A Comparative Electromyographic Analysis of Muscle Fatigue in Traditional Handwriting, Typing, and the Palmer Method: Implications for Ergonomic Writing Practices

Conduction velocity in ischemic muscle: effect on EMG frequency spectrum.

The prevention of work‐related musculoskeletal disorders is one of the main goals in ergonomics. Among others, surface electromyography (sEMG) is an important tool for the evaluation of risks related to work activity. Three main issues have been approached in ergonomics via sEMG: 1) the analysis of muscle activation, 2) the analysis of exerted forces and torques, and 3) the analysis of muscle fatigue. Many studies have been carried out in static conditions. In ergonomics, however, it is more relevant to study muscle activity and fatigue during real tasks that are, in general, dynamic. From isometric to dynamic contractions, the complexity of the interpretation of sEMG signals increases considerably. Changes in sEMG signals are related to the continuous modifications in force output, muscle fiber length, and relative position of surface electrodes and sources. To increase the reliability of the information extracted from sEMG, multichannel detection systems have been applied, showing the possibility of overcoming some limits of the standard technique. Some illustrative laboratory and field studies are reported in this work to illustrate the potentialities and the open problems in the use of multichannel sEMG in ergonomics. Case 1 is a laboratory study investigating the myoelectric manifestations of fatigue in the biceps brachii (BB) during dynamic elbow flexion/extension. Case 2 is a laboratory study investigating the myoelectric manifestations of fatigue during a repetitive lifting task. Case 3 is a field study, carried out in an automotive plant, investigating muscle activation during the welding of a car door. Many factors play a leading role in the correct interpretation of information provided by sEMG. Even though multichannel sEMG provides information able to improve the estimation of force and/or fatigue during working tasks, many problems related to the signal acquisition and interpretation are still open. Further improvements are necessary to develop multichannel sEMG into an effective tool supporting other methodologies for the evaluation of work‐related risks. © 2010 Wiley Periodicals, Inc.

Longlasting truck driving leads often to excessive muscle load and muscle fatigue. Body postures maintained during long time by drivers may cause not only a disturbances of physiological functions but also fatigue of musculoskeletal system and may lead to rise the musculoskeletal disorders. Excessive muscle fatigue as well as the musculoskeletal disorders may have an influence on the risk of the car accidents. One of the commonly used methods to assess the load and fatigue from the muscles is a surface electromyography (EMG). A great number of studies indicate the relationship between the EMG signal amplitude and the force developed by muscles. Moreover, many studies confirm the effect of the muscle load on the values of the parameters characterising the EMG signal power spectrum and indicating muscle fatigue.The aim of the study was to evaluate the influence of the body posture maintained during truck driving on muscle load and fatigue as well as reaction time among truck drivers.The studies with usage of surface electromyography among 10 men in age between 20 and 23 years were carried out. The EMG signal from four muscles of the right lower limb (medial gastrocnemius, lateral gastrocnemius, rectus femoris and vastus lateralis) was registered. For every participant six tests were carried out. The tests differed in the angles in the hip joint and knee joint. Every test took five minutes. The participants were steering by the right lower limb on the research stand imitating real truck driver work stand. During tests the reaction time on unexpected events was also registered.The results of the analysis indicate, that body posture maintained during truck driving influences the EMG signal parameters, commonly used as a muscle load and muscle fatigue indicators. The results of the study can serve as guidelines to truck drivers regarding body posture during truck driving.KeywordsEMGmuscle fatiguetruck drivingreaction time

Detection, quantification and analysis of muscle fatigue are crucial in occupational/rehabilitation and sporting settings. Sports organizations, such as the Australian Institute of Sports (AIS), currently monitor fatigue by a battery of tests including invasive techniques that require taking blood samples and/or muscle biopsies, the latter of which is highly invasive, painful, time consuming and expensive. SEMG (surface electromyography) is non-invasive monitoring of muscle activation and is an indication of localized muscle fatigue based on the observed shift of the power spectral density of the SEMG. The success of SEMG based techniques is currently limited to isometric contraction and is not acceptable to the human movement community. The paper proposes and tests a simple signal processing technique to identify the onset of muscle fatigue during cyclic activities of muscles, such as VL and VM, during cycling. Based on experiments conducted with 7 participants, using power output as a measure of fatigue, the technique is able to identify muscle fatigue with 98% significance.

