Robotic leg exoskeletons, wearable devices that act as amplifiers to enhance, reinforce or restore human performance, could improve the quality of life for individuals who have trouble walking by helping the user walk more normally. Because humans frequently change speeds while walking, it is important to understand how these speed changes occur and for the exoskeleton to provide appropriate assistance during both steady and variable speed walking. Thus, the objective of this CAREER project is to determine if the human goal, i.e., the human?s subconscious thoughts about the best way to walk, is the same for constant speed walking, changing speeds, and exoskeleton assisted walking for both healthy and post-stroke individuals. This will be accomplished by developing a physics-based predictive computer model of human walking that includes deriving a mathematical function that describes the subconscious goal of human walking. Studies are designed to provide fundamental insight into human gait control and to provide a tool to design optimal exoskeleton controllers. The integrated educational plan will use the appeal of exoskeletons to humanize engineering by producing professional videos highlighting how engineering can help improve impaired walking. An exoskeleton-based project will be developed to improve undergraduate engineers' ability to model a system, something with which many students struggle.

The principal investigator's overarching research goal is to study how people walk and to use that knowledge to improve rehabilitation techniques for individuals who have trouble walking. Towards this goal, this CAREER project will determine if the human goal is the same for constant speed walking, changing speeds, and exoskeleton assisted walking for both healthy and post-stroke individuals, which will provide fundamental insight into human gait control and a tool to design optimal exoskeleton controllers. Novel physics-based (sagittal six-link model with revolute hip, knee, and ankle joints connecting the thighs, shanks, and feet), predictive models will be developed that can account for the highly nonlinear and non-intuitive nature of human-device interaction and create controllers that correctly account for this interaction. The models developed will better predict human gait, including transition between speeds, and quantify how people change walking speed. Model development requires determination of an objective function that mathematically describes the subconscious goal of human walking. Initially, the objective will be based on the assumption that humans minimize energetic effort while walking. If minimum effort does not correlate well with chosen gaits, incorporating fall risk into the function will be considered. Participants in the studies include healthy young adults (30), healthy elderly adults (12), slow elderly adults (12), and post-stroke elderly adults (12) who walk with difficulty but are able to walk without an assistive device and can follow directions. The Research Plan is organized under four tasks: 1) Quantify the spatial-temporal (step length, duration and speed) and kinematic properties of speed transitions for young, elderly, and post-stroke adults for the first time, and generate human-like speed transitions for the model by combining several methods from robotic control in a novel manner; 2) Determine if the same objective function can predict healthy human joint kinematics for steady, variable speed, and exoskeleton-assisted walking, providing novel insights into how humans react to exoskeleton assistance; 3) Determine how advanced age and stroke alter the objective function used to predict walking, providing novel insights into how age and stroke affect walking priorities and 4) Create a method to design exoskeleton controllers in simulation that accounts for the nonlinear human-device interactions and produces the desired human gait without additional tuning.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Start
Project End
Budget Start
2020-06-01
Budget End
2025-05-31
Support Year
Fiscal Year
2019
Total Cost
$397,040
Indirect Cost
Name
Pennsylvania State University
Department
Type
DUNS #
City
University Park
State
PA
Country
United States
Zip Code
16802