From getting to work to strolling through the park, our mobility is an essential part of life. Losing one’s mobility can be devastating. Injuring the spinal cord can paralyze one or both legs, strokes can degrade motor coordination, and the elderly suffer from an increased risk of falling. Scientists are on the verge of enhancing mobility for these populations via exoskeletons. Once relegated to science fiction, exoskeletons are now sold commercially to improve mobility and assist with manual labor. However, designing effective exoskeletons is challenging because exoskeletons are tightly coupled with the complex human body. Moreover, an exoskeleton designed for one person may perform poorly for others. Recent advances in experimental techniques for designing exoskeletons have led to huge leaps in progress. However, these techniques require extensive time when applied to complex exoskeletons. Computer simulations are a complementary approach that can reduce the duration of 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 (lifting the leg sideways) would be most efficient at reducing the energy required to walk while carrying heavy loads. I found that many of our devices affected muscles elsewhere in the limb;​ 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, could optimize device torques and predict changes in muscle coordination but could not predict changes to the walking motion itself. The motion was fixed to that which we recorded experimentally, without an exoskeleton. Musculoskeletal simulation tools usually model the nervous system via objectives we believe the brain minimizes. When simulating walking, researchers often assume the primary objective is the energy required to walk. 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 mass of a body segment or 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 choose a nervous system objective from a library of existing modules. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which are common in musculoskeletal models. To maximize the impact of Moco, the tool is open-source and freely available.

In collaboration with a labmate, I used Moco to design a passive device to assist with a squat-to-stand motion. We predicted both the stiffness of the device and a new squat-to-stand motion without relying on motion data, neither of which are possible with Computed Muscle Control. Moco opens the door to techniques for simulation-based design of assistive devices that go far beyond what is possible with Computed Muscle Control, thereby accelerating the use of simulations to restore and enhance mobility with exoskeletons, orthopedic surgeries, artificial joints, and other interventions.