USE CASE TO SIMULATION: MUSCULAR FATIGUE MODELING AND ...

Turkish Journal of Physiotherapy and Rehabilitation; 32(2)

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www.turkjphysiotherrehabil.org

USE CASE TO SIMULATION: MUSCULAR FATIGUE MODELING AND

ANALYSIS USING OPENSIM

KORUPALLI V RAJESH KUMAR1, SUSAN ELIAS2 1Research Scholar, School of Electronics Engineering, Vellore Institute of Technology,

Chennai, TamilNadu – India [email protected] 2Professor & Deputy Director, Centre for Advanced Data Science, School of Electronics

Engineering, Vellore Institute of Technology, Chennai, TamilNadu – India [email protected]

ABSTRACT

Background: The Human Body system builds on a digital platform helping researchers to

observe valuable and informative insights from the body's biological and physiological changes. In

this context, the OpenSim simulation tool became popular and used in numerous applications.

Objective: Use-case-based model building for simulation - that is during the simulation, the digital

human model has to walk for 1200 seconds, With and Without rest conditions. From the simulation,

Fatigue Analysis carried out using muscle force data extracted from the OpenSim-based CMC tool.

Methods: Full-body musculoskeletal system developed by the OpenSim research community used as

a digital human body. Body movements were calibrated by adjusting the .mot file as per mentioned

research objectives - With and Without rest conditions, for 1200 seconds. CMC tool

responses extracted, lower limb muscles force data analyzed and used to assess muscle fatigue.

Results: Correlation analysis carried out on lower limb muscle force exertions of right and left leg

separately, during With and Without results conditions. Significantly found the Muscle force decline

state of each muscle i.e is Fatigue point.

Conclusion: These methods are used to analyze the various ergonomic and occupational-related

tasks in the simulation environment, where human physical presence not require. In this experiment,

results depict that the simulation modeling approach significantly reaches and outperforms the

existing techniques for the analysis of muscular fatigue states.

KEYWORDS:

Human Body, OpenSim, Simulation, Walking, With Rest, Without Rest, Fatigue.

I. INTRODUCTION

Musculoskeletal disorders are common symptoms in aged people. In adults, it also depends on a few factors

like occupation, habit, and living circumstances. Irrespective of age and gender, musculoskeletal disorders

affect a specific group of muscles and bones. There are a lot of experimental research studies and

investigations being carried out to find the relation between muscle strength, fatigue, and age-related factors.

The human body system consists of nerves, muscles, tendons, ligaments, blood vessels, etc. In these, muscles

are the active tissues that generate forces to drive the body, causing skeletal motion. The somatic nervous

system controls the skeletal muscle contractions producing forces that are transferred to the skeletal system,

resulting in body movement [1–5]. In certain cases of movement analysis, research on humans is not possible,

due to limitations of the human physiological system. Human-computer interface, simulation modeling, and

computational techniques were used along with the latest technologies to develop digital human models.

These tools help us to understand and analyze the biological and physiological parameters of the human body

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system over a wide spectrum [6–8]. In this research, OpenSim simulation software is used to create real-world

use-case-based scenarios to analyze muscular fatigue. Fatigue is defined as the reduction in muscle

performance ability, i.e., a decline in the muscle force [1, 9–11]. Scenarios are created viz. walking for 1200

seconds ‘with’ and ‘without’ rest conditions. Simulation modeling helps to extract reliable information but

requires utmost effort and care. In simulation modeling, every phase is exceptionally challenging to

understand the physiological and biological phenomenon. The OpenSim simulation platform has in-built tools

for Inverse Kinematics, Static Optimization, Computed Muscle Control (C.M.C.), and Analyzer tool. These

tools generate complex computational data of muscles, tendons, and joints, as per the simulation movements.

The responses of the C.M.C. tool are Kinematics and Kinetics of muscles, joints, and tendons in the form of

forces, velocity, length, power, activation, etc. These responses signify the physiological changes during the

simulated movement. In this research, muscle kinetic responses generated as muscle fiber force data are

analyzed [12–14].

