Parameter Identification for a Four-Compartment Controller Muscle Fatigue Model.

Accumulation of muscle fatigue and subjective fatigue are significant causes of decline in individual performance. Those fatigues can also lead to work errors and the associated musculoskeletal disorders. Thus, a quantitative evaluation of fatigue accumulation during work is required to manage the risk of industrial accidents. Most of the current assessment methods of workload are based on observing and scoring the range of joint motion and work frequency at a point in time. In other words, these assessment methods do not fully consider the continuous accumulation of fatigue. However, even while repeating the same task, muscle fatigue-recovery states and work movements change over time. Therefore, risk management of industrial accidents is important to objectively evaluate the subjective sense of strain and muscle fatigue from work movement data. This study aims to biomechanically model muscle fatigue and subjective fatigue during high-frequency repetitive manual-handling tasks. In an experiment, participants were asked to repeatedly lift a bottle weighing approximately 1 kg, containing salt as ballast, from a chest-height shelf to an eye-level shelf every two seconds for ten minutes. Both start and end points were set at the point approximately 80% of the upper limb length from shoulders at those heights in the midsagittal plane. During the experiment, whole-body motion was measured using an inertial sensor-based motion capture system. In addition to the body motion, electromyograms and subjective evaluations based on the Borg-CR10 scale of the upper limb were recorded. The measured body motion data were applied to a human musculoskeletal model to simulate muscle activity at each sampling time in the experiment. The results were then applied to the Xia and Frey-Law muscle fatigue model to simulate each muscle's residual capacity and fatigue at each sampling time during the experiment. The ratio of the simulated muscle activity to the simulated residual capacity was defined as the substantial muscle activity rate (SMAR). Changes in the SMAR during the experiment were compared with the changes in subjective fatigue and EMG median frequency. Throughout the task, slight abduction and forward flexion were kept in the upper arm. Therefore, we focused our discussion on the deltoid muscle, which might be the most heavily loaded during the experiment. The frequency analysis result of electromyograms indicated that the frequency power spectrum in the medial deltoid shifted to a lower frequency band in the first few minutes and was generally constant in the rest. The residual capacity of the medial deltoid simulated by the muscle fatigue model declined nonlinearly in the first few minutes and was almost constant after that. These results indicate that the muscle fatigue model sufficiently represented the fatigue at the medial deltoid. The muscle activity rate simulated by the musculoskeletal model was almost the same throughout the experiment. On the other hand, the SMAR declined in the first few minutes and continued at a higher range than the muscle activity rate. This changing trend of the SMAR was similar to the time change of the subjective fatigue of the shoulder.

Compartment based models of muscle fatigue have been particularly successful in accurately modeling isometric (static) tasks or actions. However, dynamic actions, which make up most everyday movements, are governed by different central and peripheral processes, and must therefore be modeled in a manner accounting for the differences in the responsible mechanisms. In the literature, a three-component controller (3CC) muscle fatigue model (MFM) has been proposed and validated for static tasks. A recent study reported a four-compartment muscle fatigue model considering both short- and long-term fatigued states. However, neither has been validated for both static and dynamic tasks. In this work we proposed a new four-compartment controller model of muscle fatigue with enhanced recovery (4CCr) that allows the modeling of central and peripheral fatigue separately and estimates strength decline for static and dynamic tasks. Joint velocity was used as an indicator of the degree of contribution of either mechanism. Model parameters were estimated from part of the experimental data and finally, the model was validated through the rest of experimental data that were not used for parameter estimation. The 3CC model cannot capture the fatigue phenomenon that the velocity of contraction would affect isometric strength measurements as shown in experimental data. The new 4CCr model maintains the predictions of the extensively validated 3CC model for static tasks but provides divergent predictions for isokinetic activities (increasing fatigue with increasing velocity) in line with experimentally observed trends. This new 4CCr model can be extended to various domains such as individual muscle fatigue, motor units' fatigue, and joint-based fatigue.

Optimization of a Stimulation Train based on a Predictive Model of Muscle Force and Fatigue

This paper predicts the optimal motion for a repetitive lifting task considering muscle fatigue. The Denavit-Hartenberg (DH) representation is employed to characterize the two-dimensional (2D) digital human model with 10 degrees-of-freedom (DOFs). Two joint-based muscle fatigue models, i.e., a three-compartment controller (3CC) muscle fatigue model (validated for isometric tasks) and a four-compartment controller with augmented recovery (4CCr) muscle fatigue model (validated for dynamic tasks), are utilized to account for the fatigue effect due to the repetitive motion. The lifting problem is formulated mathematically as an optimization problem, with the objective of minimizing dynamic effort and joint acceleration subjected to both physical and task-specific constraints. The design variables include joint angle profiles, discretized by quartic B-splines, and the control points of the profiles of the fatigue compartments associated with major body joints (spinal, shoulder, elbow, hip, and knee joints). The outcomes of the simulation encompass profiles of joint angles, joint torques, and the advancement of joint fatigue. It is notable that the profiles of joint angles and torques exhibit distinct periodic patterns. Numerical simulations and experiments with a 20 kg box reveal that the maximum predicted lifting cycles are 11 for the 3CC fatigue model and 13 for the 4CCr fatigue model while the experimental result is 13 cycles. The results indicate that the 4CCr muscle fatigue model provides enhanced accuracy over the 3CC model for predicting task duration (number of cycles) of repetitive lifting.