Analysis of muscle fatigue in helicopter pilots

The objective of this study is to understand fatigue of leg muscles (tibialis anterior and gastrocnemius) using surface electromyography (sEMG) when the feet are supported on the floor and when they are unsupported. Twelve healthy male volunteers participated in the study. Chair height was changed to two positions, i.e. (A) suit subject height perfectly - with feet supported on the floor, and (B) height higher than A and feet were suspended in air. Each seated position was tested for 20 minutes. Surface EMG signals were recorded bilaterally from the two groups of leg muscles for 60 seconds at 5 minutes intervals from 0th to 20th minute. These signals were filtered using a 4th order Butterworth filter with a pass band of 20-250 Hz and a 4th order notch filter with a stop band of 47-49 Hz. RMS values and mean power frequency (MPF) of the signals were calculated. Linear regression analysis was performed on MPF values and the RMS Values. Mann-Whitney `U' test was performed on the slope of the regression lines and the RMS values to determine if there was any significant difference in fatigue. Results indicated a higher muscle fatigue for height B. This suggests that while adjusting height of chair, identifying optimal height should be taken into consideration. In places where adjustable chairs are not available, provision for foot rest would be advisable, to avoid muscle fatigue. (4 pages)

Analysis of upper-limb muscle fatigue in the process of rotary handling

ObjectiveInvestigate the effect of passive, active or no intra-operative work breaks on static, median and peak muscular activity, muscular fatigue, upper body postures, heart rate, and heart rate variability.BackgroundAlthough laparoscopic surgery is preferred over open surgery for the benefit of the patient, it puts the surgeons at higher risk for developing musculoskeletal disorders especially due to the less dynamic and awkward working posture. The organizational intervention intraoperative work break is a workplace strategy that has previously demonstrated positive effects in small-scale intervention studies.MethodsTwenty-one surgeons were exposed to three 90-min conditions: no breaks, 2.5-min passive (standing rest) or active (targeted stretching and mobilization exercises) breaks after 30-min work blocks. Muscular activity and fatigue of back, shoulder and forearm muscles were assessed by surface electromyography; upper body posture, i.e., spinal curvature, by inclination sensors; and heart rate and variability (HRV) by electrocardiography. Generalized estimating equations were used for statistical analyses. This study (NCT03715816) was conducted from March 2019 to October 2020.ResultsThe HRV-metric SDNN tended to be higher, but not statistically significantly, in the intervention conditions compared to the control condition. No statistically significant effects of both interventions were detected for muscular activity, joint angles or heart rate.ConclusionIntraoperative work breaks, whether passive or active, may counteract shoulder muscular fatigue and increase heart rate variability. This tendency may play a role in a reduced risk for developing work-related musculoskeletal disorders and acute physical stress responses.

Introduction: Muscle fatigue is often experienced by athletes and in work settings. Excessive fatigue can lead to injury and musculoskeletal disorders. Surface electromyography (EMG) is typically used to detect and ultimately prevent fatigue during isometric movement. The application of EMG to fatigue detection in dynamic movement requires, however, a secondary confirmation of fatigue based on physiological measures. Our objective was to determine if muscle oxygenation derived via near-infrared spectroscopy (NIRS) was correlated with relevant EMG indicators of neuromuscular fatigue and whether observed correlations were related to the fatigue process. Methods: Bilateral electromyograms from three upper leg muscles and the tissue oxygenation index (TOI) of the vastus lateralis muscle were recorded in sixteen non-disabled individuals during cycle ergometry to volitional exhaustion. Six EMG activity features were extracted and the Pearson correlation coefficient between each feature and TOI was determined. Results: The EMG root mean square, spectral standard deviation, second spectral moment, and zero-crossing rate (ZC) were strongly correlated with TOI. The time course of ZC and the correlation of this feature with TOI suggest that there could be a relation between muscle oxygenation and fatigue. Conclusion: Future work should use the knowledge gained in this study to investigate whether NIRS can be used to verify the onset of fatigue as detected by EMG.

