Modeling, Evaluation, and Control Optimization of Exosuit with OpenSim

Team Member

Jaehyun Bae

Overview

Motivation

DARPA Warrior Web Program

This project is motivated by DARPA Warrior Web Program.

There has been a lot of research into exoskeletons over the years to alleviate heavy loads that soldiers should burden, but strapping a person into a robotic outfit just isn't practical in a combat zone yet.
DARPA's Warrior Web program aims to build a lightweight suit that improves a soldier's endurance and overall effectiveness, while preventing injuries.
The main goals by developing the warrior web are

1. To prevent and reduce musculoskeletal injuries.
2. To augment positive work done by the muscles and reduce the physical burden

You can see the details on http://www.darpa.mil/Our_Work/DSO/Programs/Warrior_Web.aspx

Harvard Exosuit

In order to develop an under-suit that doesn’t interrupt wearer’s free movement, researchers are trying to make it soft and deformable, but still capable of applying force to body joints. Among several different projects in the Warrior Web program, I focused on Harvard exosuit.

Harvard Biodesign group, one of a project groups in this program, is trying to make their warrior web suit soft and light, and they call their suit harvard exosuit.
Experimental data proved that it can help loaded walking by reducing metabolic costs.

Challenge

As it is a new approach to assist human gait with deformable structure, there are many challenges while developing the exosuit. The examples of the challenges are

It is difficult to analyze the effectivness of exist.
It is difficult to find the optimal input force for actuators to reduce the metabolic cost
It is difficult to identify the effect of change of design parameters

The reasons for the challenges are

The under-suit may be soft and deformable
Hard to identify how external actuation assists loaded gait.
Experimental metabolic cost data is inconsistent case by case

Goals

Through this project, I tried to resolve the challenges in developing exosuit with Opensim simulation. Simulation can help developing soft wearable exosuit as it can gives an intuition on how exosuit help muscles, and what are the key features that one should care about when developing the suit.

I hope this project will construct a systematic way of analyzing and designing soft wearable device. The goal that I set as a starting point are

Evaluate the effectiveness of wearing active actuator on metabolic cost reduction during loaded walking.
Explain how exosuit can help loaded gait.
Verify the impact of changes in design parameters.
Find optimal control inputs for exosuit actuators.
Evaluate the effectiveness of Biarticular actuator

Strategy

Experimental data

Two different data were collected from a same subject.

One gait cycle of loaded walking (From left toe off to next left toe off)
One gait cycle of unloaded walking (From left toe off to next left toe off)

For both data, walking speed is identical, and mass of the load for loaded walking was 38kg.

Data type

1. Marker position data
2. Ground reaction force data

Subject

1. mass: 61.3kg
2. Sex: male

Modeling

To simulate an exosuit wearer, it is most important to create a model which can replicate a real subject as possible as we can. In this project, as the experimental data was acquired from a subject walking freely, it is not possible to make a realistic exosuit wearer. However, to make my model dynamically consistent to the experimental data, my model was gone through the basic steps of modeling procedure in Opensim, and then added actuators and metabolic cost probes.

How to model a subject wearing active actuator

The diagram describes what procedure my model had gone through.

The first three steps are basic modeling procedure in Opensim to make a model dynamically and kinematically consistent to the experimental data. For more information, refer to

After RRA is done to the model, probes for calculating metabolic cost were added to the model. . For more information about how to add the probes, refer to

Simulation-Based Design to Reduce Metabolic Cost

And then, I added active actuators to the model. Here, I used PathActuator class to simulate active actuators of exosuit, as they are cable driven actuator. If you are interested in how PathActuator works, refer to

OpenSIm::PathActuator Class

Sample models

Here are the figures of sample simulation models. I created several different types of models for both loaded gait and unloaded gait.

Loaded gait model

Three different types of models were created for loaded gait simulation

A model without actuator
A model with path actuators supporting plantar flexion
A model with path actuators supporting hip extension

The path actuator supporting plantar flexion is attached to heel and tibia, and the pathactuator supporting hip extension is attached to backpack and femur. Loaded mass was added to torso for simplicity.

Unloaded gait model

Same types of models were created for unloaded gait simulation. The main difference between loaded gait model and unloaded gait model is the mass of torso, and transparancy of backpack.

Optimization methodology

The idea to optimize the control input force for the actuator is to take advantage of CMC tool. The main reason we use CMC in OpenSim is to find a most suitable excitations for muscles to create body movement while minimizing activation. To see how it works, refer to

How CMC Works

In this project, I make use of different use of the objective function in CMC in order to find optimal input force.

CMC procedure contains static optimization process, and it tries to minimize the cost function J which can be represented as

When we add active actuators on OpenSim Model, the activation term in cost function becomes

Where X_muscle is muscle control and X_actuator is actuator control. X_actuator is part of activation state, and it is also adjusted after the optimization process.
Now, if we diminish the influnece of X_actuator on J, and run CMC, the optimizer tries to find X_actuator in order to minimize muscle activation.
We know that minimizing muscle activation correponds to minimizing metabolic cost, so we can we can say that the actuator input force resulted from CMC after diminishing the influence of X_actuator is the optimal actuator input for most efficient metabolic reduction.
Muscle force is constructed by the equation

And if we assign large value of maximum force to each actuator, then actuator control x_actuator decreases, so that the influence of actuator to J is decreases.

Using this methodology, I could find an optimal input for each actuator, and also see the metabolic cost reduction after active actuators are added to a model.

Result & Discussion

Metabolic cost change when active actuators are added to model

I investigated how much metabolic cost is reduced when active actuators are added to a model, and optimal input force is applied to actuators. I did simulation for both loaded and unloaded walking cases, and I compared the influence of hip actuator and ankle actuator to metabolic cost reduction.

