Simulating assistive technology : insights, tools, and open science

From getting to work to strolling through the park, our mobility is an essential part of life. Losing one's mobility can be devastating. Scientists are on the verge of enhancing mobility for many movement disorders via exoskeletons. However, designing effective exoskeletons is challenging because of their tight coupling with the complex human body. Computer simulations of exoskeletons can reduce the duration of lengthy human experiments and reveal the effect of an exoskeleton on muscle coordination. A promising application for exoskeletons is reducing the burden of carrying heavy loads on the torso, which is a requirement of many occupations. To guide the design of such exoskeletons, my lab performed an experiment with seven male subjects walking while carrying 88 pounds on their torso. I used these data to simulate the effect of seven hypothetical idealized devices, each providing unrestricted torque at one joint in one direction (hip abduction, hip flexion, hip extension, knee flexion, knee extension, ankle plantarflexion, or ankle dorsiflexion). My simulations predicted that a device assisting with hip abduction would be most efficient at reducing the energy required to walk while carrying heavy loads. I found that many of our devices affected muscles that were not directly assisted. This result supported the notion that exoskeletons can have complex effects that are difficult to discover via experiments, or via simulations that do not include muscles. Although my simulations yielded valuable insights, I discovered that the method I employed limited the accuracy of my predictions. The method, named Computed Muscle Control, can optimize device torques and predict changes in muscle coordination but cannot predict changes to the walking motion itself. Musculoskeletal simulation tools usually model the nervous system via objectives we believe the brain minimizes. Even though individuals might employ different objectives for different motions, the nervous system objective that Computed Muscle Control employs cannot be modified. Lastly, Computed Muscle Control cannot optimize the values of constant model parameters, such as the stiffness of an assistive device. To address the limitations of Computed Muscle Control and related simulation tools, I created a flexible framework for optimizing the motion and control of musculoskeletal models. This framework, named Moco, employs the direct collocation method, which has become a popular approach for solving related problems within and beyond the field of biomechanics. Compared to other simulation tools, Moco provides an unprecedented amount of flexibility. Researchers can choose a nervous system objective from an existing library of modules. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which are common in musculoskeletal models. In collaboration with a labmate, I used Moco to design a passive device to assist with a squat-to-stand motion. We predicted the stiffness of the device and a new squat-to-stand motion without relying on motion data; such predictions were challenging to conduct with previous simulation tools. Moco will accelerate the use of simulations to predict the effect of exoskeletons, orthopedic surgeries, artificial joints, and other interventions that restore and enhance mobility

Keywords: Mechanical Engineering.
Advisor: Delp Scott 
Collins Steve (Steven Hartley) 
Pavone Marco