Team Member
- Jaehyun Bae
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Motivations
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Motivation
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 burdencarry, 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.
- You can see the details of this project 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 hinder the wearer’s free movement, researchers are trying to make it soft and deformable, but still capable of applying force to body joints. The Harvard exosuit is the an example of a new approach to create creating an under-suit in a soft and deformable manner.
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- Experimental data proved that it can help loaded walking by reducing metabolic costscost.
- This suit applies force to lower limb joints through cables driven by actuators on in the backpack.
Challenges
As the Exosuit tries to assist human gait with a 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 changes in design parameters.
The reasons for the challenges are:
- The suit is deformable and closely attached to a the body.
- We can not cannot predict how external actuation assists muscles during loaded walking, as the human body is highly redundant.
- Experimental data is inconsistent case-by-case.
Goals
- Evaluate the effectiveness of wearing active actuators on metabolic cost reduction.
- Explain how Exosuit the exosuit can help loaded gait.
- Verify the impact of changes of in design parameters.
- Find optimal control inputs for active actuators.
Strategy
Experimental data
- Two different sets of data were collected from a the same subject. :
- One gait cycle of loaded walking (From from left toe-off to next left toe-off).
- One gait cycle of unloaded walking (From again, from left toe-off to next left toe-off).
- The subject didn't wear a suit and walked freely.
- walking Walking speeds were identical for both cases.
- Mass of the load was 38kg38 kg.
Modeling
To simulate the movement of the exosuit wearer, the first thing to do is to create a model which can replicate a real subject as realistically as possible as we can. Before I created the simulation model with active actuators on it, I had used the generic gait model to go through the basic steps of modeling procedure in the OpenSim simulation pipeline. By doing so, I could make my model dynamically consistent to the experimental data. And I then , I added actuators and metabolic cost probes to the model.
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Modeling a subject wearing an active actuator
The diagram above describes what the procedure used to simulate the generic gait model had gone through.
- The first three steps are comprise the basic modeling procedure in Opensim OpenSim to make a model dynamically and kinematically consistent to with the experimental data. For more information, readsee the following pages on Confluence:
- After step 3 is was complete, probes for calculating metabolic cost were added to the model. . For more information about how to add the probes, readsee Simulation-Based Design to Reduce Metabolic Cost.
- Finally, I added active actuators to the model. Here, I used the PathActuator class to simulate the active actuators of the exosuit, as they are cable-driven actuatoractuators. If you are interested in how the PathActuator works, readsee OpenSIm::PathActuator Class.
Sample models
Here are the figures of sample simulation models. I created modified the RRA-adjusted model to create several different types of models by modifying RRA-adjusted model to compare them for their evaluation.
Loaded gait model
Three different for comparison.
Loaded gait models
Three types of models were created for loaded gait simulationsimulations:
- A model without actuatorany additional actuators
- A model with path actuators supporting plantar flexionplantarflexion
- A model with path actuators supporting hip extension
The path actuator supporting plantar flexion plantarflexion is attached to the heel and tibia, and the path actuator supporting hip extension is attached to the backpack and femur. For Simplicitysimplicity, Loaded the loaded mass was added directly to the torso.
Unloaded gait
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models
The same types of models were created for unloaded gait simulationsimulations.
Optimization methodology
The idea to optimize the control input force for the actuators is to take advantage of the Computed Muscle Control (CMC) tool. The main reason we use CMC in OpenSim is to find a the most suitable excitations for muscles to create body movement while minimizing muscle activations. To see how it works, readsee How CMC Works.
In this project, I make different use of the optimization process in CMC in order to optimize the control input force for active actuators.
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- When we add active actuators on to an OpenSim Modelmodel, the activation term in the cost function becomes
Where 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 the 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 resulting from CMC if we diminish the influence of Xactuator to on objective function J is the optimal actuator input for metabolic cost reduction.
- Muscle force is constructed from the equation Factuator = Factuatormax * Xactuator. if If we assign a large value of maximum force to each actuator, the actuator control Xactuator decreases, so the influence of the actuator to on J is decreasesdecreased.
- Using this idea, I could find found an optimal input for each actuator, and also found the metabolic cost reduction after running CMC with a model where active actuators are were added.
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Results and Discussion
Metabolic cost change when active actuators are added to the 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 simulated both loaded and unloaded walking cases, and I compared the influence of the hip actuator and ankle actuator to actuators on metabolic cost reduction. I assigned used 10,000N to 000 N as the maximum active actuator force (Factuatormax) for this simulationthese simulations.
| Loaded walking | Unloaded walking | ||||||||
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- Metabolic cost reduction when active actuators are added to the loaded gait model:
- Ankle actuator: 10.35%
- Hip actuator: 6.62%
- Metabolic cost reduction when active actuators are added to the unloaded gait model:
- Ankle actuator: 10.62%
- Hip actuator: 1.04%
- Things to notice:
- The metabolic cost is much lower during unloaded walking than loaded walking. Loaded Unloaded walking costs only 75% of the metabolic energy spent during loaded walking.
- Ankle The ankle actuator works better to reduce at reducing metabolic cost than the hip actuator when we can apply the optimal input force.
- Hip The hip actuator is not assistive to during unloaded gait.
Therefore, we can say that the ankle actuator helps reduce metabolic cost reduction better than the hip actuator if we have ideal actuators which has with no maximum force limitation.
Optimal actuator input force
| Loaded walking | Unloaded walking | |
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| Ankle actuator |   |   |
| Hip actuator |   |   |
Optimal input force for Ankle the ankle actuator:
- Actuation is started begins right after the toe-off of a the foot on the opposite side, and the peak force happens between the heal-strike of a the 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 The same actuation strategy is valid for both loaded gait and unloaded gait.
