Simulation-based soft exosuit design
Team Members
Nick Bianco
Rachel Troutman
Project Summary
GitHub Repository
https://github.com/nickbianco/soft-exosuit-design
Motivation
Recent developments in the design of soft exosuits have been shown to effectively assist human locomotion. A variety of joint configurations (ankle + hip, Quinlivan et al 2017; hip, Ding et al 2016; etc) and assistive strategies have reduced the metabolic cost of walking by 5 to 20 percent. However, the human-device interaction across the design space has yet to be fully characterized, given time and cost limitations in the laboratory setting. Computational modeling and simulation approaches mitigate many of these limitations, and thus there exists an opportunity to improve the efficacy of device configurations and assistive strategies prior to experimental implementation. Using a pre-existing musculoskeletal model and example data used for human gait, our goal is to simulate a current exosuit design (Harvard Biodesign Lab) and optimize the device configuration and assistive strategy across the hip and ankle.
Research Questions
Can an OpenSim model reproduce experimental observed metabolic cost reductions in Quinlivan et al. 2017?
Is the linear in metabolic cost reduction valid beyond peak assistive forces in Quinlivan et al. 2017?
What is the optimal joint torque distribution for a given exosuit configuration?
Now, reintroduce geometry. Can we find similar trends in metabolic cost reduction for lower device powers through optimization of exosuit configuration?
How does device mass affect the results?
Study Approach
Approach #1: implement ideal force actuators at ankle and hip
Problem #1 (simulate Quinlivan study)
Minimize: activations squared (possibly metabolic cost)
Constraints:
Track model kinematics, muscle parameter trajectories, joint torques
Bounds on excitations
Prescribe: biologically inspired device control at 4 assistance levels
Solve for: muscle activations, metabolic cost
Problem #2 (optimize exosuit moment arms)
Problem #3 (optimize exosuit moment arms + device control)
[Optional, if time allows] Approach #2 : model device as Path Actuator
Problem #4 (redo Problem #1 and compare)
Problem #5 (solve for passive properties of device)
Problem #6 (solve for passive properties of device + introduce device mass)
Implementation
Use DeGroote muscle redundancy solver MATLAB framework
Compute metabolic cost using Umberger model
Plot trajectories of muscle activations, fiber lengths/velocities, tendon lengths
Tasks
Methods
Our general optimization approach was solving a muscle redundancy problem using the Gait2354 OpenSim default model and associated data. The problem formulation is based on the direct collocation optimal control framework for solving the muscle redundancy published by Friedl De Groote.
A cost function was minimized subject to a set of constraints including tracking the joint moments calculated from inverse dynamics. The joint moments are made up of contributions from the muscles, reserve actuators, and assistive moments from the exosuit. Depending on the problem being solved, the assistive torques of the device were either prescribed or solved for as part of the optimization.
Quinlivan 2017
In this first problem, the goal was to simulate the experiments done with the soft exosuit in a paper by Quinlivan et al. We wanted to see if we could recreate their metabolic cost savings and predict what would happen above the maximum condition they tested. The cost function for this problem was a sum of excitations and activations of the muscles used to create the desired walking motion. For the first four conditions (blue curves), the exosuit moments were pulled directly from the paper. The next six conditions are scaled versions of these curves. The applied moments are shown below as a function of time of the stance phase. The associated change in metabolic rate for each condition is shown on the right. The simulation results are shown as colored dots and the experimental results from the paper are shown as bars.
The same general trend of decreasing metabolic cost as peak assistive force increases is seen in both the simulation and experimental results. The simulations results however, underestimate the metabolic savings at the higher force levels. All metabolic costs were calculated using the Umberger metabolics model. The simulations suggest that there is some opportunity to further decrease metabolic cost with with device by increasing the assistive force above the maximum experimental trial, but the linear trend seen in the paper doesn't continue indefinitely. A bowl shape curve was found for metabolic savings over the 10 force levels tested.
The initial decrease in metabolic cost can be seen because of muscles such as the medial gastrocnemius and illiacus which are assisted by the device. Their activations go down as the assistive force from the exosuit goes up. Other muscles however, such as the tibialis anterior, are fighting against the device so their activation goes up with assistance level increasing metabolic cost.
Exosuit Joint Tradeoff
The next question we investigated is if we are given a single actuator, how should we allocate assistive moments across the exosuit to cause the greatest reduction in metabolic cost? We introduced a tradeoff parameter, alpha, to allow us to weight the assistance between multiple joints.
Hip and Ankle
The first case we investigated is similar to the Quinlivan study in that we only assist at the ankle and hip. In this case, positive values of alpha means the hip flexion moment would be assisted more than the ankle plantar-flexion moment and vice-versa for negative values of alpha. In this problem we are optimizing for alpha and the exosuit activation over a range of maximum actuator forces.