Muscle Fatigue Estimation Research Articles

Mid-Activity and At-Home Wearable Bioimpedance Elucidates an Interpretable Digital Biomarker of Muscle Fatigue.

Muscle health and decreased muscle performance (fatigue) quantification has proven to be an invaluable tool for both athletic performance assessment and injury prevention. However, existing methods estimating muscle fatigue are infeasible for everyday use. Wearable technologies are feasible for everyday use and can enable discovery of digital biomarkers of muscle fatigue. Unfortunately, the current state-of-the-art wearable systems for muscle fatigue tracking suffer from either low specificity or poor usability. We propose using dual-frequency bioimpedance analysis (DFBIA) to non-invasively assess intramuscular fluid dynamics and thereby muscle fatigue. A wearable DFBIA system was developed to measure leg muscle fatigue of 11 individuals during a 13-day protocol consisting of exercise and unsupervised at-home portions. We derived a digital biomarker of muscle fatigue, fatigue score, from the DFBIA signals that was able to estimate the percent reduction in muscle force during exercise with repeated-measures Pearson's r = 0.90 and mean absolute error (MAE) of 3.6%. This fatigue score also estimated delayed onset muscle soreness with repeated-measures Pearson's r = 0.83 and MAE = 0.83. Using at-home data, DFBIA was strongly associated with absolute muscle force of participants (n = 198, p < 0.001). These results demonstrate the utility of wearable DFBIA for non-invasively estimating muscle force and pain through the changes in intramuscular fluid dynamics. The presented approach may inform development of future wearable systems for quantifying muscle health and provide a novel framework for athletic performance optimization and injury prevention.

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors.

Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts.

A New Wrist-Forearm Rehabilitation Protocol Integrating Human Biomechanics and SVM-Based Machine Learning for Muscle Fatigue Estimation.

In this research, a new remote rehabilitation system was developed that integrates an IoT-based connected robot intended for wrist and forearm rehabilitation. In fact, the mathematical model of the wrist and forearm joints was developed and integrated into the main controller. The proposed new rehabilitation protocol consists of three main sessions: the first is dedicated to the extraction of the passive components of the dynamic model of wrist-forearm biomechanics while the active components are extracted in the second session. The third session consists of performing continuous exercises using the determined dynamic model of the forearm-wrist joints, taking into account the torque generated by muscle fatigue. The main objective of this protocol is to determine the state level of the affected wrist and above all to provide a dynamic model in which the torque generated by the robot and the torque supplied by the patient are combined, taking into account the constraints of fatigue. A Support Vector Machine (SVM) classifier is designed for the estimation of muscle fatigue based on the features extracted from the electromyography (EMG) signal acquired from the patient. The results show that the developed rehabilitation system allows a good progression of the joint's range of motion as well as the resistive-active torques.

Permutation Lempel-Ziv 복잡성 척도를 적용한 근 피로도 추정

A new approach for quantifying muscle fatigue is presented by analyzing the deterministic complexity in the surface electromyogram(SEMG) using Permutation Lempel-Ziv(PLZ) complexity measure. The PLZ complexity measure provide a metric for the number of distinct deterministic patterns in signals by combining permutation patterns and Lempel-Ziv complexity analysis. For muscle fatigue experiment by using PLZ complexity measure, surface EMG signals from eleven subjects were recorded in biceps brachii muscle with constant isometric 20, 50, and 80%MVC contractions. The PLZ complexity measure is evaluated with the performance of muscle fatigue estimation for the SEMG signals compared to the performance of previous reported LZ complexity measure. This PLZ complexity measure has advantages that is computationally easy to compute and does not require any assumptions about the stationarity of the SEMG signal.

Attribution of sensory prediction error to perception of muscle fatigue

Sensory prediction-error is vital to discriminating whether sensory inputs are caused externally or are the consequence of self-action, thereby contributing to a stable perception of the external world and building sense of agency. However, it remains unexplored whether prediction error of self-action is also used to estimate the internal body condition. To address this point, we examined whether prediction error affects the perceived intensity of muscle fatigue. Participants evaluated fatigue while maintaining repetitive finger movements. To provide prediction error, we inserted a temporal delay into online visual feedback of self-movements. The results show that the subjective rating of muscle fatigue significantly increased under the delayed visual feedback, suggesting that prediction error enhances the perception of muscle fatigue. Furthermore, we introduced visual feedback that preceded actual finger movements to test whether the temporal direction of the mismatch is crucial in estimating muscle fatigue. We found that perceived fatigue was significantly weaker with preceding visual feedback compared to normal feedback, showing that the perception of muscle fatigue is affected by the signed prediction-error. Our findings support the idea that the brain flexibly attributes prediction errors to a self-origin with keeping sense of agency, or external origin by considering contexts and error characteristics.

Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation.

In this study, we present an IoT-based robot for wrist rehabilitation with a new protocol for determining the state of injured muscles as well as providing dynamic model parameters. In this model, the torque produced by the robot and the torque provided by the patient are determined and updated taking into consideration the constraints of fatigue. Indeed, in the proposed control architecture based on the EMG signal extraction, a fuzzy classifier was designed and implemented to estimate muscle fatigue. Based on this estimation, the patient’s torque is updated during the rehabilitation session. The first step of this protocol consists of calculating the subject-related parameters. This concerns axis offset, inertial parameters, passive stiffness, and passive damping. The second step is to determine the remaining component of the wrist model, including the interaction torque. The subject must perform the desired movements providing the torque necessary to move the robot in the desired direction. In this case, the robot applies a resistive torque to calculate the torque produced by the patient. After that, the protocol considers the patient and the robot as active and all exercises are performed accordingly. The developed robotics-based solution, including the proposed protocol, was tested on three subjects and showed promising results.

Retentive capacity of power output and linear versus non-linear mapping of power loss in the isotonic muscular endurance test

The limit of dynamic endurance during repetitive contractions has been referred to as the point of muscle fatigue, which can be measured by mechanical and electrophysiological parameters combined with subjective estimates of load tolerance for revealing the human real-world capacity required to work continuously. In this study, an isotonic muscular endurance (IME) testing protocol under a psychophysiological fatigue criterion was developed for measuring the retentive capacity of the power output of lower limb muscles. Additionally, to guide the development of electrophysiological evaluation methods, linear and non-linear techniques for creating surface electromyography (sEMG) models were compared in terms of their ability to estimate muscle fatigue. Forty healthy college-aged males performed three trials of an isometric peak torque test and one trial of an IME test for the plantar flexors and knee and hip extensors. Meanwhile, sEMG activity was recorded from the medial gastrocnemius, lateral gastrocnemius, vastus medialis, rectus femoris, vastus lateralis, gluteus maximus, and biceps femoris of the right leg muscles. Linear techniques (amplitude-based parameters, spectral parameters, and instantaneous frequency parameters) and non-linear techniques (a multi-layer perception neural network) were used to predict the time-dependent power output during dynamic contractions. Two mechanical manifestations of muscle fatigue were observed in the IME tests, including power output reduction between the beginning and end of the test and time-dependent progressive power loss. Compared with linear mapping (linear regression) alone or a combination of sEMG variables, non-linear mapping of power loss during dynamic contractions showed significantly higher signal-to-noise ratios and correlation coefficients between the actual and estimated power output. Muscular endurance required in real-world activities can be measured by considering the amount of work produced or the activity duration via the recommended IME testing protocol under a psychophysiological termination criterion. Non-linear mapping techniques provide more powerful mapping of power loss compared with linear mapping in the IME testing protocol.

Adaptive Cooperative Control for Hybrid FES-Robotic Upper Limb Devices: a Simulation Study.

Robotic systems and Functional Electrical Stimulation (FES) are common technologies exploited in motor rehabilitation. However, they present some limits. To overcome the weaknesses of both approaches, hybrid cooperative devices have been developed, which combine the action of the robot and that of the electrically stimulated muscles on the same joint. In this work, we present a novel adaptive cooperative controller for the rehabilitation of the upper limb. The controller comprises an allocator - which breaks down the reference torque between the motor and the FES a-priori contributions based on muscle fatigue estimation - an FES closed-loop controller, and an impedance control loop on the motor to correct trajectory tracking errors. The controller was tested in simulation environment reproducing elbow flexion/extension movements. Results showed that the controller could reduce motor torque requirements with respect to the motor-only case, at the expense of trajectory tracking performance. Moreover, it could improve fatigue management with respect to the FES-only case. In conclusion, the proposed control strategy provides a good trade-off between motor torque consumption and trajectory tracking performance, while the allocator manages fatigue-related phenomena.Clinical relevance-The use of allocation proves to be effective in both reducing motor torque and FES-induced muscle fatigue and might be an effective solution for hybrid FES-robotic systems.

