Adaptive Hip Exoskeleton for Stroke Gait Enhancement
- Conditions
- Stroke
- Interventions
- Device: Robotic hip exoskeleton
- Registration Number
- NCT05536739
- Lead Sponsor
- Georgia Institute of Technology
- Brief Summary
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.
- 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.
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 12
- 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
- Require a walker to walk independently
- Have a shuffling gait pattern
- 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
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Arm && Interventions
Group Intervention Description Powered Hip Exoskeleton for Stroke Assistance Robotic hip exoskeleton This study will be conducted on a sample population of stroke subjects (single arm). Each subject will test with each condition of the exoskeleton (repeated measures).
- Primary Outcome Measures
Name Time Method Temporal Convolutional Network (TCN) model performance (Joint moment accuracy) 1 year 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.
Metabolic cost for level ground walking 1 year 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.
Biological joint work 1 year 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.
- Secondary Outcome Measures
Name Time Method Single limb stance time asymmetry index 1 year 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.
custom surveys 1 year Custom surveys will be administered to assess participant opinion on all aspects of the wearable hip exoskeleton including fit, comfort, assistance level and timing, aesthetics and other aspects of wearable devices. This survey data will be used to understand the perceived usability and acceptance of the device for users as well as to understand areas where improvement is needed.
Step Length Asymmetry index 1 year 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.
10 meter walk test (self-selected) 1 year 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.
The timed up and go (TUG) 1 year 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.
10 meter walk test (fast) 1 year This will be measured as the participant walks a distance of 10 meters across a gait mat at their fastest and safest walking speed. This measure will be recorded in seconds with lower values indicating faster speed and higher values indicating slower speeds.
2 Minute Walk Test 1 year This is a measurement of endurance and functional ability that assesses the participants ability to walk a distance over a time period of 2 minutes. It is measured in distance with greater distances indicating improved levels of endurance and functional ability.
OPUS (Orthotic and Prosthetic User Survey)-Satisfaction module 1 year This survey specifically assesses the satisfaction of a user with a wearable assistive device and includes questions on comfort, aesthetics, fit, comfort, weight, pain and skin issues. Higher scores are indicative of improved levels of satisfaction with the device.
System Usability Scale 1 year This questionnaire consists of 10 questions that participants will answer on a 5-point Likert scale regarding the perceived usability of the exoskeleton like ease of use, complexity and overall satisfaction. Higher scores are indicative of better perceived usability.
Trial Locations
- Locations (1)
Exoskeleton and Prosthetic Intelligent Controls Lab
🇺🇸Atlanta, Georgia, United States