The group of muscles is stretched or shortened to different extents, inducing different levels of muscle forces.

Responses of the C.M.C. tool are in three states: active, passive, and total states by considering force, velocity,

and length. When there is no muscle activation, muscle force depends on restorative passive force against

stretching. When the muscle gets activated it contracts and results in the generation of the active force. Hence

total muscle force is a summation of active and passive muscle forces. In this research, a digital human model

developed by the OpenSim community was used for the simulation. A use-case-based model was built for the

simulation, that accurately mimics human movements. Here, the primary objective is to analyze the response

of the simulation model ‘with’ and ‘without’ rest conditions [10, 15].

The primary motivation of this research is to present a methodology by analyzing the muscular fatigue state of

the human body by using simulation modeling. Improper postures, occupational conditions, habitual postures,

long working hours, walking / running for a long time, age, and disorders are some significant causes of

muscular fatigue. Currently, there are few effective methods available to find muscular fatigue in both

practical and theoretical ways. Electromyography (E.M.G.) is the standard gold method, but it requires a vast

area, clinical setup, and also cost-effectiveness. Even though we can use Inertial Measurement Unit devices to

track and record movement data in a non-invasive manner, but then it requires subject attention and presence

in a study [15–21]. To overcome challenges in conventional methods, we used a simulation platform to

achieve the research objectives.

II. SIMULATION MODELS AND METHODS

The primary objective of this research is to analyze muscular fatigue based on digital modeling. In this

context, the OpenSim simulation tool is used for the simulation. The Full Body- Lower Limb

Musculoskeletal Model is used for this experimental study and was developed by the OpenSim community

team of researchers [22].

Full Body- Lower Limb Musculoskeletal Model

The Full-Body Lower Limb musculoskeletal model is considered for analysis of muscular fatigue [22]. In this

simulation model, muscular fatigue and metabolic cost variations can be found during walking and running

states. This model is considered for finding muscle fatigue during walking ‘with’ and ‘without’ rest

conditions. This musculoskeletal model consists of lower extremity muscles. We are aware that, the body

system consists of numerous muscles, to drive skeletal motion [22]. In this context, six significant muscle

responses are considered in this analysis. In Figure 1, muscles are shown with color markers. This full-body

musculoskeletal system simulation model file is available in the “.osim” file format.

OpenSim Platform – Inbuilt Tools Responses

The OpenSim simulation platform consists of several inbuilt tools. These tools help us in building and

executing simulation models. Each tool gives a specific set of responses; the Scaling tool is used to adjust the

physical dimensions of the simulating model and responses are saved in the ``.osim'' format. This file is

loaded into OpenSim for visualization and execution. Then “.trc”-file [track row column] is given as an input

to the Inverse Kinematics tool that generates the “.mot” file [motion file] as an output. This motion file is used



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Figure 1. Lower Limb and other Muscles – Six important muscles shown in different colors.

to create movements in the skeletal system. The corresponding actions can be adjusted by observing it on

Graphical User Interface (G.U.I.). The “.mot” -file is fed as an input to the Inverse Dynamics tool, resulting in

the file “Inverse-Dynamics.sto” as an output. Then the “.mot “file is given as an input to the Static

Optimization tool and extracts the following output files:

1. Full body_Static_Optimization_Controls.xml,

2. Full body_Static_Optimization_Activation.sto,

3. Full body_Static_Optimization_Force.sto.

Figure 2. Opensim Simulation platform inbuilt tools and its responses



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As the next steps, .trc and .mot files are fed as inputs to the Computed Muscle Control [C.M.C] tool, which

produces kinematics and kinetics parameters of the joints, muscles, and tendons. These output parameters

symbolize the joint kinematics and muscular kinetics like velocity, forces, power and length parameters, etc.

for active, passive fiber, and tendons. Similarly, by using the Analyzer tool additional parameters like Probes

report, Joints report, Point, Body kinematics, Induced Accelerations, Muscle Analysis, and Output reports, can

be obtained [23]. Figure 2. shows the functional structure of OpenSim and its inbuilt tools.

