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.
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
Allocation
NA
Purpose
BASIC_SCIENCE
Masking
NONE
Enrollment
12
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.
The intervention will serve as a baseline where participants will be asked to perform the tasks without wearing a 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.
Exoskeleton and Prosthetic Intelligent Controls Lab
Atlanta, Georgia, United States
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.
Time frame: 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.
Time frame: 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.
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Time frame: 1 year
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 1 year