Non-invasive Analysis of Fiber Type Composition in Lower Limb Skeletal Muscles Using Reduced Interference Rihaczek Distribution

Fiber composition is an important factor influencing force generation and endurance of different skeletal muscles. The analysis of the heterogeneous composition of muscles has gained importance in the field of sports biomechanics and biomedicine. In this work, an attempt is made to analyze the fiber composition of Rectus femoris (type II dominant) and Soleus (type I dominant) muscles using surface electromyography. Isometric signals are acquired from the muscles of 15 participants using a well-defined protocol and are further processed using reduced interference Rihaczek distribution. Instantaneous median frequency (IMDF) is extracted from the non-fatigue (NF) and fatigue (F) segments of the signals and is analyzed. From the distributions, it is found that the spectral power increases in the lower frequencies of the signal recorded from the rectus femoris and in the higher frequencies of signals recorded from the soleus during fatigue. The soleus is showing higher IMDF values than the rectus femoris in both segments. A reduction of 14% and an increase of 10% is observed in the IMDF during fatigue for rectus femoris and soleus, respectively. Thus, the extracted feature is found to be sensitive and statistically significant (p<0.05) to differentiate fiber types as well as the NF and F states of the two muscles.Clinical Relevance- This study may be extended to non-invasively analyze the fiber type shifts in muscles due to athletic training and pathological conditions.

ANALYSIS OF INDUCED ISOMETRIC FATIGUING CONTRACTIONS IN BICEPS BRACHII MUSCLES USING MYOTONOMETRY AND SURFACE ELECTROMYOGRAPHIC MEASUREMENTS

Viscoelastic properties of skeletal muscle tissue are known to be impacted by fatiguing contractions. In this study, an attempt has been made to utilize myotonometry for analyzing the relationship between muscle viscoelasticity and contractile behaviors in a fatiguing task. For this purpose, thirteen young healthy volunteers are recruited to perform the fatiguing isometric task and the time to task failure (TTF) is recorded. Myotonometric parameters and simultaneous surface electromyographic (sEMG) signals are recorded from the Biceps Brachii muscle of the flexed arm. The correlation between myotonometric parameters and TTF is further analyzed. Cross-validation with sEMG features is also performed. Stiffness of muscle has a positive correlation with TTF in the left hand ([Formula: see text]). Damping property of the nonfatigued muscle is positively associated with the fatigue-induced changes in amplitude features of sEMG signal in the right hand ([Formula: see text]). The normalized rate of change of mean frequency of sEMG signal has a positive correlation with stiffness values in both of the hands ([Formula: see text]). Muscle viscoelasticity is demonstrated to influence the progression of fatigue, although the difference in motor control due to handedness is also found to be an important factor. The results are promising to improve the understanding of the effect of muscle mechanics in fatigue-induced task failure.

Human motor function estimation based on EMG signal fractal dimension standard deviation

Wearable robots must adjust the assist mode/intensity according to human motion during the motion assistance process. By decoding the surface electromyography (sEMG) signal, the standard deviation of the fractal dimension is used as a characteristic index of muscle contraction-relaxation ability, and explore the feasibility of using the standard deviation of the fractal dimension to estimate the human motor function and thus provide a basis for decision-making for the flexible control of wearable robots. First, the sEMG signals of several subjects with different motor functions were collected and their time-domain and frequency-domain features were extracted. The experimental results for one hour of walking showed that the time-domain and frequency-domain feature values increased with muscle fatigue. The trend has little to do with the inherent motor function of the human body; Second, due to the strong nonlinearity, time-varying, and strong complexity of the sEMG signal, the fractal dimension nonlinear method is used to characterize the complexity of the EMG signal that is closely related to muscle function. Besides, theoretical and experimental studies have been conducted to clarify the feasibility of the complexity of fractal dimension representation and to provide theoretical support for the further use of the standard deviation of fractal dimension to estimate human motor function. The experimental results of continuous walking for one hour show that, macroscopically, the fractal dimension of each muscle of the individual subject does not change significantly with walking time, which shows that the fractal dimension has nothing to do with exercise time and muscle fatigue; On the microscopic level, the value of the fractal dimension changes when the subject’s muscles contract and relax. Subjects with strong motor function have smaller fractal dimensions when their muscles contract than subjects with weaker motor function, and the opposite happens when their muscles relax, and it can be seen that there is a positive correlation between the difference in the fractal dimension during muscle contraction and relaxation and the muscle contraction-relaxation ability and the human body’s inherent motor function. The test results verify the feasibility of using the standard deviation of fractal dimension to estimate the intrinsic motor function of the human body.

ESTIMATING FATIGUE LEVEL OF FEMORAL AND GASTROCEMIUS MUSCLES BASED ON SURFACE ELECTROMYOGRAPHY IN TIME AND FREQUENCY DOMAIN

This paper presents a new method for estimating muscle fatigue level based on surface electromyography (EMG) of femoral and gastrocnemius muscles during repetitive motions with various load. The relationship between fatigue level and EMG signals was examined through repetitive movements of the femoral and gastrocnemius muscles with the use of leg extension and squat machines. The fatigue level was based on the maximum voluntary contraction (MVC) levels with various loads. The integrated EMG (IEMG) value and the mean frequency value for each load cycle were obtained through the surface EMG signal. This work presents a global EMG index map by using the new analytical technique based on the relationship between the average IEMG and mean power frequency (MPF) values. The proposed method enables simultaneous estimation of muscle fatigue level and force using real-time EMG signals from the femoral and gastrocnemius muscles.

Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm

Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions.

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG) signal is used to monitor the workers' muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird's eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications.

Analysis of Muscle Fatigue Progression using Cyclostationary Property of Surface Electromyography Signals

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.