Team Member
Project Overview
Motivations
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
- 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 exosuit is the example of new approach to create under-suit in a soft and deformable manner.
- Experimental data proved that it can help loaded walking by reducing metabolic costs.
- This suit applies force to lower limb joints through cables driven by actuators on backpack.
Challenges
As the Exosuit tries to assist human gait with deformable structure, there are many challenges in developing the exosuit. The challenges are
- It is difficult to analyze the effectiveness of the suit
- 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 suit is deformable and closely attached to a body
- We can not predict how external actuation assists muscles during loaded walking as human body is highly redundant
- Experimental data is inconsistent case by case
Goals
This project attempts to tackle the challenges of developing wearable device for supporting loaded gait with OpenSim simulation. Simulation can help developing the wearable device as it can give an intuition on how the device help muscles and how metabolic cost changes during loaded walking. We can also find the key features that one should account for in order to make the device more efficient. I hope this project will construct a systematic way of analyzing and designing soft wearable device. The initial goals of this project are
- Evaluate the effectiveness of wearing active actuators on metabolic cost reduction.
- Explain how Exosuit can help loaded gait
- Verify the impact of changes of design parameters
- Find optimal control inputs for active actuators
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)
- The subject didn't wear a suit and walked freely.
- walking speeds were identical for both cases
- Mass of the load was 38kg.
Modeling
To simulate the movement of exosuit wearer, the first thing to do is to create a model which can replicate a real subject as possible as we can. Before I created the simulation model with active actuators on it, I had the generic gait model go through the basic steps of modeling procedure in OpenSim. By doing so, I could make my model dynamically consistent to the experimental data. And then, I added actuators and metabolic cost probes to the model.
How to model a subject wearing active actuator
The diagram describes what procedure the generic gait 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, read
- How Scaling Works
- How Inverse Kinematics Works
- How RRA Works
- After step 3 is complete, probes for calculating metabolic cost were added to the model. . For more information about how to add the probes, read
Simulation-Based Design to Reduce Metabolic Cost
- Finally, 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, read
OpenSIm::PathActuator Class
Sample models
Here are the figures of sample simulation models. I created several different types of models by modifying RRA-adjusted model to compare them for their evaluation.
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 path actuator supporting hip extension is attached to backpack and femur. For Simplicity, Loaded mass was added to torso.
Unloaded gait model
Same types of models were created for unloaded gait simulation.
Optimization methodology
The idea to optimize the control input force for the actuators 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 muscle activations. To see how it works, read
In this project, I make different use of the optimization process in CMC in order to optimize the control input force for active actuators.
- CMC procedure is static optimization process, and it minimizes the cost function J which can be represented as
J = \sum_{i=1}^{n_x} x_i^2 + \sum_{j=1}^{n_q} w_j \left( \ddot{q}_j\,^* - \ddot{q}_j \right)^2 |
- When we add active actuators on OpenSim Model, the activation term in cost function becomes
x = \begin{bmatrix} x_{muscle} \\ x_{actuator} \end{bmatrix} |
Where Xmuscle is muscle control and Xactuator is actuator control. As Xactuator is part of activation states, it is also adjusted after the optimization process.
- Now, if we diminish the influnece of Xactuator on J, and run CMC, the optimizer tries to find X_actuator in a manner of minimizing muscle activations.
- We know that minimizing muscle activation correponds to minimizing metabolic cost, so we can conclude that the actuator input force resulted from CMC if we diminish the influence of Xactuator to objective function J is the optimal actuator input for metabolic cost reduction.
- Muscle force is constructed from the equation Factuator = Factuatormax * Xactuator. if we assign large value of maximum force to each actuator, actuator control Xactuator decreases, so the influence of actuator to J is decreases.
- Using this idea, I could find an optimal input for each actuator, and also found the metabolic cost reduction after running CMC with a model where active actuators are added.
Result & Discussion
Metabolic cost change when active actuators are added to model
I investigated how much metabolic cost is reduced when optimal input force is applied to a model by active 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( Factuatormax) for this simulation.
| Loaded walking | Unloaded walking |
|---|
 |  |
 |  |
- Metabolic cost reduction when active actuators are added to loaded gait model
- Ankle actuator: 10.35%
- Hip actuator: 6.62%
- Metabolic cost reduction when active actuators are added to unloaded gait model
- Ankle actuator: 10.62%
- Hip actuator: 1.04%
- The metabolic cost is much lower during unloaded walking than loaded walking. Loaded walking costs only 75% of metabolic energy spent during loaded walking.
