This project is motivated by DARPA Warrior Web Program.
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.
As it is a new approach to assist human gait with deformable structure, there are many challenges when developing the exosuit. The examples of the challenges are
The reasons for the challenges are
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
Two different data were collected from a same subject.
For both data, walking speed is identical, and mass of the load for loaded walking was 38kg.
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Loaded gait model
Now, let’s take a look at my project results. In this slide, different types of loaded gait models are shown. These models are based on the generic gait model in Opensim, and they were adjusted through RRA algorithm in order to be dynamically consistent with experimental data. After RRA had been done, I added different types of actuators to different models. One on the left is a loaded walking model without any actuator, and the one in the middle has ankle actuators on both ankles, and the one on the right has hip actuators around hip flexion DOF. You can see cable driven actuators which are represented by blue lines on the bottom. With these models, I first compared the effectiveness of ankle actuator and hip actuator in loaded gait scenario.
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Unloaded gait model
Same procedure has been done to unloaded walking data and models. All of these models represent unloaded walking model by empty backpack.
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
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
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When there is active actuators on OpenSim Model, the activation term in cost function becomes
x = \begin{bmatrix} x_{muscle} \\ x_{actuator} \end{bmatrix} |
and,
F_{actuator} = F_{actuator}^{max} \times x_{actuator} |
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.
| Loaded walking | Unloaded walking |
|---|---|
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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.
| 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.
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.
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 |
|---|---|
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Discussion
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.
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.
You can find the model that I used in htt~~~~