I assigned 10,000N to maximum active actuator force in order to find optimal control input force through CMC.

Loaded walking	Unloaded walking

Metabolic cost reduction when active actuators are added to loaded gait model

1. Ankle actuator: 10.35%
2. Hip actuator: 6.62%

Metabolic cost reduction when active actuators are added to unloaded gait model

1. Ankle actuator: 10.62%
2. Hip actuator: 1.04%

Things to notice

1. The metabolic cost is much lower during unloaded walking than loaded walking. Loaded walking costs only 75% metabolic energy compared to loaded walking.
2. Ankle actuator works better to reduce metabolic cost than hip actuator when we can apply optimal input force.
3. Hip actuator is not assistive to unloaded gait.

Therefore, we can say that ankle actuator helps metabolic cost reduction better than hip actuator if we have an optimal actuator which has no maximum force limitation.

Optimal actuator input

	Loaded walking	Unloaded walking
Ankle actuator
Hip actuator

Optimal input force for Ankle actuator

Actuation is started right after the toe-off of a foot on the opposite side, and the peak force occurs 7.12% of gait cycle before toe off, and ends at the toe-off of a foot on the same side.
This actuation scheme is valid for both loaded gait and unloaded gait.
This force signal is clear and easy to implement in real world.
However, the maximum actuation force is about 2500 N, which is too high

Optimal input force for Hip actuator

Hip actuator can reduce the metabolic cost with lower maximum force than ankle actuator.
However, it is hard to identify how the actuator assists walking.
Also, it is difficult to implement the optimal control input for hip actuator in real world

Analysis of optimal input force for ankle actuator

I could explain how the optimal actuation input for ankle actuator could help loaded gait by investigating the change of plantar flexor muscle forces

The gastrocnemius muscle forces are barely changed.
Other plantarflexor muscle forces, including Soleus muscle forces, are significantly decreased.
If we draw the sum of baseline uniarticular forces and active actuator input force together, we can see that the active actuator force follows baseline uniarticular muscle forces.

To sum up, we can say that ankle actuator assists uniarticular muscles during loaded walking.

Best realistic actuation input force for ankle actuator

Optimal input force when actuation force is limited to 400N

From the previous results, we found that the optimal force for hip actuator is small enough to be achieved by real actuator, while the optimal input force for ankle actuator is not achievable. (>2000N). Therefore, I tried different types of input forces for ankle actuators up to 400N, and compare the results to optimal control input case.

My initial guess was to saturate the optimal input force that I found earlier at 400N. I generated an new input force which is identical to optimal input force up to 400N, and saturated once optimal input force exceeds 400N. And then, I run CMC after setting the actuator input forces as a control constraints for each case.

I compared the saturated optimal input to a new CMC results which was acquired after assigning 4000N to maximum actuation force and bounding control input between 0 and .1.

In other word,

According to the formula, the new CMC results also has maximum force of 400N, and it gives better result in terms of metabolic cost reduction than a result of CMC which was acquired with an actuator with 400N actuation and conventional control input.

You can see a similarity between the saturated optimal input and a results of new CMC procedure.

Metabolic cost reduction

Loaded walking	Unloaded walking

Metabolic cost reduction when active actuators are added to loaded gait model

1. optimal: 10.35% reduction
2. Saturated: 1.84% reduction
3. New CMC: 2.68% reduction

Metabolic cost reduction when active actuators are added to unloaded gait mode

1. optimal: 10.62% reduction
2. Saturated: 3.46% reduction
3. New CMC: 3.82%

The result from new CMC procedure reduces metabolic cost more efficiently.
However, the reduction is not significant, and it is much lower than the optimal case.
The interesting thing is that the realistic actuation input force works better in unloaded walking case than loaded walking case. It makes sense because we requires lower force to assist unloaded walking.

Biarticular actuator

Now that we know both ankle actuator hip actuator works well to reduce metabolic cost during loaded walking, the natural progress is to create biarticular actuator which can affect both ankle plantar flexion and hip extension. In order to reduce the number of actuator, I created biarticular actuator with 1 DOF, and see how much it reduces metabolic cost, and what it’s optimal input force is.

Modeling

Simulation result

Optimal input	Metabolic cost reduction

Metabolic cost reduction when biarticular actuators are added to loaded gait is 3.12% from baseline. It is much lower than reduction rate of ankle actuator or hip actuator.
Control input is noisy, which makes it hard to realize
Biarticular actuator is not as effective as uni-articular actuators in terms of metabolic cost reduction.

Conclusion

Both hip actuator and ankle actuator can reduce the metabolic cost during walking

1. If we can apply sufficient amount of force, it is better to apply force to ankle joint.
2. If not, hip actuator is a good alternative, even though it is hard to control

Exosuit offers greater assist for loaded walking than unloaded walking
Optimal input of ankle actuator is consistent with gait cycle and muscle forces data, while that of hip actuator is not.
The optimal input force for ankle actuator when it’s maximum force is bounded is similar to the general optimal input force saturated at maximum force
Biarticular actuator doesn’t assist loaded walking very well and the force input is not consistent.

Limitations

The experimental data was obtained from a subject without exosuit. Exosuit may change the kinematics of a subject as well as GRF.
The simulation methodology to use CMC as an optimization tool works, but more improvement is needed.
CMC process doesn’t minimize metabolic cost. Instead, it minimizes 2-norm of activation.
The experimental data is only one gait cycle.
More realistic actuator simulation is needed. (E.g. Combination of passive & active actuator.

Source code

You can find the model that I used in htt~~~~