Simulation Methods

There are various techniques and research methods developed to analyze the muscle fatigue conditions during

walking and running states with multiple speeds and loads. To conduct this experimental research, a treadmill

model of structured equipment is Video capturing systems, Bio-Markers, Inertial Measurement Units,

Electromyography, and Pressure plates are required. These are expensive and large areas are required for

installation. The outcomes of these experiments provide insights into the human muscle stamina based on

walking/running speed, inclination, and loads parameters.

Figure 3. Opensim Simulation platform-based research flow methods

The OpenSim simulation tool helps to build and analyze the human physiological parameters in simulation

mode. This model is entirely non-invasive and easy to develop. Figure 3 shows the functional flow of this

research. The study commences with the selection of simulation files i.e., the “.osim” file. Based on the use-

case scenarios, movements can be calculated and adjusted in the “.mot” file (motion file). OpenSim - G.U.I.

helps to view the skeletal movements during the simulation. G.U.I., based visualization is the initial step in

this research. If the skeletal motion is not matched with the actual use-case model the “.mot” file can be

calibrated and readjusted till the skeletal movement matches with the exact use-case motion model. In Figure

3, the simulation modeling process is shown. Once the motion file is finalized with computational values and

modifications, it needs to be set up as an input to the Static Optimization tool (S.T.O.). This tool generates

control inputs, this data is fed as an input to the C.M.C. tool along with a motion file. This C.M.C. tool

provides its responses in the form of kinematics and kinetics data of the body based on motion data. These

results are analyzed using the Analyzer tool.



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Simulation Model-based Datasets

Table 1. OpenSim inbuilt tools and its responses – Dataset Dimensions

In this research, the .mot file was created to make the skeletal motion simulation for 1200 seconds on a

timeline. This 1200 seconds timeframe signifies that every – input/output file has 1200 rows of data. The

output format of the tool has differed, and information also varied according to the tool's functional property.

Table 1 shows the Rows and Columns of datasets produced by the OpenSim inbuilt tools at different stages.

III. USE-CASE BASED SIMULATION MODELING

Musculoskeletal Walking Model

The primary objective of this section is to find a state of muscular fatigue during walking. The G.U.I.-based

visualization helps to adjust the “.mot” file. The musculoskeletal movements were calibrated and computed

for the final “.mot” file based on the use-case scenario. Here, the Full-body musculoskeletal model simulated

for 1200 seconds within a time frame. In this simulation, the skeletal model walks with a speed of 1 m/s on a

flat surface.

Here, the simulation runs on two conditions.

1. Continuous Simulation [ Without Rest ]

And here, the simulation runs for 1200 seconds without any rest (20 minutes).

2. Provided Halt During Simulation [ With Rest ]

In this condition, the simulation runs for 1200 seconds ( 20 minutes). But with the rest condition added on a

timeline of 0 to 300seconds, 60 seconds of rest is given[1st rest point ] and then simulation resumes from 360

seconds. At 600 seconds on a timeline simulation halts for 60 seconds, till 660 seconds [2nd

rest point], then

simulation resumes till 960 seconds and then rest is provided till 1020 seconds [3rd

rest point ], then simulation

resumes till 1200 seconds on a timeline at an endpoint. Figure 4 shown the entire scenario.

Tool Output -Dataset

Format

Dataset

Dimensions

[rows X

columns]

Data Representation

Inverse

kinematics

.mot file

[motion file]

1200 X 40 Joints and its Co-ordinate Movements

Inverse

Dynamics

.sto file

[Generalized Forces]

1200 X 40 Joints and its Co-ordinate Movements

Static

Optimization

.xml, .sto

[Controls,Activations,

and Forces]

1200 X 98 Muscles, Joints responces – Activations, forces

and Controls

Computed

Muscle

Control

.xml, .sto files

[Controls, Activations,

and Forces]

1200 X 98 Kinematics of joints, and kinetics of muscles,

tendons for active and passive fibers, etc.