Muscle fatigue models (MFM) have broad potential application if they can accurately predict muscle capacity and/or endurance time during the execution of diverse tasks. As an initial step toward facilitating improved MFMs, we assessed the sensitivity of selected existing models to their inherent parameters, specifically that model the fatigue and recovery processes, and the accuracy of model predictions. These evaluations were completed for both prolonged and intermittent isometric contractions, and were based on model predictions of endurance times. Based on a recent review of the literature, four MFMs were initially chosen, from which a preliminary assessment led to two of these being considered for more comprehensive evaluation. Both models had a higher sensitivity to their fatigue parameter. Predictions of both models were also more sensitive to the alteration of their parameters in conditions involving lower to moderate levels of effort, though such conditions may be of most practical, contemporary interest or relevance. Although both models yielded accurate predictions of endurance times during prolonged contractions, their predictive ability was inferior for more complex (intermittent) conditions. When optimizing model parameters for different loading conditions, the recovery parameter showed considerably larger variability, which might be related to the inability of these MFMs in simulating the recovery process under different loading conditions. It is argued that such models may benefit in future work from improving their representation of recovery process, particularly how this process differs across loading conditions.

AB1259-HPR Correlation Between Perception of Fatigue and Peripheral Fatigue in Patients with Rheumatoid Arthritis

Commentaries on Viewpoint: Fatigue mechanisms determining exercise performance: Integrative physiology is systems physiology

Localised muscle fatigue (LMF) is a complex phenomenon that can differ between individuals, tasks, and muscles. Several muscle fatigue models (MFMs) have been developed in prior research. MFMs have potential practical value in ergonomics, given that LMF can impair performance, serve as a surrogate measure of injury risk, and may act as a causal factor for work–related musculoskeletal disorders. Existing MFMs are reviewed here, and which are broadly classified as either 'empirical' or 'theoretical'. Two specific MFMs, considered most ergonomically–relevant, were directly compared and some important differences in predictions were found. Identifying such differences is suggested as a useful approach, both for developing testable hypotheses and in guiding subsequent model development or refinement. Other potential approaches for improving future MFMs are also discussed, including expansion of model structure to account for individual differences (e.g., age, gender, and obesity), task related parameters, and variability in motor unit composition.

Manual demolition tasks are heavy, physically demanding tasks that could cause muscle fatigue accumulation and lead to work-related musculoskeletal disorders (WMSDs). Fatigue and recovery models of muscles are essential in understanding the accumulation and the reduction in muscle fatigue for forceful exertion tasks. This study aims to explore the onset of muscle fatigue under different work/rest arrangements during manual demolition tasks and the offset of fatigue over time after the tasks were performed. An experiment, including a muscle fatigue test and a muscle fatigue recovery test, was performed. Seventeen male adults without experience in demolition hammer operation were recruited as human participants. Two demolition hammers (large and small) were adopted. The push force was either 20 or 40 N. The posture mimicked that of a demolition task on a wall. In the muscle fatigue test, the muscle strength (MS) before and after the demolition task, maximum endurance time (MET), and the Borg category-ratio-10 (CR-10) ratings of perceived exertion after the demolition task were measured. In the muscle fatigue recovery test, MS and CR-10 at times 1, 2, 3, 4, 5, and 6 min were recorded. Statistical analyses were performed to explore the influence of push force and the weight of the tool on MS, MET, and CR-10. Both muscle fatigue models and muscle fatigue recovery models were established and validated. The results showed that push force affected MET significantly (p < 0.05). The weight of the tool was significant (p < 0.05) only on the CR-10 rating after the first pull. During the muscle fatigue recovery test, the MS increase and the CR-10 decrease were both significant (p < 0.05) after one or more breaks. Models of MET and MS prediction were established to assess muscle fatigue recovery, respectively. The absolute (AD) and relative (RD) deviations of the MET model were 1.83 (±1.94) min and 34.80 (±31.48)%, respectively. The AD and RD of the MS model were 1.39 (±0.81) N and 1.9 (±1.2)%, respectively. These models are capable of predicting the progress and recovery of muscle fatigue, respectively, and may be adopted in work/rest arrangements for novice workers performing demolition tasks.

