Fatiguing Effects of Indirect Vibration Stimulation in Upper Limb Muscles- pre, post and during Isometric Contractions Superimposed on Upper Limb Vibration

© 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ , which permits unrestricted use, provided the original author and source are credited.Whole-body vibration and upper limb vibration (ULV) continue to gain popularity as exercise intervention for rehabilitation and sports applications. However, the fatiguing effects of indirect vibration stimulation are not yet fully understood. We investigated the effects of ULV stimulation superimposed on fatiguing isometric contractions using a purpose developed upper limb stimulation device. Thirteen healthy volunteers were exposed to both ULV superimposed to fatiguing isometric contractions (V) and isometric contractions alone Control (C). Both Vibration (V) and Control (C) exercises were performed at 80% of the maximum voluntary contractions. The stimulation used was 30 Hz frequency of 0.4 mm amplitude. Surface-electromyographic (EMG) activity of the Biceps Brachii, Triceps Brachii and Flexor Carpi Radialis were measured. EMG amplitude (EMGrms) and mean frequency (MEF) were computed to quantify muscle activity and fatigue levels. All muscles displayed significantly higher reduction in MEFs and a corresponding significant increase in EMGrms with the V than the Control, during fatiguing contractions (p < 0.05). Post vibration, all muscles showed higher levels of MEFs after recovery compared to the control. Our results show that near-maximal isometric fatiguing contractions superimposed on vibration stimulation lead to a higher rate of fatigue development compared to the isometric contraction alone in the upper limb muscles. Results also show higher manifestation of mechanical fatigue post treatment with vibration compared to the control. Vibration superimposed on isometric contraction not only seems to alter the neuromuscular function during fatiguing efforts by inducing higher neuromuscular load but also post vibration treatment.Peer reviewedFinal Published versio

A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue

Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results

Evaluation of performance fatigability through surface EMG in health and muscle disease: state of the art

In literature, it is commonly reported that the progress of performance fatigability may be indirectly assessed through the changes in the features of the surface electromyogram (sEMG) signal. In particular, during isometric constant force contractions, changes in the sEMG signal are caused by several physiological factors, such as a decay in muscle fibers conduction velocity (CV), an increase of the degree of synchronization between the firing times of simultaneously active motor units (MUs), by the central nervous system, and a reduction of the recruitment threshold and a modulation of MUs firing rate. Amplitude and spectral parameters may be used to characterize the global contributions to performance fatigability, such as MU control properties and fiber membrane properties, or central and peripheral factors, respectively. In addition, being CV a physiological parameter, its estimation is of marked interest to the study of fatigue both in physiological and in presence of neuromuscular diseases

Evaluation of Concavity Compression Mechanism as a Possible Predictor of Shoulder Muscle Fatigue

This study examined the lived experiences of American Muslim principals who serve in public schools post-9/11 to determine whether global events, political discourse, and the media coverage of Islam and Muslims have affected their leadership and spirituality. The aim of the study was to allow researchers and educators to gain an understanding of the adversities that American Muslims principals have experienced post-9/11 and to determine how to address these adversities, particularly through decisions about educational policy and district leadership. A total of 14 American Muslim school leaders who work in public schools post-9/11 across the United States participated in the study, and a phenomenological methodology was used to direct the data collection and coding. Edelman\u27s political spectacle theory served as the theoretical framework for the research. The findings yielded six themes of political climate, role of the media, inferior and foreign: being seen as the other, unconscious fear, spirituality, and education and communication over spectacle. Further, collective guilt and social responsibility emerged as two additional findings. The research suggests that political spectacle and its effects have a large impact on the lives of American Muslim principals, particularly in regard to their leadership and spirituality