- The force signal is clear and easy to implement in the real world.
- However, the maximum actuation force is about 2500 N, which is too high to achieve in reality.
Optimal input force for Hip the hip actuator:
- Hip The hip actuator can reduce the metabolic cost with lower maximum force than the ankle actuator.
- However, it is hard to identify how the actuator assists walking.
- Also, it is difficult to implement the optimal control input for the hip actuator in the real world .
How the ankle actuator assists loaded gait
I could We can explain how the optimal actuation input for the ankle actuator helps loaded gait by investigating the change of plantar flexor plantarflexor muscle forces.
- The gastrocnemius muscle forces are barely changedchange.
- Other plantarflexor muscle forces, including Soleus soleus muscle forces, are significantly decreased.
- If we draw compare the active actuator input force with the sum of the baseline uniarticular forces and active actuator input force together, we can see that the active actuator force follows the sum of the baseline uniarticular muscle forces.
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Optimal input force for ankle actuator
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when actuation force is limited to
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400 N (i.e., realistic actuation limit)
- From the previous results, we found that the optimal input force for the hip actuator is small enough to be achieved by a real actuator, while the optimal input force for ankle actuator is not realistic . (>2000N> 2000 N). Therefore, I tried different types of input forces for ankle actuators up to 400N400 N, and compare compared the results to the optimal control input case.
- My initial guess was to saturate the optimal input force that I found earlier at 400N400 N. I generated an new input force which is identical to the optimal input force up to 400N400 N, and saturated once the optimal input force exceeds 400Nexceeded 400 N.
- The second input force I tried is was a new result from different my CMC proceduresimulations. The new CMC result was acquired after by assigning 4000N 4000 N to the maximum actuation force and bounding the control input between 0 and 0.1. In other
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- words,
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According
According to the formula Factuator = Factuatormax * Xactuator, the new CMC
resultsresult also has a maximum force of
400N400 N. As Xactuator is bounded between 0 and 0.1 and X
muscle hasmuscle is chosen between 0 and 1, the influence of Xactuator to the objective function of the CMC procedure is relatively lower than that of X
musclemuscle, so we can use this idea to create an optimal input for
activethe ankle actuator when the maximum actuation force is limited.
When we compare the saturated optimal input and the result from the 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.
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| Loaded walking | Unloaded walking |
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- Metabolic cost reduction when active actuators are added to loaded gait model:
- optimalOptimal: 10.35% reduction
- Saturated: 1.84% reduction
- New CMC: 2.68% reduction
- Metabolic cost reduction when active actuators are added to unloaded gait modemodel:
- optimalOptimal: 10.62% reduction
- Saturated: 3.46% reduction
- New CMC: 3.82% reduction
- The result from the 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 the unloaded walking case than the loaded walking case. It makes sense because we requires require lower force to assist unloaded walking than to assist loaded walking.
Biarticular actuator
Now that we know both the ankle actuator and the hip actuator can reduce metabolic cost during loaded walking, the a natural procedure progression is to test the actuators which can affect both ankle plantar flexor plantarflexion and hip extensorextension. In order to To reduce the number of actuatoractuators, I added single-degree-of-freedom biarticular actuators of 1 DOF affecting ankle plantar flexion affecting ankle plantarflexion and hip extension to legs on both sidesides, and see how much it reduces investigate the metabolic cost.
Modeling
The main idea to create in creating a biarticular actuator is to let the path actuator go through the axis of ankle joint rotation. I chose the attachment points of the ankle actuator and hip actuator actuators as the via points and end points of the biarticular actuator line, and also set the origin of the ankle joint rotation as one of the via points. By doing so, I create created a biarticular actuator which combines the effects of the ankle actuator and hip actuatoractuators.
Simulation result
I run ran CMC on the loaded gait model with the biarticular actuator. I set the maximum force of the biarticular actuator to be 10,000N , in order to see the optimal input force and the best possible metabolic cost reduction.
| Optimal input | Metabolic cost reduction |
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- 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 the reduction observed using either the ankle or hip actuator.
- Control input is complex, which makes it hard to realize.
- Biarticular The biarticular actuator is not as effective as uni-articular the uniarticular actuators in terms of metabolic cost reduction.
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Conclusions
- Three types of active actuators are evaluated in terms of metabolic cost reduction and controllability:
- If we can apply a sufficient amount of force, it is better to apply force to the ankle joint.
- If not, the hip actuator is a good alternative, even though it is hard difficult to control.
- Biarticular The 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 a sufficient amount of force.
- Optimal The optimal input of for the ankle actuator is consistent with gait cycle and muscle forces force data, while that of hip actuator or biarticular actuator is notin contrast to the optimal input for the hip and biarticular actuators.
- The optimal input force for the ankle actuator when it’s its maximum force is bounded is similar to the general optimal input force when it is saturated at the maximum force.
Limitations
- The experimental data was obtained from a subject without an exosuit. Exosuit The exosuit may change the kinematics of a subject as well as GRFthe ground-reaction forces.
- The simulation methodology to use CMC as an optimization tool works, but more improvement is needed.
- The CMC process doesn’t minimize metabolic cost. Instead, it minimizes the 2-norm of activation.
- The experimental data was involved only one gait cycle.
- More realistic actuator simulation is simulations are needed . (Ee.g. Combination , a combination of passive & and active actuatoractuators).
Source
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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/exosuiton my Simtk project page.
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