Development of a Computer Vision-Based Muscle Stimulation Method for Measuring Muscle Fatigue during Prolonged Low-Load Exposure.

Measuring muscle fatigue is one essential and standard method to quantify the ergonomic risks associated with prolonged low-load exposure. However, measuring muscle fatigue using EMG-based methods has shown conflicting results under low-load but sustained work conditions, e.g., prolonged sitting. Muscle stimulation technology provides an alternative way to estimate muscle fatigue development during such work conditions by monitoring the stimulation-evoked muscle responses, which, however, could be restricted by the accessibility and measurability of targeted muscles. This study proposes a computer vision-based method to overcome such potential restrictions by visually quantifying the muscle belly displacement caused by muscle stimulation. The results demonstrate the ability of the developed computer vision-based stimulation method to detect muscle fatigue from prolonged low-load tasks. Current results can be used as a foundation to develop a sensitive and reliable method to quantify the adverse effects of the daily low-load sustained condition in occupational and nonoccupational settings.

A Real-Time Stability Control Method Through sEMG Interface for Lower Extremity Rehabilitation Exoskeletons.

Herein, we propose a real-time stable control gait switching method for the exoskeleton rehabilitation robot. Exoskeleton rehabilitation robots have been extensively developed during the past decade and are able to offer valuable motor ability to paraplegics. However, achieving stable states of the human-exoskeleton system while conserving wearer strength remains challenging. The constant switching of gaits during walking may affect the center of gravity, resulting in imbalance of human–exoskeleton system. In this study, it was determined that forming an equilateral triangle with two crutch-supporting points and a supporting leg has a positive impact on walking stability and ergonomic interaction. First, the gaits planning and stability analysis based on human kinematics model and zero moment point method for the lower limb exoskeleton are demonstrated. Second, a neural interface based on surface electromyography (sEMG), which realizes the intention recognition and muscle fatigue estimation, is constructed. Third, the stability of human–exoskeleton system and ergonomic effects are tested through different gaits with planned and unplanned gait switching strategy on the SIAT lower limb rehabilitation exoskeleton. The intention recognition based on long short-term memory (LSTM) model can achieve an accuracy of nearly 99%. The experimental results verified the feasibility and efficiency of the proposed gait switching method for enhancing stability and ergonomic effects of lower limb rehabilitation exoskeleton.

Physiology-based dynamic muscle fatigue model for upper limbs during construction tasks

Due to physically demanding construction tasks, workers frequently suffer from significant levels of muscle fatigue that can cause diverse detrimental effects on safety, health, and productivity. In this regard, evaluating the level of muscle fatigue prior to work is essential to take proper preventive actions before severe fatigue takes place. Although previous research efforts have quantified muscle fatigue using surveys, instruments, and mathematical models, most of them do not take into account irregularly varying muscle activation and fatigue recovery during a task. They are thus limited, especially for construction tasks that have varying forces and intermittent idling/resting periods. This study thus proposes a physiology-based modeling approach to computationally model and empirically validate dynamic muscle fatigue generation and recovery for construction workers through laboratory testing. Specifically, a muscle fatigue estimation model for upper limbs based on System Dynamics, which is a differential equation-based continuous simulation, is developed based on fundamental physiological mechanisms of the accumulation and clearance of intramuscular metabolites during muscle exertion and their effects on muscle contractile processes. Then the model is refined and validated through laboratory experiments. The results demonstrated the immense potential for the developed elbow and shoulder models to evaluate workers' muscle fatigue in upper limbs under varying workloads. The contribution of this study is to provide an analytic tool for understanding the physiological mechanisms of muscle fatigue and estimating workers' muscle fatigue levels during construction tasks, which can help to design appropriate interventions prior to work, thereby reducing undesirable results from muscle fatigue.

Non-Invasive Quantitative Muscle Fatigue Estimation Based on Correlation Between sEMG Signal and Muscle Mass

Muscle fatigue is required to be assessed in real-time to maintain the best physical condition, especially for sports and rehabilitation areas. In recent years, numerous studies proposed muscle fatigue estimation methods with non-invasive surface electromyography (sEMG). However, the previous approaches were limited to discerning whether muscle fatigue occurs and were unable to quantify the fatigue level due to individual differences in muscle characteristics. In this study, we propose a novel method for quantitative muscle fatigue estimation that is applicable for various people without individual calibration. Because muscle mass is closely related to muscular endurance, it is utilized as a standard parameter in our assessment process. We introduce a new concept of muscle fatigue score (MFS), based on the cosine similarity between muscle mass and representative fatigue indicators. The MFS exhibits a high correlation coefficient ( $\vert \text {R}\vert = 0.7398$ ) with key muscle characteristics compared to previous representative muscle fatigue indicators calculated from sEMG: mean frequency ( $\vert \text {R}\vert = 0.2848$ ), median frequency ( $\vert \text {R}\vert = 0.1972$ ), and low-frequency ratio ( $\vert \text {R}\vert = 0.0346$ ).

