Perturbation-based estimation of within-stride cycle metabolic cost.

Metabolic cost greatly impacts trade-offs within a variety of human movements. Standard respiratory measurements only obtain the mean cost of a movement cycle, preventing understanding of the contributions of different phases in, for example, walking. We present a method that estimates the within-stride cost of walking by leveraging measurements under different force perturbations.

Optimizing exoskeleton assistance to improve walking speed and energy economy for older adults.

Background: Walking speed and energy economy tend to decline with age. Lower-limb exoskeletons have demonstrated potential to improve either measure, but primarily in studies conducted on healthy younger adults. Promising techniques like optimization of exoskeleton assistance have yet to be tested with older populations, while speed and energy consumption have yet to be simultaneously optimized for any population.

Optimized hip-knee-ankle exoskeleton assistance reduces the metabolic cost of walking with worn loads.

Background: Load carriage is common in a wide range of professions, but prolonged load carriage is associated with increased fatigue and overuse injuries. Exoskeletons could improve the quality of life of these professionals by reducing metabolic cost to combat fatigue and reducing muscle activity to prevent injuries. Current exoskeletons have reduced the metabolic cost of loaded walking by up to 22% relative to walking in the device with no assistance when assisting one or two joints.

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation.

Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data.

Optimizing Exoskeleton Assistance for Faster Self-Selected Walking.

Self-selected walking speed is an important aspect of mobility. Exoskeletons can increase walking speed, but the mechanisms behind these changes and the upper limits on performance are unknown. Human-in-the-loop optimization is a technique for identifying exoskeleton characteristics that maximize the benefits of assistance, which has been critical to achieving large improvements in energy economy.

Using force data to self-pace an instrumented treadmill and measure self-selected walking speed.

Background: Self-selected speed is an important functional index of walking. A self-pacing controller that reliably matches walking speed without additional hardware can be useful for measuring self-selected speed in a treadmill-based laboratory.

Methods: We adapted a previously proposed self-pacing controller for force-instrumented treadmills and validated its use for measuring self-selected speeds.

Predictive neuromechanical simulations indicate why walking performance declines with ageing.

Key Points: Although the natural decline in walking performance with ageing affects the quality of life of a growing elderly population, its physiological origins remain unknown. By using predictive neuromechanical simulations of human walking with age-related neuro-musculo-skeletal changes, we find evidence that the loss of muscle strength and muscle contraction speed dominantly contribute to the reduced walking economy and speed. The findings imply that focusing on recovering these muscular changes may be the only effective way to improve performance in elderly walking.

Evaluation of a Neuromechanical Walking Control Model Using Disturbance Experiments.

Neuromechanical simulations have been used to study the spinal control of human locomotion which involves complex mechanical dynamics. So far, most neuromechanical simulation studies have focused on demonstrating the capability of a proposed control model in generating normal walking. As many of these models with competing control hypotheses can generate human-like normal walking behaviors, a more in-depth evaluation is required.

Generalization of a muscle-reflex control model to 3D walking.

The neural controller that generates human locomotion can currently not be measured directly, and researchers often resort to forward dynamic simulations of the human neuromuscular system to propose and test different controller architectures. However, most of these models are restricted to locomotion in the sagittal plane, which limits the ability to study and compare proposed neural controls for 3D-related motions. Here we generalize a previously identified reflex control model for sagittal plane walking to 3D locomotion.

Integration of an adaptive swing control into a neuromuscular human walking model.

Understanding the neuromuscular control underlying human locomotion has the potential to deliver practical controllers for humanoid and prosthetic robots. However, neurocontrollers developed in forward dynamic simulations are seldom applied as practical controllers due to their lack of robustness and adaptability. A key element for robust and adaptive locomotion is swing leg placement.

The effect of foot compliance encoded in the windlass mechanism on the energetics of human walking.

The human foot, which is the part of the body that interacts with the environment during locomotion, consists of rich biomechanical design. One of the unique designs of human feet is the windlass mechanism. In a previous simulation study, we found that the windlass mechanism seems to improve the energy efficiency of walking.