruited. Evaluation of the knee joint position sense and the muscle fatigue protocol had been performed using an isokinetic dynamometer. Fatigue was considered when the maximum torque was reduced by 50%. The joint position sense was analyzed by the absolute error and the variable error. The paired t-test was used to compare the mean in pre and during muscle fatigue conditions. The level of significance was 5%. Absolute and variable errors were not significantly affected by muscle fatigue. Knee joint position sense does not seem to be affected by fatigue of knee joint extensors.

This work aims to analyze the complexity of surface electromyography (sEMG) signals under muscle fatigue conditions using Hjorth parameters and bubble entropy (BE). Signals are recorded from the biceps brachii muscle of 25 healthy males during dynamic and isometric contraction exercises. These signals are filtered and segmented into 10 equal parts. The first and tenth segments are considered as nonfatigue and fatigue conditions, respectively. Activity, mobility, complexity, and BE features are extracted from both segments and classified using support vector machine (SVM), Naïve bayes (NB), k-nearest neighbor (kNN), and random forest (RF). The results indicate a reduction in signal complexity during fatigue. The parameter activity is found to increase under fatigue for both dynamic and isometric contractions with mean values of 0.35 and 0.22, respectively. It is observed that mobility, complexity, and BE are lowest during fatigue for both contractions. Maximum accuracy of 95.00% is achieved with the kNN and Hjorth parameters for dynamic signals. It is also found that the reduction of signal complexity during fatigue is more significant in dynamic contractions. This study confirms that the extracted features are suitable for analyzing the complex nature of sEMG signals. Hence, the proposed approach can be used for analyzing the complex characteristics of sEMG signals under various myoneural conditions.

Tactile feedback is beneficial to improve the hand prosthesis performance, alleviate phantom pain, reduce muscle fatigue, etc. During the manipulation process, muscle fatigue not only causes discomfort to prosthesis users but also disturbs the surface electromyographic (sEMG)-based motion recognition, which significantly deteriorates the prosthesis functional performance. Efforts have been made to explore appropriate signal processing algorithms which could be less influenced by muscle fatigue. However, few studies concern how to alleviate muscle fatigue directly. Thus, this study proposes a novel method to avoid excessive muscle fatigue based on electrotactile feedback. A potable electrotactile stimulator is developed with adjustable parameters, multiple channels and wireless communication. It is implemented in a virtual hand grasping platform driven by sEMG signals to investigate the impact of tactile feedback on muscle fatigue. Experimental results show a higher success rate of grasping with electrotactile feedback than that with no feedback. Moreover, compared with grasp in the no feedback condition, there is an observable decrease of sEMG intensity when grasping a heavy object with electrotactile feedback, despite a comparable performance on the light and medium objects in both feedback conditions. It indicates that tactile feedback helps to alleviate muscle fatigue caused by excessive muscle contraction, especially when large strength is needed.

Background: Various factors, inherited and acquired, are associated with habitual spinal postures.

Objective: The purpose of this study was to identify the relationships between trunk muscle endurance, anthropometry and physical activity/inactivity and the sagittal standing lumbopelvic posture in pain-free young participants.

Methods: In this study, 112 healthy young adults (66 females), with median (IQR) age of 20 years (18.2–22 years), without low back pain, injury or trauma were included. Lumbar curve (LC) and sacral slope (SS) angles were measured in standing with a mobile phone application (iHandy level). Anthropometric, physical activity/inactivity levels (leisure-time sport involvement and sitting hours/day) and abdominal (plank prone bridge test) and paraspinal (Sorensen test) isometric muscle endurance measures were collected.

Results: LC and SS angles correlated significantly ($r=0.80$, $p<0.001$). Statistically significant differences for both LC ($p=0.023$) and SS ($p=0.013$) angles were identified between the male and female participants. A significant negative correlation was identified between the abdominal endurance time and LC ($r=-0.27$, $p=0.004$); however, the power of this result (56%) was not sufficiently high. The correlation between abdominal endurance and SS was non-significant ($r=-0.17$, $p=0.08$). In addition, no significant associations were identified between either of the sagittal lumbopelvic angles (LC–SS) in standing and the participants’ body mass index (BMI), paraspinal endurance, leisure-time sport involvement or sitting hours/day.

Conclusion: The potential role of preventive exercise in controlling lumbar lordosis via enhancement of the abdominal muscle endurance characteristics requires further confirmation. A subsequent study, performed in a larger population of more diverse occupational involvement and leisure-time physical activity levels, is proposed.

The aim of this paper is to investigate the effect of prolonged running on lower limb muscle activity, foot pressure and foot contact area. The treadmill running test was performed at a running velocity of 12 km/h for 20 minutes. Twenty-nine male students from the Army Infantry School took part in this study. For all subjects in our study, a number of variables were analyzed by the prolonged running. The EMG variables included the signal maximum amplitude of EMG linear envelope of all the muscles. Meanwhile, maximal forces and peak foot pressures in 10 anatomically defined areas of the foot, and contact area of the whole foot were analyzed. Running EMG data in each of the phases (phase 2–4) were compared to those at the beginning of the run (phase 1). Dynamic pedography data in phase 4 was compared to those of phase 1. Pedography analysis revealed a significant increase in the maximal forces and peak pressures under the medial midfoot and all forefoot regions. From phase 1 to phase 4, the maximal force increased by 32% under the medial midfoot, 29% under the first metatarsal, 34% under the second and third metatarsal, and 21% under the fourth and fifth metatarsal. The peak pressure under the medial midfoot increased by 19%, under the first metatarsal increased by 21%, under the second and third metatarsal increased by 31%, and under the fourth and fifth metatarsal increased by 21%. The averaged maximum EMG amplitudes of almost all the muscles were increased gradually as time increased. Among them, rectus femoris, gastrocnemius, soleus, and tibialis anterior muscles reach a significant amplitude at the p < 0.05 level. In conclusion, our results showed that a prolonged running under a 20 minutes limitation led to a greater increase in muscle amplitude, midfoot and forefoot loading compared with the beginning of running.

Surface electromyography (EMG) signals classification is currently applied in various prostheses and arm controls using various classification methods. The limited robustness in practical EMG control applications has become an important matter of research consideration. The precision of EMG signal features and parameters proportionally vary with muscle fatigue (MF). The major challenge for the study is to identify the MF manifestation in the EMG signal, so that the control performance is improved. This can be done by the improvement of data collection practicality, features extraction and classification. Hence, fundamental study is performed by investigating the signals acquired from the human upper forearm (UFA) to determine muscle characteristics and to establish the inter-relationship between both muscles of the forearm and upper arm. The aim of the present study is to investigate the applicability of human UFA muscles and MF indices at various force levels of maximum voluntary contraction (MVC). EMG signals are recorded from nine (9) normally limbed subjects. The frequency domain power spectrum density (PSD) is computed in order to derive the useful characteristics of the signal. The results show that only few muscles contributes for the movement. Further analysis show that flexor digitorum superficialis (FDS), flexor carpi radialis (FCR), extensor carpi radialis longus (ECRL), extensor digitorum communis (EDC) and biceps/triceps brachii show interesting results.