Analyzer .xml, .sto files

[Controls, Activations,

and Forces]

1200 X 98 Kinematics, Actuation,

Point Kinematics, Body Kinematics, Muscle

Analysis, Joint Reaction,

Static Optimization, ForceReporter,

StatesReporter, InducedAccelerations,

ProbeReporter, OutputReporter.



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Figure 4. OpenSim Simulation-based Walking Model with and without Rest conditions

Simulational Analysis – Execution & Results

Experimental research was carried out using With_Rest and Without_Rest conditions. OpenSim-C.M.C. tool-

based computational results were joint kinematics, muscular kinetics like forces, velocity, and length, etc.

Muscle active-force data was considered to find the muscular fatigue point.

C.M.C tool-based computational result –Fiber_Force data was analyzed. This data consists of 1200 rows X 98

columns of data points. In this dataset, rows signify the muscle force exertion based on the movements on a

timeline, and 98 columns signifying various muscles of the full-body musculoskeletal system. Among 98

muscles, few were enough to describe human gait and the pattern of walking. Those muscles were Gaslat,

Gasmed, Soleus, Tibant, Vaslat, and Vasmed.

According to simulation timing analysis, muscle fiber force exertions can be extracted based on movement

patterns. After the continuous walk –i.e. initially Without_Rest condition was simulated followed by Walk-

With_Rest condition. Here, the rest condition in the simulation model is achieved through the static position

for the pre-defined time interval. Once pre-defined time-interval exceeds, simulation models resume till the

next rest-interval. Here, the rest condition is given at 300, 600, and 960 seconds respectively on a timeline

with 60 seconds of rest condition. Figures 5. A and B show the right and left muscle force exertions during

walking ‘with’ and ‘without’ rest conditions on a timeline of 0 to 1200 seconds.

Comparative Analysis

The objective of this research is to find valuable insights between Without_Rest and With_Rest conditions

during the walk. In this context, the observed key factor is, that the muscles in the Without_Rest model, exert

more forces to complete the task, i.e., to reach the endpoint simulation, executed for 1200 seconds on a

timeline. During this phase, muscles use more energy and release more force to move the skeletal body, which

indirectly results in muscle fatigue. Similarly, the observed muscle response of Walk-With_Rest condition and

walk Without_Rest condition, muscle force exertion was in a low state. This is due to providing rest between

the movements. This effect directly signifies that the With_Rest model reduces the strain on muscles which

results in lower muscle fatigue. Here, in the simulation - during the walk, muscles consume energy and release



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the force to make a move of the skeletal body. In this regard, as time exceeds from one to its maximum range

and then declines towards the fatigue range. By the nature of some muscles, they exhibit force exertions.

5 A. Right Leg muscles force exertion during With and Without Rest conditions on Timeline.



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Figure 5 B. Left Leg muscles force exertion during With and Without Rest conditions on Timeline.

IV. DISCUSSIONS



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When compared to the muscle responses, it is observed that providing rest during a continuous walk gives the

potential to our musculoskeletal system to complete the task effectively. This resting time mostly depends on

individual physiological parameters. In this study, comparative results were shown. From these results, we

strongly believe that providing sufficient rest between walking stages helps to maintain muscle health. In this

regard, it is found the correlation between muscles during walking ‘with’ and ‘without’ rest conditions and

observed that there is a low correlation between muscles ‘with’ and ‘without’ rest conditions. In both

conditions, muscles exhibit different levels of force exertion ranges. In both cases - WithRest condition

muscles exerted low force when compared with the Without rest condition. Figures 6 A, and B show the

cumulative muscle force exertion levels of Right and left leg muscles during With and Without rest

conditions, and Figure 6 C shows the comparison chart of both. The cumulative muscle force of each muscle

is the summation of muscle force exertion during the walking on a timeline (0th sec to 1200

th sec). This value

represents the muscle total force exertion rate to complete the task.