Relating forearm muscle electrical activity to finger forces

The electromyogram (EMG) signal is desired to be used as a control signal for applications such as multifunction prostheses, wheelchair navigation, gait generation, grasping control, virtual keyboards, and gesture-based interfaces [25]. Several research studies have attempted to relate the electromyogram (EMG) activity of the forearm muscles to the mechanical activity of the wrist, hand and/or fingers [41], [42], [43]. A primary interest is for EMG control of powered upper-limb prostheses and rehabilitation orthotics. Existing commercial EMG-controlled devices are limited to rudimentary control capabilities of either discrete states (e.g. hand close/open), or one degree of freedom proportional control [4], [36]. Classification schemes for discriminating between hand/wrist functions and individual finger movements have demonstrated accuracy up to 95% [38], [39], [29]. These methods may provide for increased amputee function, though continuous control of movement is not generally achieved. This thesis considered proportional control via EMG-based estimation of finger forces with the goal of identifying whether multiple degrees of freedom of proportional control information are available from the surface EMG of the forearm. Electromyogram (EMG) activity from the extensor and flexor muscles of the forearm was sensed with bipolar surface electrodes and related to the force produced at the four fingertips during constant-posture, slowly force-varying contractions from 20 healthy subjects. The contractions ranged between 30% maximum voluntary contractions (MVC) extension and 30% MVC flexion. EMG amplitude sampling rate, least squares regularization, linear vs. nonlinear models and number of electrodes used in the system identification were studied. Results are supportive that multiple degrees of freedom of proportional control information are available from the surface EMG of the forearm, at least in healthy subjects. An EMG amplitude sampling frequency of 4.096 Hz was found to produce models which allowed for good EMG amplitude estimates. Least squares regularization with a pseudo-inverse tolerance of 0.055 resulted in significant improvement in modeling results, with an average error of 4.69% MVC-6.59% MVC (maximum voluntary contraction). Increasing polynomial order did not significantly improve modeling results. Results from smaller electrode arrays remained fairly good with as few as six electrodes, with the average %MVC error ranging from 5.13%-7.01% across the four fingers. This study also identified challenges in the current experimental study design and subsequent system identification when EMG-force modeling is performed with four fingers simultaneously. Methods to compensate for these issues have been proposed in this thesis

A novel spatial feature for the identification of motor tasks using high-density electromyography

Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.Peer ReviewedPostprint (published version

Muscle Force Estimation and Fatigue Detection Based on sEMG Signals

Ph.DDOCTOR OF PHILOSOPH

Investigation of localized muscle fatigue

Muscle fatigue is a condition where the ability of the muscle to contract and produce force is reduced. Generally the result of prolonged, relatively strong muscle activity, localized muscle fatigue (LMF) occurs when a muscle or a group of muscles has reduced ability to contract and produce force despite neural stimulation. The causes of physical fatigue include poor workplace practices and lack of regular physical exercise. Signs of fatigue include reduced motivation, blurred vision, increased reflex time and poor concentration – all elements in fatigue-related accidents. Muscle fatigue is a leading cause of workplace and transport-related accidents, as well as work-related musculoskeletal disorders. This thesis reports on an experimental study conducted to determine the effects of LMF on the physiological signals produced during voluntary isometric and cyclic muscle contraction. Surface electromyography (SEMG) was considered relevant for this research because it is the most practical and non-invasive technique for recording such physiological signals. Time and frequency domain responses were extracted from recorded signals and analysed. Statistical analysis on extracted data was carried out using analysis of variance (ANOVA) and non parametric (sign-test) analysis. Sign-test analysis shows a statistically significant change in root-mean-square (RMS) amplitude both before and after the onset of fatigue during cyclic contraction but no statistically significant change in median frequency (MDF). But for isometric contraction the results of sign-test show that there is a statistically significant change in both MDF and RMS before and after the onset of fatigue. Similarly, ANOVA results suggest that for isometric contraction there is a statistically significant change in both MDF and RMS before and after the onset of fatigue. In addition, there is a statistically significant change in RMS amplitude before and after the onset of fatigue during cyclic contraction but no statistically significant change in MDF. The results clearly demonstrate that while SEMG analysis is appropriate for muscular fatigue detection, the use of MDF alone does not provide a reliable and valid measure for LMF detection in real world applications where most tasks require a combination of both isometric and cyclic contractions