Real-time measurement of muscle fatigue related changes in surface EMG

A Real-Time Algorithm to Estimate Shoulder Muscle Fatigue Based on Surface EMG Signal For Static and Dynamic Upper Limb Tasks

Despite prevention efforts, the prevalence of workrelated upper extremity musculoskeletal disorders (WRUED) is increasing. A limit in the development of preventive interventions is the lack of devices that can measure and process sEMG signals in order to provide real-time reliable information on muscular fatigue of the upper limb in relation to the physical demands of the work. In this paper, the development and evaluation of a real-time muscle fatigue detection algorithm based on sEMG will be presented. The proposed algorithm uses the median frequency of sEMG power spectrum density (PSD) obtained with the Continuous Wavelet Transform (CWT) as an indicator of the muscle fatigue level. To extend the algorithm's efficiency to dynamic tasks, a muscle contraction detection module is added in order to remove the segments when the muscle is not contracting. To assess the algorithm's performance, eight healthy adults performed simple static and dynamic shoulder tasks using different loads. The results of the proposed time-frequency method (i.e. CWT) were first compared to those of the traditional Short Time Fourier Transform (STFT). It was shown that the CWT performs better than the STFT in both static and dynamic loading conditions. The validity of the algorithm's output as a muscle fatigue indicator was verified by comparing the output's decrease rate with different loads. As expected, the algorithm's fatigue indicator decreased faster over time with heavier loads. It was also shown that the initial muscle fatigue estimation output is independent of the load. Finally, we studied the proposed muscle contraction detection module's efficiency to overcome issues associated with dynamic tasks. We observed a substantial improvement of the smoothness of the fatigue indicator's evolution by using of the muscle contraction detection module.

Non-obstructive monitoring of muscle fatigue for low intensity dynamic exercise with infrared thermography technique

Surface electromyography (sEMG) has been widely used in evaluating muscle fatigue among athletes where electrodes are attached on the skin during the activity. Recently, infrared thermography technique (IRT) has gain popularity and shown to be another preferred method in monitoring and predicting muscle fatigue non-obstructively. This paper investigates the correlation between surface temperature and muscle activation parameters obtained using both IRT and sEMG methods simultaneously. Twenty healthy subjects were required to perform a repetitive calf raise exercise with various loads attached around their ankle for 3 min to induce fatigue on the targeted gastrocnemius muscles. Average temperature and temperature difference information were extracted from thermal images, while root mean square (RMS) and median frequency (MF) were extracted from sEMG signals. Spearman statistical analysis performed shows that there is a significant correlation between average temperature with RMS and between temperature difference with MF values at p<0.05. While ANOVA test conducted shows that there is significant impact of loads on RMS and MF where F=12.61 and 3.59, respectively, at p< 0.05. This study suggested that skin surface temperature can be utilized in monitoring and predicting muscle fatigue in low intensity dynamic exercise and can be extended to other dynamic exercises.

IMAGE-BASED SKELETAL MUSCLE COORDINATION: CASE STUDY ON A SUBJECT SPECIFIC FACIAL MIMIC SIMULATION

Facial muscle coordination is a fundamental mechanism for facial mimics and expressions. The understanding of this complex mechanism leads to better diagnosis and treatment of facial disorders like facial palsy or disfigurement. The objective of this work was to use magnetic resonance imaging (MRI) technique to characterize the activation behavior of facial muscles and then simulate their coordination mechanism using a subject specific finite element model. MRI data of lower head of a healthy subject were acquired in neutral and in the pronunciation of the sound [o] positions. Then, a finite element model was derived directly from acquired MRI images in neutral position. Transversely-isotropic, hyperelastic, quasi-incompressible behavior law was implemented for modeling facial muscles. The simulation to produce the pronunciation of the sound [o] was performed by the cumulative coordination between three pairs of facial mimic muscles (Zygomaticus Major (ZM), Levator Labii Superioris (LLS), Levator Anguli Oris (LAO)). Mean displacement amplitude showed a good agreement with a relative deviation of 15% between numerical outcome and MRI-based measurement when all three muscles are involved. This study elucidates, for the first time, the facial muscle coordination using in vivo data leading to improve the model understanding and simulation outcomes.