Muscle fatigue is defined as the point at which the muscle is no longer able to sustain the required force or work output level. The overexertion of muscle force and muscle fatigue can induce acute pain and chronic pain in human body. When muscle fatigue is accumulated, the functional disability can be resulted as musculoskeletal disorders (MSD). There are several posture exposure analysis methods useful for rating the MSD risks, but they are mainly based on static postures. Even in some fatigue evaluation methods, muscle fatigue evaluation is only available for static postures, but not suitable for dynamic working process. Meanwhile, some existing muscle fatigue models based on physiological models cannot be easily used in industrial ergonomic evaluations. The external dynamic load is definitely the most important factor resulting muscle fatigue, thus we propose a new fatigue model under a framework for evaluating fatigue in dynamic working processes. Under this framework, virtual reality system is taken to generate virtual working environment, which can be interacted with the work with haptic interfaces and optical motion capture system. The motion information and load information are collected and further processed to evaluate the overall work load of the worker based on dynamic muscle fatigue models and other work evaluation criterions and to give new information to characterize the penibility of the task in design process.

Modification of a three-compartment muscle fatigue model to predict peak torque decline during intermittent tasks

Automation in industries reduced the human effort, but still there are many manual tasks in industries which lead to musculo-skeletal disorder (MSD). Muscle fatigue is one of the reasons leading to MSD. The objective of this article is to experimentally validate a new dynamic muscle fatigue model taking cocontraction factor into consideration using electromyography (EMG) and Maximum voluntary contraction (MVC) data. A new model (Seth's model) is developed by introducing a co-contraction factor 'n' in R. Ma's dynamic muscle fatigue model. The experimental data of ten subjects are used to analyze the muscle activities and muscle fatigue during extension-flexion motion of the arm on a constant absolute value of the external load. The findings for co-contraction factor shows that the fatigue increases when co-contraction index decreases. The dynamic muscle fatigue model is validated using the MVC data, fatigue rate and co-contraction factor of the subjects. It has been found that with the increase in muscle fatigue, co-contraction index decreases and 90% of the subjects followed the exponential function predicted by fatigue model. The model is compared with other models on the basis of dynamic maximum endurance time (DMET). The co-contraction has significant effect on the muscle fatigue model and DMET. With the introduction of co-contraction factor DMET decreases by 25:9% as compare to R. Ma's Model.

Sensitivity analysis of sex- and functional muscle group-specific parameters for a three-compartment-controller model of muscle fatigue

Recent evidence suggests that power output during repeated sprints of short duration is adjusted by the central nervous system to restrain the development of locomotor muscle fatigue to a critical threshold. This adjustment of power output might involve a complex interplay between peripheral and central determinants of muscle fatigue. The rate of peripheral and/or central fatigue development and its influence on the pattern of power output during repeated sprints remain however largely unknown. PURPOSE:We aimed to determine the contribution of peripheral and central fatigue to power output reduction during repeated sprints and whether power output levels off when a critical threshold of peripheral fatigue is reached. METHODS: On separate days, twelve healthy subjects performed the following tests: 1, 4, 6, 8 and 10, 10 s all-out sprints, each with 30 s of passive recovery between sprints, as well as 8, 10 s sprints, each with 10 s of passive recovery to test the influence of varying the work / recovery ratio on our hypotheses. Peripheral fatigue was quantified via changes in pre- to post-exercise potentiated quadriceps twitch force evoked by supramaximal electrical stimulation of the femoral nerve (ΔQtw, 0.5 through 6 min recovery). Central fatigue was estimated via changes in pre- to post-exercise voluntary quadriceps activation (ΔVA). EMG during sprints was normalized by maximal M-wave amplitude (ΔRMS.Mmax-1). RESULTS: From the 1st to the 6th sprint, we found a significant and gradual reduction in ΔQtw (- 47 ± 3 %), ΔRMS.Mmax-1 (- 7 ± 1 %) and power output (- 25 ± 2 %). During the 4 subsequent sprints, no additional reduction of quadriceps fatigue, RMS.Mmax-1 or power output was found but VA was significantly reduced compared to baseline (- 11 ± 2 %). Recovery of Qtw, post-sprints (0.5 through 6 min), was reduced progressively following sprint #1 through #6, with no further reduction thereafter (through sprint #10). CONCLUSIONS: Both peripheral and central fatigue contribute to the reduction of power output during repeated sprints. The leveling off of power output and RMS.Max-1 when the same degree of peripheral fatigue was reached, independently of the work / recovery duration ratio, suggest that central motor drive was restricted to limit excessive development of peripheral muscle fatigue.

Although automatic techniques have been employed in manufacturing industries to increase productivity and efficiency, there are still lots of manual handling operations, especially for assembly and maintenance operations. In these operations, musculoskeletal disorder (MSD) is one of the major health problems due to overload and cumulative physical fatigue. With combination of conventional posture analysis techniques, digital human modeling and simulation (DHM) techniques have been developed and commercialized to evaluate the potential physical exposures. However, those ergonomic analysis tools are mainly based on posture analysis techniques, and until now there is still no fatigue index available in commercial software to evaluate the physical fatigue effectively. In this paper, a new muscle fatigue and recovery model is used to evaluate joint fatigue level in manual handling operations. The physical fatigue in a special application case is described and analyzed using digital human simulation techniques.