- Ankle actuator works better to reduce metabolic cost than hip actuator when we can apply optimal input force.
- 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 ideal actuators which has no maximum force limitation.
Optimal actuator input force
| | 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 happens between the heal-strike of a foot on the opposite side and the toe off of a foot on the same side, and ends at the toe-off of a foot on the same side
- Same actuation strategy is valid for both loaded gait and unloaded gait
- The force signal is clear and easy to implement in real world
- However, the maximum actuation force is about 2500 N, which is too high to achieve in reality
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
How ankle actuator assists loaded gait
I could explain how the optimal actuation input for ankle actuator helps 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 the sum of baseline uniarticular muscle forces.
To sum up, 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 input force for hip actuator is small enough to be achieved by real actuator, while the optimal input force for ankle actuator is not realistic. (>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.
- The second input force I tried is a new result from different CMC procedure. The new CMC result was acquired after assigning 4000N to maximum actuation force and bounding control input between 0 and 0.1.
In other word,
F_{actuator}^{max} = 400 N, 0 \leq x_{actuator} \leq 0.1 |
According to the formula Factuator = Factuatormax * Xactuator, the new CMC results also has maximum force of 400N. As Xactuator is bounded between 0 and 0.1 and Xmuscle has is chosen between 0 and 1, the influence of Xactuator to objective function of CMC procedure is relatively lower than that of Xmuscle , so we can use this idea to create optimal input for active actuator when the maximum actuation force is limited.
When we compare the saturated optimal input and the result from new type of CMC procedure, we can find a similarity between the saturated optimal input and a results of new CMC procedure. Now, let's compare the metabolic cost reduction when each control input is applied to ankle actuators.
Metabolic cost reduction
| Loaded walking | Unloaded walking |
|---|
 |  |
- Metabolic cost reduction when active actuators are added to loaded gait model
- optimal: 10.35% reduction
- Saturated: 1.84% reduction
- New CMC: 2.68% reduction
- Metabolic cost reduction when active actuators are added to unloaded gait mode
- optimal: 10.62% reduction
- Saturated: 3.46% reduction
- 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 than to assist loaded walking.
Biarticular actuator
Now that we know both ankle actuator and hip actuator can reduce metabolic cost during loaded walking, the natural procedure is to test the actuators which can affect both ankle plantar flexor and hip extensor. In order to reduce the number of actuator, I added biarticular actuators of 1 DOF affecting ankle plantar flexion and hip extension to legs on both side, and see how much it reduces metabolic cost.
Modeling
The main idea to create biarticular actuator is to let the path actuator go through the axis of ankle joint rotation. I chose the attachment points of ankle actuator and hip actuator as the via points and end points of biarticular actuator line, and also set the origin of ankle joint rotation as one of the via points. By doing so, I create a biarticular actuator which combines ankle actuator and hip actuator.
Simulation result
I run CMC on loaded gait model with biarticular actuator. I set the maximum force of biarticular actuator to be 10,000N, in order to see the optimal input force and best metabolic cost reduction.
| 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 complex, which makes it hard to realize
- Biarticular actuator is not as effective as uni-articular actuators in terms of metabolic cost reduction.
Conclusion
- Three types of active actuators are evaluated in terms of metabolic cost reduction and controllability
- If we can apply sufficient amount of force, it is better to apply force to ankle joint.
- If not, hip actuator is a good alternative, even though it is hard to control
- Biarticular actuator doesn’t assist loaded walking very well and the force input is not consistent.
- Active actuators offer greater assist for loaded walking than unloaded walking when we can apply sufficient amount of force
- Optimal input of ankle actuator is consistent with gait cycle and muscle forces data, while that of hip actuator or biarticular 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
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 was only one gait cycle.
- More realistic actuator simulation is needed. (E.g. Combination of passive & active actuator).
Source code
You can find the simulation models that I created for the project in SimTK website. Please visit my project webpage in SimTK website- https://simtk.org/home/exosuit