Electromyography

Analysis of neuromuscular fatigue finds various applications ranging from clinical studies to biomechanics. Surface electromyography (sEMG) signals are widely used for these studies due to its non-invasiveness. During cyclic dynamic contractions, these signals are nonstationary and cyclostationary. In recent years, several nonstationary methods have been employed for the muscle fatigue analysis. However, cyclostationary based approach is not well established for the assessment of muscle fatigue. In this work, cyclostationarity associated with the biceps brachii muscle fatigue progression is analyzed using sEMG signals and Spectral Correlation Density (SCD) functions. Signals are recorded from fifty healthy adult volunteers during dynamic contractions under a prescribed protocol. These signals are preprocessed and are divided into three segments, namely, non-fatigue, first muscle discomfort and fatigue zones. Then SCD is estimated using fast Fourier transform accumulation method. Further, Cyclic Frequency Spectral Density (CFSD) is calculated from the SCD spectrum. Two features, namely, cyclic frequency spectral area (CFSA) and cyclic frequency spectral entropy (CFSE) are proposed to study the progression of muscle fatigue. Additionally, degree of cyclostationarity (DCS) is computed to quantify the amount of cyclostationarity present in the signals. Results show that there is a progressive increase in cyclostationary during the progression of muscle fatigue. CFSA shows an increasing trend in muscle fatiguing contraction. However, CFSE shows a decreasing trend. It is observed that when the muscle progresses from non-fatigue to fatigue condition, the mean DCS of fifty subjects increases from 0.016 to 0.99. All the extracted features found to be distinct and statistically significant in the three zones of muscle contraction (p < 0.05). It appears that these SCD features could be useful in the automated analysis of sEMG signals for different neuromuscular conditions.

Surface electromyography during sustained isometric contractions.

The manual lifting of heavy loads by personnel is susceptible to the development of muscle fatigue, which, in severe cases, can result in the irreversible impairment of muscle function. This study proposes a novel method of signal fusion to analyse muscle fatigue during manual lifting. Furthermore, this study represents the inaugural application of the back-propagation neural network and bidirectional encoder representation from the transformer (BP + BERT) algorithm to the fusion of two sensor inputs for the analysis of muscle fatigue. Lifting action fatigue tests were carried out on 16 testers in this study, with both surface electromyography (sEMG) and mechanomyography (MMG) signals collected as part of the process. The mean power frequency (MPF) eigenvalues were extracted separately for the two signals, and the results of muscle fatigue labelling according to the trend of the MPF eigenpeak were merged to produce three datasets. Subsequently, the three datasets were employed to categorise muscle fatigue classes using the support vector machine and radial basis function (SVM + RBF), support vector machine and bidirectional encoder representation from transformer (SVM + BERT), back-propagation neural network (BP), and back-propagation neural network and bidirectional encoder representation from transformer (BP + BERT) algorithms, respectively. The results of the muscle fatigue classification model demonstrated that the sEMG and MMG fused dataset, imported into the BP + BERT algorithm, exhibited the highest average accuracy of 98.10% for the muscle fatigue classification model. This study indicates that the fusion of sEMG and MMG signals is an effective approach, and the performance of the BP + BERT muscle fatigue classification model is also enhanced.

Surface electromyography (sEMG) is a common technique used in the assessment of local muscle fatigue. As opposed to static contraction situations, sEMG recordings during dynamic contractions are particularly characterised by non-stationary (and non-linear) features. Standard signal processing methods using Fourier and wavelet based procedures demonstrate well known restrictions on time-frequency resolution and the ability to process non-stationary and/or non-linear time-series, thus aggravating the spectral parameters estimation. The Hilbert-Huang transform (HHT), comprising of the empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA), provides a new approach to overcome these issues. The time-dependent median frequency estimate is used as muscle fatigue indicator, and linear regression parameters are derived as fatigue quantifiers. The HHT method is utilised for the analysis of the sEMG signals recorded over quadriceps muscles during cyclic dynamic contractions. The results are compared with those obtained by the Fourier and wavelet based methods. It is shown that HHT procedure provides the most consistent and reliable assessment of spectral and derived linear regression parameters, given the time epoch width and sampling interval in the time domain. The suggested procedure successfully deals with non-stationary and non-linear properties of biomedical signals.

In most assembly lines in manufacturing industry and service occupations, employees may experience pain and discomfort associated with long periods of standing. Prolonged standing tasks in manufacturing industry may lead to musculoskeletal disorders including pain, increased fatigue and stiffness in active muscles. The aims of this study are to determine subjective fatigue experienced by male Malaysian operators in metal stamping industry, and assess muscle fatigue in their lower extremities. Ten production operators of the industry participated in the study. Muscle fatigue has been analyzed using surface electromyography (sEMG) whereby six muscles such as left and right erector spinae muscles, left and right tibialis anterior muscles, and left and right gastrocnemius muscles were simultaneously measured. On the other hand, the subjective fatigue was assessed through questionnaire surveys. Results of questionnaire surveys found that all operators involved in the study reported that they experienced muscle fatigue due to prolonged standing tasks. Result obtained from sEMG measurement shows similar trend with the conclusion derived from surveys. Workers who experienced fatigue recorded a decreased average mean.