R21EB037268
Project Grant
Overview
Grant Description
AI-driven hip exoskeleton control framework that rapidly generalizes to a broad range of users and real-world locomotor tasks - Abstract
In order for wearable robotic exoskeletons to assist the American public throughout daily life, researchers need to develop a control framework that satisfies real-world use.
Over 10% of adults have difficulty walking, which hinders their ability to perform daily activities, maintain independence, and have a satisfactory quality of life.
To address this issue, wearable exoskeletons have the potential to augment the walking ability of a diverse array of community members throughout their daily lives.
That is, if researchers can establish an exoskeleton control framework that is 1) easy to use and 2) adequately assists the walking needs of users throughout daily life.
However, current exoskeleton controllers can only assist a few stereotypical movements or require hours of arduous expert tuning using specialized equipment.
Thus, there is a critical need for an exoskeleton control framework that rapidly and easily tunes to the diversity of user movement patterns during real-world ambulation.
Until such exoskeleton controls exist, many community members, especially those with distinct movement patterns and limited resources, will continue to lack the mobility to achieve independent community ambulation.
Our long-term goal is to develop an exoskeleton control framework that is easy to use and can quickly tune to any user and effectively assist their daily ambulation.
Here, we will progress towards our goal by developing and evaluating a hip exoskeleton control framework that leverages artificial intelligence to rapidly tune to both young and older adult movement patterns in minutes.
We expect such exoskeleton tuning to improve user ability to navigate an outdoor course with hills, stairs, and turns better than current ‘state-of-the-art’ exoskeleton controllers.
Further, we will mechanistically explain how each exoskeleton control framework affects user walking performance by pairing the outdoor testing with indoor lab tests that involve detailed physiological measurements.
The first aim of this research focuses on developing ‘Trailblazer Exoskeleton Control’ - a versatile hip exoskeleton control framework that leverages artificial intelligence to interpret the movements of new users in minutes via robot integrated wearable sensors, thereby enabling a non-specific and task-agnostic control strategy.
The second aim’s objective is to evaluate the ability of young and older adults using Trailblazer Exoskeleton Control and three alternative conditions to navigate an outdoor walking course.
Our engineering innovation using artificial intelligence to develop a new and easy to use exoskeleton control framework will set the stage of wearable robotic exoskeletons to assist the movement patterns of community members across the lifespan.
In order for wearable robotic exoskeletons to assist the American public throughout daily life, researchers need to develop a control framework that satisfies real-world use.
Over 10% of adults have difficulty walking, which hinders their ability to perform daily activities, maintain independence, and have a satisfactory quality of life.
To address this issue, wearable exoskeletons have the potential to augment the walking ability of a diverse array of community members throughout their daily lives.
That is, if researchers can establish an exoskeleton control framework that is 1) easy to use and 2) adequately assists the walking needs of users throughout daily life.
However, current exoskeleton controllers can only assist a few stereotypical movements or require hours of arduous expert tuning using specialized equipment.
Thus, there is a critical need for an exoskeleton control framework that rapidly and easily tunes to the diversity of user movement patterns during real-world ambulation.
Until such exoskeleton controls exist, many community members, especially those with distinct movement patterns and limited resources, will continue to lack the mobility to achieve independent community ambulation.
Our long-term goal is to develop an exoskeleton control framework that is easy to use and can quickly tune to any user and effectively assist their daily ambulation.
Here, we will progress towards our goal by developing and evaluating a hip exoskeleton control framework that leverages artificial intelligence to rapidly tune to both young and older adult movement patterns in minutes.
We expect such exoskeleton tuning to improve user ability to navigate an outdoor course with hills, stairs, and turns better than current ‘state-of-the-art’ exoskeleton controllers.
Further, we will mechanistically explain how each exoskeleton control framework affects user walking performance by pairing the outdoor testing with indoor lab tests that involve detailed physiological measurements.
The first aim of this research focuses on developing ‘Trailblazer Exoskeleton Control’ - a versatile hip exoskeleton control framework that leverages artificial intelligence to interpret the movements of new users in minutes via robot integrated wearable sensors, thereby enabling a non-specific and task-agnostic control strategy.
