Team Members
- Jaehyun Bae
Motivation
DARPA Warrior Web Program
- There has a 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
- To prevent and reduce musculoskeletal injuries.
- To augment positive work done by the muscles and reduce the physical burden
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.
- 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.
Challenges
- 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
Project 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 construct a systematic way of analyzing and designing exosuit.
Strategy
Experimental data
Two different types of data was 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 loaded weight was 38kg.
Modeling
How to model a subject wearing active actuator
Add explanation here- The diagram
Sample models
Loaded gait model
Add explanation here
Unloaded gait model
Optimization process
The idea to optimize the control input force for the actuator is to take advantage of the optimization procedure in CMC tool. CMC procedure. CMC procedure contains static optimization process, and it tries to minimize the cost function J which can be represented as
When there is active actuators on OpenSim Model, the activation term in cost function becomes
and,
Assign large value of maximum force to each actuator to reduce the size of xactuator, so that the influence of actuator to J is diminished.
Result & Discussion
Metabolic cost change
| Loaded walking | Unloaded walking |
|---|---|
 |  |
Loaded walking
I put together the metabolic cost changes in loaded gait case and unloaded walking case
The first thing to notice is that the metabolic cost is much lower during unloaded walking than loaded walking. Loaded walking costs only 75% metabolic energy compared to loaded walking. Also, we can see that ankle actuator works better to reduce metabolic cost than hip actuator, especially in loaded walking case.
In loaded walking case, ankle actuator reduces metabolic cost by 10%, while hip actuator reduces it by about 7%.
On the other hand, in unloaded walking case, ankle actuator reduces metabolic cost by 10%, while hip actuator reduces it by 1%.
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 |  |  |
In hip actuator case, the optimal input force is very complex, and I could not find any intuition from it. My initial guess on the optimal input force of hip actuator was to follow the hip flexion angle change, but the result doesn’t follow it at all. The complexity may be due to the optimization procedure in CMC, and I will try to figure out the reason in a future. However, the good thing about hip actuator is that it doesn’t require large amount of force to reduce the metabolic cost. The maximum force in this optimal input is about 400N, which is significantly lower than ankle actuator input, and it is achievable.
Unloaded walking
This is the optimal input force result in unloaded case. Still, you can see that ankle actuator input profile is clean and goes well with our intuition, but hip actuator input profile isn’t.
Analysis of optimal input force for ankle actuator
This result shows how muscle force changes when a model has ankle actuators. The graphs show plantar flexor muscle forces, and first row is muscle forces of a baseline model, and the second row is the muscle forces of a model with ankle actuator.
The red line is The muscle forces of gastrocnemius, and it barely change when ankle actuators are added. However, other muscle forces, which are from uniarticular muscles, are significantly decreased. Therefore, we can say that ankle actuator assists uniarticular muscles during loaded walking
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. The redline here is sum of baseline uniarticular forces and blue line is active actuator input force. This force signal is clear and easy to implement real world. However, the maximum actuation force is about 2500 N, which is too high, so we need to deal with it if we want to use this profile.
Best realistic actuation input force for ankle actuator
As the optimal input force for ankle actuator is not achievable, I tried to find a realistic ankle actuator force which has its maximum force of 400N. My initial guess was to saturate the optimal input force that I found earlier at 400N. Therefore, I cut the optimal input force at 400N, and create new input force. I added this force profile to CMC tool as a control constraints, and run CMC again.
I compared the saturated optimal input to a new CMC results which was acquired with 4000N maximum actuation force and bounded control input between 0 and .1. The new CMC results also has maximum force of 400N as the control input is bounded, and it gives better input force in terms of metabolic cost reduction than a result of CMC which was acquired with an actuator with 400N actuation and conventional control input.
In these graphs, you can see a similaritly between the saturated optimal input and a results of new CMC procedure.
| Loaded walking | Unloaded walking |
|---|---|
 |  |
Discussion
ModelBiarticular 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.
Simulation result
Loaded walking- rate of metabolic reduction
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
Featured result
Limitations
Source code
You can find the model that I used in htt~~~~