Adaptive Hip Exoskeleton for Stroke Gait Enhancement

Adaptive Hip Exoskeleton for Stroke Survivors With Gait Impairment

This work will focus on new algorithms for robotic exoskeletons and testing these in human subject tests. Individuals who have previously had a stroke will walk while wearing a robotic exoskeleton on a specialized treadmill as well as during other movement tasks (e.g. over ground, stairs, ramps). The study will compare the performance of the advanced algorithm with not using the device to determine the clinical benefit.

Study Overview

• Status: Completed
• Conditions: Stroke
• Intervention / Treatment: Device: Robotic hip exoskeleton; Other: No hip exoskeleton; Device: Unpowered hip exoskeleton

Detailed Description

The focus of this work is a proposed novel artificial intelligence (AI) system to self-adapt control policy in powered exoskeletons to aid deployment systems that personalize to individual patient gait. Individuals post stroke have a broad range of mobility challenges including asymmetric gait, substantially decreased SSWS, and reduced stability, and therefore have greatly impaired overall mobility independence in the community. The investigators expect the proposed novel controller, capable of personalization to such variable and asymmetric gait patterns, will have significant benefits towards increasing community independence and mobility for patients post stroke. Patients post stroke will be fit with a hip exoskeleton (in a powered and/or unpowered state) and proceed to walk on a treadmill or perform various movement tasks. The same tasks will be performed by the patients without wearing the hip exoskeleton to serve as a baseline. The investigators expect improved outcomes in the powered hip exoskeleton compared to the unpowered hip exoskeleton and baseline conditions.

• Study Type: Interventional
• Enrollment (Actual): 12
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Georgia
    • Atlanta, Georgia, United States, 30332
      • Exoskeleton and Prosthetic Intelligent Controls Lab

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 85 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Between 18-85 years of age
• Had a stroke at least 6 months prior to study involvement
• Are community dwelling, which means the participant does not live in an assisted living facility
• Are able to provide informed consent to participate in the study activities
• Can safely participate in the study activities (per self-report)
• Must have a Functional Ambulation Category (FAC) score of 3 or above, which means the participant can walk without the assistance of another person

Exclusion Criteria:

• Require a walker to walk independently
• Have a shuffling gait pattern overground
• Have a Functional Ambulation Category (FAC) score of 2 or lower, which means the participant requires the assistance of another person in order to walk
• Have a significant secondary deficit beyond stroke (e.g. amputation, legal blindness or other severe impairment or condition) that in the opinion of the Principal Investigator (PI), would likely affect the study outcome or confound the results
• For exoskeleton-only studies, the exoskeleton device does not fit appropriately or safely, as determined by the research team during the fitting assessment.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Basic Science
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: Hip Exoskeleton for Stroke Gait Assistance; This study will be conducted on a sample population of stroke subjects (single arm). Subjects will be tested with either the powered hip exoskeleton and baseline or powered hip exoskeleton, unpowered hip exoskeleton, and baseline.