Cycling fatigue evaluation using weighted-cumulated methodology

In this paper, the weighted-cumulated methodology designed for the objective muscle fatigue estimation using surface electromyography is improved and extended for cycling applications. An automatic signal burst segmentation is proposed for dynamic electromyography, which is robust to signal amplitude fluctuations and abrupt variation of the experimental protocol parameters. The developed algorithm is integrated into a single instrumental tool. To evaluate the proposed methodology, nine participants performed cycling exercise until muscular failure in three different experimental protocols. In the first protocol, angular velocity was kept constant and exercise power was increased. In the second protocol, power was kept constant and the angular velocity was increased. The third protocol is maintained constant in both power and angular velocity at relatively high values. During the tests, surface electromyography signal was acquired from vastus lateralis. Behavioral characteristics and performance comparison involving the proposed experimental protocols are shown. The interpretations associated with objective metric to estimate muscle fatigue are discussed. The experimental results, obtained using improved weighted-cumulated methodology, are shown to be sensitive enough to distinguish spectral signature changes in the cycling electromyographic signals associated with muscle fatigue. The weighted-cumulated methodology has been shown to be fully applicable in dynamic physical protocols such as cycling. However, the investigations point out that the specification of the experimental protocol is also very important for the results reliability.

Grip and Electrophysiological Sensor-Based Estimation of Muscle Fatigue While Holding Steering Wheel in Different Positions

Hand position while driving varies considerably among drivers and it depends on their training and habit of driving. In this paper, we have estimated muscle fatigue of drivers using sensors to measure interfacial grip pressure and surface electromyography (sEMG) for three different steering wheel holding positions, i.e., 10–2, 9–3, and 8–4, during simulated driving. Thirty volunteers participated in this study by performing 60 min of simulated driving. sEMG study did not show a significant ( ${p} ) rate of increase in muscle fatigue while driving in 8–4 position when compared with 10–2 and 9–3 positions. Interfacial grip pressure showed that the mechanical loading acting on the palm of the hands was distributed while holding steering wheel in 8–4 position. In comparison, 10–2 and 9–3 positions showed a point palmar loading. In 8–4 position, the rate of decrease of interfacial grip pressure features such as contact pressure was observed to be significantly ( ${p} ) lower than 10–2 and 9–3 positions. This implies that the rate of fatigue will be lower while holding steering wheel in the 8–4 position. Based on the result from this paper, the 8–4 position can be concluded as the best position to hold the steering wheel to reduce the risk of muscle fatigue and other injuries to drivers during prolonged driving.

Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges-A Systematic Review.

Background: In order to reduce the risk of work-related musculoskeletal disorders (WMSDs) several methods have been developed, accepted by the international literature and used in the workplace. The purpose of this systematic review was to describe recent implementations of wearable sensors for quantitative instrumental-based biomechanical risk assessments in prevention of WMSDs. Methods: Articles written until 7 May 2018 were selected from PubMed, Scopus, Google Scholar and Web of Science using specific keywords. Results: Instrumental approaches based on inertial measurement units and sEMG sensors have been used for direct evaluations to classify lifting tasks into low and high risk categories. Wearable sensors have also been used for direct instrumental evaluations in handling of low loads at high frequency activities by using the local myoelectric manifestation of muscle fatigue estimation. In the field of the rating of standard methods, on-body wireless sensors network-based approaches for real-time ergonomic assessment in industrial manufacturing have been proposed. Conclusions: Few studies foresee the use of wearable technologies for biomechanical risk assessment although the requirement to obtain increasingly quantitative evaluations, the recent miniaturization process and the need to follow a constantly evolving manual handling scenario is prompting their use.

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.

Electromyography-signal-based muscle fatigue assessment for knee rehabilitation monitoring systems.