The second aim’s objective is to evaluate the ability of young and older adults using Trailblazer Exoskeleton Control and three alternative conditions to navigate an outdoor walking course.
Our engineering innovation using artificial intelligence to develop a new and easy to use exoskeleton control framework will set the stage of wearable robotic exoskeletons to assist the movement patterns of community members across the lifespan.
Awardee
Funding Goals
TO SUPPORT HYPOTHESIS-, DESIGN-, TECHNOLOGY-, OR DEVICE-DRIVEN RESEARCH RELATED TO THE DISCOVERY, DESIGN, DEVELOPMENT, VALIDATION, AND APPLICATION OF TECHNOLOGIES FOR BIOMEDICAL IMAGING AND BIOENGINEERING. THE PROGRAM INCLUDES BIOMATERIALS (BIOMIMETICS, BIOPROCESSING, ORGANOGENESIS, REHABILITATION, TISSUE ENGINEERING, IMPLANT SCIENCE, MATERIAL SCIENCE, INTERFACE SCIENCE, PHYSICS AND STRESS ENGINEERING, TECHNOLOGY ASSESSMENT OF MATERIALS/DEVICES), BIOSENSORS/BIOTRANSDUCERS (TECHNOLOGY DEVELOPMENT, TECHNOLOGY ASSESSMENT, DEVELOPMENT OF ALGORITHMS, TELEMETRY), NANOTECHNOLOGY (NANOSCIENCE, BIOMIMETICS, DRUG DELIVERY SYSTEMS, DRUG BIOAVAILABILITY, MICROARRAY/COMBINATORIAL TECHNOLOGY, GENETIC ENGINEERING, COMPUTER SCIENCE, TECHNOLOGY ASSESSMENT), BIOINFORMATICS (COMPUTER SCIENCE, INFORMATION SCIENCE, MATHEMATICS, BIOMECHANICS, COMPUTATIONAL MODELING AND SIMULATION, REMOTE DIAGNOSIS AND THERAPY), IMAGING DEVICE DEVELOPMENT, BIOMEDICAL IMAGING TECHNOLOGY DEVELOPMENT, IMAGE EXPLOITATION, CONTRAST AGENTS, INFORMATICS AND COMPUTER SCIENCES RELATED TO IMAGING, MOLECULAR AND CELLULAR IMAGING, BIOELECTRICS/BIOMAGNETICS, ORGAN AND WHOLE BODY IMAGING, SCREENING FOR DISEASES AND DISORDERS, AND IMAGING TECHNOLOGY ASSESSMENT AND SURGERY (TECHNIQUE DEVELOPMENT AND TECHNOLOGY DEVELOPMENT).
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Pittsburgh,
Pennsylvania
152133815
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 137% from $168,596 to $400,260.
Carnegie Mellon University was awarded
Project Grant R21EB037268
worth $400,260
from the National Institute of Biomedical Imaging and Bioengineering in February 2025 with work to be completed primarily in Pittsburgh Pennsylvania United States.
The grant
has a duration of 3 years and
was awarded through assistance program 93.286 Discovery and Applied Research for Technological Innovations to Improve Human Health.
The Project Grant was awarded through grant opportunity Trailblazer Award for New and Early Stage Investigators (R21 Clinical Trial Optional).
Status
(Ongoing)
Last Modified 3/20/26
Period of Performance
2/1/25
Start Date
1/31/28
End Date
Funding Split
$400.3K
Federal Obligation
$0.0
Non-Federal Obligation
$400.3K
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for R21EB037268
Transaction History
Modifications to R21EB037268
Additional Detail
Award ID FAIN
R21EB037268
SAI Number
R21EB037268-3819201183
Award ID URI
SAI UNAVAILABLE
Awardee Classifications
Private Institution Of Higher Education
Awarding Office
75N800 NIH National Institute of Biomedical Imaging and Bioengineering
Funding Office
75N800 NIH National Institute of Biomedical Imaging and Bioengineering
Awardee UEI
U3NKNFLNQ613
Awardee CAGE
97668
Performance District
PA-12
Senators
Robert Casey
John Fetterman
John Fetterman
Modified: 3/20/26