- Julia Butterfield
- Patrick Franks
Video due and should be uploaded to your page by 6/5. See previous course pages for example videos.
Videos should be less than 7 minutes.
Recently, powered exoskeletons have been used successfully to reduce the metabolic cost of walking in both healthy (Zhang et al 2017, Quinlivan et al 2017, Ding et al 2018) and clinical populations (Awad et al 2017). The devices work by supplying torques to augment and offload biological joint moments, thereby reducing the necessary muscle force contributions. Several major challenges remain for the field of powered exoskeleton assistance, and simulation could help answer some of these questions.
Firstly , finding the ideal assistance pattern, both timing and magnitude of torques, is incredibly challenging. Some groups hand-tune most device parameters, for example in Awad et al 2017, they choose a control law for all subjects and only vary the timing. Others employ a human-in-the-loop optimization strategy that tests different device control laws in real-time and adjusts the parameters based on the subject’s metabolic response (Zhang et al 2017, Ding et all 2018). For both hand-tuned devices and human-in-the-loop optimization, choosing a good parameter set to vary as well as good starting points for tuning is incredibly difficult. Simulation could be used to test more possibilities than is possible in experimentation and narrow the parameter space. Although a simulation will never exactly match a human’s response, the simulation results could help experimenters determine seed values for experimental human-in-the-loop optimization.
Secondly, although the interactions between assistance at multiple joints has been explored in the context of running (Uchida et al, 2016), very little work has been done in simulation of unconstrained multi-joint assistance during walking. As researchers seek to further decrease the energy cost of walking beyond that which can be accomplished through assistance at a single joint, the interactions between joints becomes crucial. It may be that the benefit of assistance at multiple joints is less than the sum of its individual components. Assistance at a joint may turn out to be unnecessary, or at least not worth the added weight of another actuator for that joint. Alternatively, perfectly timed assistive moments at multiple joints could have benefits well beyond individual joint assistance. The problem of finding good parameter spaces and initial guesses becomes exponentially more difficult as multiple joints are considered, and simulation again offers a way to explore more possibilities than feasible in experimentation.
The purpose of our project was to explore the simulation of individual joint assistance and multi-joint assistance, with an overall goal of designing the seed values and joint torque profiles for a human-in-the-loop optimization experiments with a bilateral ankle-knee-hip exoskeleton.
- How can bilateral hip, knee, and ankle assistive torques be optimized to reduce the metabolic cost of walking?
- How do the optimal assistive torque profiles at each joint during single joint assistance compare to the optimal assistive torque profiles when all three joints are optimized simultaneously?
- (If time allows) How do the control laws found from simulation for bilateral hip-knee-ankle assistive torques perform in physical experimentation?
To be cleaned up, just jotting down notes for now.
To answer question 1:
Model used is basic gait 10dof18 and the basic included walking kinematics that come with the OpenSim download. Then, we add ideal torque actuators at each joint capable of producing torque in either direction. See what the results are for what the assistive joint torques are that minimizes the metabolic cost (use the Umberger metabolic cost model). Then, add mass to the osim model in the places that BiLLEE adds mass already, and simulate again to get the assistive torques that minimize metabolic cost. Then, using the original model (without BiLLEE mass added in), change the set-up to say that the amount of mass added to the model for each actuator is proportional to the maximum torque that is produced by that actuator at any point during the gait cycle. So you have to simulate and optimize to find both the device masses and the joint torques they produce. Then, try something where you maybe say that any torque between 0 and 25 Nm results in an added mass of 1 kg, and then it increases linearly from there. You want to make sure that you are not sand-bagging against the design.
To answer question 2:
Model used is basic gait 10dof18 and the basic included walking kinematics that come with the OpenSim download. Start by adding just one ideal ankle actuator to each leg. Simulate and optimize to find the ideal assistance profile for the ankle joint that will minimize the metabolic cost of walking. Remove the ankle actuators and add knee actuators. Find the ideal knee assistance profile. Remove the ankle actuators and add hip actuators. Find the ideal hip assistance profile. Then, compare these individual profiles with the results from answering question 1 where you simulated adding all the joint actuators and saw the ideal assistance patterns. Just look at how the profiles compare, and see if that gives you insight into how joint coupling affects the results. If desired, also try joint pairings, like just knee-ankle, just knee-hip, or just hip-ankle.
To answer question 3:
Take the results from one of the question 1 optimizations and apply those torque patterns in real time to the Bilateral Lower limb Exoskeleton emulator in the Stanford Biomechatronics Lab. See how the metabolic cost of the participant changes from walking while wearing the device in the zero-torque mode to walking while wearing the device with the torque profiles as found in simulated optimization.
- Choose model and experimental kinematics data set
- Question #1:
- Add ideal torque actuators to each joint capable of producing extension and flexion torques
- Optimize to find muscle activations and assistance torque profiles that a) minimize metabolic cost of walking, b) match the experimental kinematics set, and c) respect constraints on maximum muscle force, etc.
- Added metabolic cost probe to model
This can include:
- Model(s) used
- Simulation strategy
- Computational tools
- Experimental procedures/data
- Flow chart of methods
This will vary based on your project.
It should include a step-by-step guide such that anyone using your page can understand and complete. The purpose of the confluence page is to share your work with others so that we can build off of each other's research, so making your methods clear is very important.
You can also insert code (with approval from project mentors) and other helpful links.
To be completed by 6/5 (but you should have a plan by 5/8).
This can include:
- Videos of simulations
- Graphs of results
- Tables of results
- Other figures
- Relevant equations
Make sure to point out how this addresses your research questions.
To be completed by 6/5.
- What were some challenges you faced?
- Did you address all the research questions you aimed to?
- What future studies could be done to address these challenges?
- Based on your results, what are the next questions to be studied?
- How does this advance the field of biomechanics on a larger scale?
To be completed by 6/5.
You should acknowledge any help you received on your project. Collaboration is always encouraged but must be acknowledged.
Awad, L. N., Bae, J., O’Donnell, K., De Rossi, S. M. M., Hendron, K., Sloot, L. H., … Walsh, C. J. (2017). A soft robotic exosuit improves walking in patients after stroke. Science Translational Medicine, 9(400). https://doi.org/10.1126/scitranslmed.aai9084
Ding, Y., Kim, M., Kuindersma, S., Walsh, C.J., 2018. Human-in-the-loop optimization of hip assistance with a soft exosuit during walking. Science Robotics 3. doi:10.1126/scirobotics.aar5438
Quinlivan, B. T., Lee, S., Malcolm, P., Rossi, D. M., Grimmer, M., Siviy, C., … Walsh, C. J. (2017). Assistance magnitude versus metabolic cost reductions for a tethered multiarticular soft exosuit. Science Robotics, 2(2), eaah4416. https://doi.org/10.1126/scirobotics.aah4416
Uchida, T.K., Seth, A., Pouya, S., Dembia, C.L., Hicks, J.L., Delp, S.L., 2016. Simulating Ideal Assistive Devices to Reduce the Metabolic Cost of Running. Plos One 11. doi:10.1371/journal.pone.0163417