Figure 6 A. Right Leg Muscles Cumulative Force exertion to complete the task,

B. Left Leg Muscles Cumulative Force exertion to complete the task,

C. Right and Left Leg Muscles Cumulative Force exertion comparison

Muscles correlation Analysis

Figure 7 A shows the Right leg muscles correlation during the walk with and without rest conditions, similarly

Figure 7 B shows the Left leg muscles force exertion correlation.

Right Leg Muscles force exertion correlation

1. Gaslat muscle has 0.77% of correlation in With and Without Rest condition and it has 0.36% - 0.43% of

correlation with Tibant muscle With and Without rest conditions, apart from this, it has a weaker correlation

with remaining muscle force exertions.

2. Gasmed muscle has 0.49% of correlation in With and Without Rest condition, it has 0.28% - 0.45% of

correlation with Tibant muscle With and Without rest conditions, apart from this it has a weaker correlation


3. Soleus muscle has a weak correlation between With and Without rest conditions, and similarly not having a

high correlation with other muscles too.



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4. Tibant muscle has a high correlation between With and without rest conditions, i.e 0.52%. Apart from this,

it has a significant correlation with Gaslat, Gasmed, and Vaslat muscle force exertions.

5. Vaslat muscle has a lower correlation between With and Without rest conditions. But it has a slightly high

rate of correlation with Tibant muscle force exertions With and Without Rest conditions.

6. Vasmed muscle has a high correlation between With and Without rest conditions, i.e 0.53%.

Figure 7 A. Right Leg Muscles force exertion correlation

Left Leg Muscles force exertion correlation

1. Gaslat muscle has 0.66% of correlation in With and Without Rest condition, then, it has 0.32% - 0.44% of

correlation with Tibant muscle With and Without rest conditions, apart from this it has a weaker correlation


2. Gasmed muscle has 0.65% of correlation in With and Without Rest condition, then, it has 0.35% - 0.58%

of correlation with Tibant muscle With and Without rest conditions, apart from this, it has a weaker

correlation with remaining muscle force exertions.

3. Soleus muscle has a weak correlation between With and Without rest conditions, and it has 0.34% to 0.4%

with Vaslat and Vasmed muscle With and Without rest conditions.

4. Tibant muscle has a high correlation between with and without rest conditions, i.e a 0.53%. Apart from this,

it has a significantly high correlation with Gasmed and a slightly good correlation with Gaslat, Vaslat, and

Vasmed muscle force exertions.

5. Vaslat muscle has less correlation between with and without rest conditions. But it has a slightly high rate

of correlation with Vasmed, Tibant, and soleus muscle force exertions With and Without conditions.

6. Vasmed muscle has less correlation between with and without rest conditions. But it has a slightly high rate

of correlation with Vasmed, Tibant, and soleus muscle force exertions with and without conditions.



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Figure 7 B. Left Leg Muscles force exertion correlation

V. CONCLUSION

This paper presents a simulation model-based analysis of human gait and associated muscle performance.

Using this investigation, it is found that muscle fatigue behavior during walking – With and Without Rest

conditions. In the real world, in some scenarios, getting human physiology-based real-time data is very

difficult and sensitive this simulation, modeling is the only option. This research successfully simulated the

tasks and analyzed them. In this research, the Digital Human model is used to simulate the Walking scenario

With and Without Rest conditions. From simulation results – acquired data and done the analysis, and found

the correlation between muscles of Right and left leg independently during Walking With and Without Rest

conditions. This Methodology can be applicable where there is a challenging task to analyze and when there is

no access to acquire the data.

VI. ACKNOWLEDGMENT

The research work presented in this paper is part of the project titled Design & Development of a simulation

model for predictive analysis of load carriage, funded by Life Sciences Research Board (LSRB), Defence

Research Development Organisation (DRDO), Government of India. We acknowledge the support and

encouragement provided by the Director and concerned scientists of Defence Institute of Physiology and

Allied Sciences (DIPAS) Laboratory, DRDO.

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