This study suggested a new EMG-signal-based evaluation method for knee rehabilitation that provides not only fragmentary information like muscle power but also in-depth information like muscle fatigue in the field of rehabilitation which it has not been applied to. In our experiment, nine healthy subjects performed straight leg raise exercises which are widely performed for knee rehabilitation. During the exercises, we recorded the joint angle of the leg and EMG signals from four prime movers of the leg: rectus femoris (RFM), vastus lateralis, vastus medialis, and biceps femoris (BFLH). We extracted two parameters to estimate muscle fatigue from the EMG signals, the zero-crossing rate (ZCR) and amplitude of muscle tension (AMT) that can quantitatively assess muscle fatigue from EMG signals. We found a decrease in the ZCR for the RFM and the BFLH in the muscle fatigue condition for most of the subjects. Also, we found increases in the AMT for the RFM and the BFLH. Based on the results, we quantitatively confirmed that in the state of muscle fatigue, the ZCR shows a decreasing trend whereas the AMT shows an increasing trend. Our results show that both the ZCR and AMT are useful parameters for characterizing the EMG signals in the muscle fatigue condition. In addition, our proposed methods are expected to be useful for developing a navigation system for knee rehabilitation exercises by evaluating the two parameters in two-dimensional parameter space.

Effect of fatigue on the stationarity of surface electromyography signals

The estimation of muscle fatigue using surface electromyography (SEMG) is of high relevance to evaluate ergonomic risk factors in the occupational settings. Signal stationarity plays an important role while selecting appropriate SEMG signal processing method for fatigue evaluation. The Fourier algorithm based signal processing methods (mean or median frequency of power spectrum) rely on the assumption that the signal under investigation is stationary. Stationarity of SEMG signals and its association with fatigue is rarely studied in the ergonomics literature. Therefore, this study was aimed at understanding the effect of fatigue on the stationarity of the SEMG data. Ten participants performed 40 min of fatiguing upper extremity exertions and SEMG data were recorded from the right upper trapezius muscle. The SEMG data recorded under static and dynamic conditions at the beginning and at the end of fatiguing exertions were used in the analysis. The stationarity analysis was performed for five window sizes of 128, 256, 512, 768 and 1024 ms using modified reverse arrangement test. The results showed that the muscle fatigue reduced the stationarity of the SEMG signal under static and dynamic conditions. The relationship between the muscle fatigue and the stationarity of the SEMG signal was found to be significant at the window size of 512 ms. A significantly higher fatigue related decrease in the stationarity was observed during dynamic exertions compared to the static exertions. Relevance to industryThe findings from the current study illustrate that the stationarity of SEMG signals could be used to quantify muscle fatigue under static and dynamic task conditions. These findings are useful to the ergonomic practitioners in conducting muscle fatigue estimation using SEMG.

Estimation of muscle fatigue by ratio of mean frequency to average rectified value from surface electromyography.

A new method to estimate muscle fatigue quantitatively from surface electromyography (EMG) is proposed. The ratio of mean frequency (MNF) to average rectified value (ARV) is used as the index of muscle fatigue, and muscle fatigue is detected when MNF/ARV falls below a pre-determined or pre-calculated baseline. MNF/ARV gives larger distinction between fatigued muscle and non-fatigued muscle. Experiment results show the effectiveness of our method in estimating muscle fatigue more correctly compared to conventional methods. An early evaluation based on the initial value of MNF/ARV and the subjective time when the subjects start feeling the fatigue also indicates the possibility of calculating baseline from the initial value of MNF/ARV.

Disassembly task evaluation by muscle fatigue estimation in a virtual reality environment

Today, disassembly operations play a very important role during the initial design phase of industrial products considering the role played by these operations throughout the product life cycle. Current simulation platforms do not offer the necessary information and versatility required for a complete disassembly process simulation, including human/operator physiological data management. The paper deals with a new method for disassembly sequence evaluation. It is based on metabolic energy expenditure and muscle fatigue estimation. For this purpose, the analytical model for mechanical energy expenditure is proposed. In this model, the required mechanical work is used as a parameter that allows comparing the relationships among fatigue levels when performing disassembly sequences. Then, the fatigue levels are evaluated by analyzing the recorded electromyography signal on an operator’s arm. The proposed method is validated by a set of experimental disassembly tests performed in a virtual reality environment. The comparison of the analytical and experimental results has shown good correlation between them. The main result of this study is the proposed model for assessing muscle fatigue and its validation by experimental procedure. The proposed method provides the feasibility to integrate human muscle fatigue into disassembly sequence evaluation via mechanical energy expenditure when performing disassembly operation simulations.