Device: Robotic hip exoskeleton; The intervention is an experimental robotic hip exoskeleton in a powered state providing assistance to the user that has been previously developed by the team. It is used to improve walking gait performance.; Other: No hip exoskeleton; The intervention will serve as a baseline where participants will be asked to perform the tasks without wearing a hip exoskeleton.; Device: Unpowered hip exoskeleton; The intervention is an experimental robotic hip exoskeleton in an unpowered state that has been previously developed by the team. It is used to improve walking gait performance.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Temporal Convolutional Network (TCN) model performance (Joint moment accuracy)	This outcome represents the error with which the deep learning model embedded into our hip exoskeleton's microprocessor predicts hip joint moments in stroke patients. Specifically, the coefficient of determination (R²) is computed between the predicted hip joint moments and the ground truth measurements. Ground truth measurements are obtained from a laboratory-grade force plate system and inverse dynamics calculations. Hip joint moment predictions are made at a frequency of 200 Hz and compared to the laboratory-measured values. For these measures, higher R² values (closer to 1.0) indicate better correlation between predicted and actual hip joint moments. This metric provides a comprehensive assessment of the exoskeleton's ability to accurately estimate hip joint moments in stroke patients during tasks, with improved outcomes representing better assistive capabilities for the user.	1 year
Metabolic cost for level ground walking	Metabolic energy expenditure will be quantified using an indirect calorimetry system (Parvo Medics, UT) that measures oxygen consumption (VO₂) and carbon dioxide production (VCO₂) during experimental tasks. Measurements will be collected from each participant during a 5-minute baseline standing period followed by level ground walking trials under three conditions: without the exoskeleton, with the exoskeleton in a powered state, and with the exoskeleton in an unpowered state. Metabolic cost will be calculated from respiratory gas exchange data using standard equations for energy expenditure.	1 year
Biological joint work	Mechanical work performed by the lower limb joints will be quantified through biomechanical analysis of motion capture data. Joint moments and angular velocities will be derived through inverse dynamics and kinematics, respectively. Joint power, calculated as the product of joint moment and angular velocity, will be integrated with respect to time using trapezoidal integration to determine mechanical work. Positive and negative work will be calculated by separately integrating positive and negative joint powers, providing comprehensive quantification of joint energy generation and absorption at each joint during the movement tasks.	1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Single limb stance time asymmetry index	This will be measured as the participant walks across a gait mat and/or via motion capture as the time spent on the right and left leg is calculated. The index will be calculated as the difference between the time spent in single-limb support for the right and left legs during walking and expressed as a percentage with a value of 0 indicating perfect symmetry and greater values indicating larger asymmetry.	1 year
Step Length Asymmetry index	This will be measured as the participant walks across a gait mat and/or via motion capture as the distance traversed by the right and left leg for each step. The index will be calculated as the difference between the step lengths of the right and left legs during walking and expressed as a percentage with a value of 0 indicating perfect symmetry and greater values indicating larger asymmetry.	1 year
10 meter walk test (self-selected)	This will be measured as the participant walks a distance of 10 meters across a gait mat at their self-selected (or comfortable) walking speed. This measure will be recorded in seconds with lower values indicating faster speed and higher values indicating slower speeds. Self-selected walking speed is highly correlated with functional ability and dependence.	1 year
The timed up and go (TUG)	This will be measured as the time it takes a participant to rise from a chair, walk three meters at a self-selected pace, turn, walk back to the chair and sit down. The total time taken will be measured in seconds with longer times indicating poorer physical performance. This test assesses functional mobility and dynamic balance.	1 year
6 Minute Walk Test	This is a measurement of endurance and functional ability that assesses the participants ability to walk a distance over a time period of 6 minutes. It is measured in distance with greater distances indicating improved levels of endurance and functional ability.	1 year
Modified Stroke Impact Scale	The Modified Stroke Impact Scale (SIS) is a self-report questionnaire that evaluates disability and health-related quality of life after stroke. Each item is rated in a 5-point Likert scale in terms of the difficulty the patient has experienced in completing each item. Higher scores are indicative of improved quality of life.	1 year
Modified Activities-specific balance confidence	The modified activities specific balance confidence is a self-report measure of balance confidence in performing various activities without losing balance or experiencing a sense of unsteadiness. Confidence is rated for various activities on a scale from 0% to 100% for each activity, with 0% indicative of no confidence and 100% indicative of complete confidence. Scores reflect balance confidence with higher scores indicative of improved balance confidence.	1 year
Fast self-selected walking speed	This will be measured as the participant walks on a treadmill at their fastest and safest walking speed. This measure will be recorded in meters/seconds with higher values indicating faster speed and lower values indicating slower speeds.	1 year

Collaborators and Investigators

• Sponsor: Georgia Institute of Technology
• Collaborators: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
• Investigators: Principal Investigator: Aaron Young, Ph.D., Georgia Institute of Technology

Study record dates

Study Major Dates

• Study Start (Actual): May 21, 2025
• Primary Completion (Actual): August 29, 2025
• Study Completion (Actual): August 29, 2025

Study Registration Dates

• First Submitted: September 8, 2022
• First Submitted That Met QC Criteria: September 8, 2022
• First Posted (Actual): September 13, 2022

Study Record Updates

• Last Update Posted (Actual): March 30, 2026
• Last Update Submitted That Met QC Criteria: March 25, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: Stroke
• Additional Relevant MeSH Terms: Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases; Stroke
• Other Study ID Numbers: H18182; R03HD097740 (U.S. NIH Grant/Contract); DP2HD111709 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No