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.

Surface EMG based muscle fatigue evaluation in biomechanics

In the last three decades it has become quite common to evaluate local muscle fatigue by means of surface electromyographic (sEMG) signal processing. A large number of studies have been performed yielding signal-based quantitative criteria of fatigue in primarily static but also in dynamic tasks. The non-invasive nature of this approach has been particularly appealing in areas like ergonomics and occupational biomechanics, to name just the most prominent ones. However, a correct appreciation of the findings concerned can only be obtained by judging both the scientific value and practical utility of methods while appreciating the corresponding advantages and limitations. The aim of this paper is to serve as a state of the art summary of this issue. The paper gives an overview of classical and modern signal processing methods and techniques from the standpoint of applicability to sEMG signals in fatigue-inducing situations relevant to the broad field of biomechanics. Time domain, frequency domain, time-frequency and time-scale representations, and other methods such as fractal analysis and recurrence quantification analysis are described succinctly and are illustrated with their biomechanical applications, research or clinical alike. Examples from the authors' own work are incorporated where appropriate. The future of this methodology is projected by estimating those methods that have the greatest chance to be routinely used as reliable muscle fatigue measures.

Fatigue analysis before and after shaker exercise: physiologic tool for exercise design

Recent studies suggest that the Shaker exercise induces fatigue in the upper esophageal sphincter (UES) opening muscles and sternocleidomastoid (SCM), with the SCMs fatiguing earliest. The aim of this study was to measure fatigue induced by the isometric portion of the Shaker exercise by measuring the rate of change in the median frequency (MF rate) of the power spectral density (PSD) function, which is interpreted as proportional to the rate of fatigue, from surface electromyography (EMG) of suprahyoid (SHM), infrahyoid (IHM), and SCM. EMG data compared fatigue-related changes from 20-, 40-, and 60-s isometric hold durations of the Shaker exercise. We found that fatigue-related changes were manifested during the 20-s hold. The findings confirm that the SCM fatigues initially and as fast as or faster than the SHM and IHM. In addition, upon completion of the exercise protocol, the SCM had a decreased MF rate, implying improved fatigue resistance, while the SHM and IHM showed increased MF rates, implying that these muscles increased their fatiguing effort. We conclude that the Shaker exercise initially leads to increased fatigue resistance of the SCM, after which the exercise loads the less fatigue-resistant SHM and IHM, potentiating the therapeutic effect of the Shaker exercise regimen with continued exercise performance.

Real time monitoring of muscular fatigue from dynamic surface myoelectric signals using a complex covariance approach

A method aimed at the real-time monitoring of muscular fatigue was implemented and optimized. The method is based on an estimate of the complex covariance function in order to evaluate, in real time, the mean frequency of the myoelectric signal spectrum. Real-time implementation is guaranteed by a recursive computation of the complex covariance and then of the mean frequency. The results show good performance on both synthetic and experimental non-stationary myoelectric signals recorded during fatiguing dynamic protocols. Performance in the presence of noise is highly satisfactory on both deterministic signals and stochastic processes, even when there are strong non-stationarities. Moreover, the computational complexity is highly reduced with respect to that offered by traditional methods based on short time Fourier transform.

Fatigue compensation of the electromyographic signal for prosthetic control and force estimation

During a sustained muscle contraction, the amplitude of electromyographic (EMG) signals increases and the spectrum of the EMG signal shifts toward lower frequencies. These effects are due to muscular fatigue and can cause problems in the control of myoelectric prostheses and in the estimation of contraction level from the EMG signal. It has been well known that the fatigue effects can be explained by the conduction velocity changes during the fatigue process and by the idea that the conduction velocity is linearly proportional to the median frequency of EMG signals. Hence the fatigue process can be monitored by measuring the median frequency. A fatigue compensation preprocessor has been developed. It uses the widely accepted power spectrum density model of EMG signals that contains the conduction velocity as a measure of fatigue. It was verified that the preprocessor scales down the amplitude of the fatigued EMG signal and decompresses the spectrum. Hence, the preprocessor eliminates the increase in amplitude and the shift in frequency and enables consistent EMG signals to be used to control prostheses.