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A new simple dynamic muscle fatigue model and its validation (2201.01069v1)

Published 4 Jan 2022 in cs.RO

Abstract: Musculoskeletal disorder (MSD) is one of the major health problems in physical work especially in manual handling jobs. In several literatures, muscle fatigue is considered to be closely related to MSD, especially for muscle related disorders. In addition to many existing analysis techniques for muscle fatigue assessment and MSD risk analysis, in this paper, a new muscle fatigue model was proposed. The new proposed model reflects the influence of external load, workload history, and individual differences. This model is simple in mathematics and can be easily applied in realtime calculation, such as the application in realtime virtual work simulation and evaluation. The new model was mathematically validated with 24 existing static models by comparing the calculated METs, and qualitatively or quantitatively validated with 3 existing dynamic models. The proposed model shows high or moderate similarities in predicting the METs with all the 24 static models. Validation results with the three dynamic models were also promising. The main limitation of the model is that it still lacks experimental validation for more dynamic situations. Relevance to industry Muscle fatigue is one of the main reasons causing MSDs in industry, especially for physical work. Correct evaluation of muscle fatigue is necessary to determine work-rest regimens and reduce the risks of MSD.

Citations (73)

Summary

  • The paper introduces a simple, real-time dynamic muscle fatigue model based on cumulative load integration.
  • It quantifies muscle fatigue by modeling gradual strength capacity reduction using differential equations and validates predictions with high static MET correlation.
  • The model incorporates a fatigue index and shows strong alignment with established physiological models for both dynamic and static conditions.

This paper introduces a new mathematical model for predicting dynamic muscle fatigue, aiming to provide a simpler, computationally efficient alternative to existing models for real-time applications like virtual work simulation and ergonomic assessment. The motivation stems from the high prevalence of musculoskeletal disorders (MSDs) linked to muscle fatigue in physical jobs and the limitations of existing assessment methods (observational, self-report, complex physiological models).

The Proposed Dynamic Muscle Fatigue Model

The model aims to quantify fatigue based on two primary factors: the external load applied to the muscle over time (Fload(t)F_{load}(t)) and the individual's maximum strength capacity, represented by Maximum Voluntary Contraction (MVC).

  1. Current Muscle Capacity (Fcem(t)F_{cem}(t)): The core idea is that a muscle's maximum force-generating capacity (Fcem(t)F_{cem}(t)) decreases over time during contraction due to fatigue. The rate of this decrease depends on the current load relative to the current capacity. This is described by the differential equation:

    dFcem(t)dt=kFcem(t)MVCFload(t)\frac{dF_{cem}(t)}{dt} = -k \frac{F_{cem}(t)}{MVC} F_{load}(t)

* kk is a constant. * The term Fload(t)F_{load}(t) indicates that higher loads cause faster fatigue. * The term Fcem(t)/MVCF_{cem}(t)/MVC suggests that as the muscle fatigues (i.e., Fcem(t)F_{cem}(t) decreases), the rate of further fatigue slows down. This is analogized to the recruitment of muscle motor units, where fatigue-resistant (Type I) units remain active longer. * Integrating this equation yields the current capacity at time tt:

Fcem(t)=MVCek0tFload(u)MVCduF_{cem}(t) = MVC \cdot e^{-k \int_0^t \frac{F_{load}(u)}{MVC} du}

* If we define F(t)=0tFload(u)MVCduF(t) = \int_0^t \frac{F_{load}(u)}{MVC} du, which represents the normalized cumulative load, the equation simplifies to:

Fcem(t)=MVCekF(t)F_{cem}(t) = MVC \cdot e^{-kF(t)}

* For a constant static load (FloadF_{load}), where fMVC=Fload/MVCf_{MVC} = F_{load}/MVC, this becomes Fcem(t)=MVCekfMVCtF_{cem}(t) = MVC \cdot e^{-k \cdot f_{MVC} \cdot t}.

  1. Fatigue Index (U(t)U(t)): A fatigue index is proposed, representing the perceived level of fatigue. Its rate of change depends on the relative load (Fload(t)/Fcem(t)F_{load}(t)/F_{cem}(t)) and the inverse of the remaining relative capacity (MVC/Fcem(t)MVC/F_{cem}(t)):

    dU(t)dt=MVCFcem(t)Fload(t)Fcem(t)\frac{dU(t)}{dt} = \frac{MVC}{F_{cem}(t)} \frac{F_{load}(t)}{F_{cem}(t)}

* Integrating this gives the fatigue index:

U(t)=12k(e2kF(t)1)U(t) = \frac{1}{2k} (e^{2kF(t)} - 1)

This model is mathematically simple, involving exponential functions and integration of the load history, making it suitable for real-time computation in simulations.

Validation

The model was validated mathematically against existing models, not through new experiments.

  1. Static Validation:
    • The model was tested in the static case (FloadF_{load} is constant). The Maximum Endurance Time (MET) – the maximum time a static load can be held – is predicted by finding the time tt when the capacity Fcem(t)F_{cem}(t) drops to the level of the load FloadF_{load}.
    • From the model, this yields: MET=ln(fMVC)kfMVCMET = \frac{-\ln(f_{MVC})}{k \cdot f_{MVC}}, where fMVC=Fload/MVCf_{MVC} = F_{load}/MVC.
    • This predicted MET was compared to MET values from 24 empirical models found in the literature (for general tasks, shoulder, elbow, hand, back/hip) across various load levels (%MVC).
    • Results: High linear correlation (Pearson's r > 0.97 for most comparisons) was found, indicating the model follows similar trends. Similarity (Intraclass Correlation Coefficient, ICC) was high for simpler joints (elbow, hand, ICC > 0.9 for several) but moderate or lower for more complex areas (back/hip), potentially due to anatomical complexity, experimental variations in the original studies, and different mathematical forms used in the empirical models.
  2. Dynamic Validation:
    • Qualitative Comparison: Compared with a forearm model by Freund & Takala (2002) which also uses a concept of decreasing force capacity, and Wexler's physiological model (1997, 2000b) based on Ca2+ mechanisms. The proposed model showed similar conceptual behavior: higher loads/stimulation frequencies lead to faster fatigue (capacity reduction).
    • Quantitative Comparison: Compared with Liu et al.'s (2002) dynamic model based on motor unit activation/fatigue/recovery. Under simplifying assumptions (maximum effort, no recovery), Liu's model equations reduce to a form identical to the proposed model's capacity equation (Fcem(t)F_{cem}(t)) for the MVC condition. This provided quantitative agreement for the specific case of maximum voluntary contraction.

Application Framework

The paper proposes integrating this dynamic fatigue model into a virtual reality framework for ergonomic evaluation. This system (Objective Work Evaluation System - OWES) would combine:

  • Motion capture for posture data.
  • Haptic interfaces for force interaction data.
  • A virtual human model within a virtual environment.
  • Real-time calculation of the fatigue index U(t)U(t) using the proposed model based on captured motion and force data. This allows for dynamic, ongoing fatigue assessment during simulated tasks. A prototype system for evaluating 2D panel control tasks was mentioned.

Conclusion and Limitations

The paper concludes that the proposed model offers a simple, computationally feasible way to estimate dynamic muscle fatigue, considering load history and individual MVC. It shows promising alignment with existing static MET data and qualitative/limited quantitative agreement with other dynamic models. The primary limitation acknowledged is the lack of direct experimental validation, especially for diverse dynamic tasks beyond constant load or MVC conditions.