This work will focus on new algorithms for robotic ankle 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., overground, 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 on a proposed novel artificial intelligence (AI) system that self-adapts 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. Stroke survivor participants will be fitted with an ankle exoskeleton and proceed to walk on a treadmill or perform various movement tasks. The same tasks will be performed by the participants without wearing the ankle exoskeleton to serve as a baseline. The investigators expect improved outcomes in the powered ankle exoskeleton compared to baseline conditions.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
BASIC_SCIENCE
Masking
NONE
Enrollment
10
The ankle exoskeleton provides bilateral torque assistance at the ankle joints during common functional tasks such as level-ground walking, stair and ramp ascent, and other daily activities, thereby reducing the mechanical workload and supporting more effective community ambulation. In particular, the device is designed to address drop-foot on the paretic side by delivering bidirectional assistance, which helps improve toe clearance during swing as well as push-off during stance. As a wearable assistive device, assistance is applied only while the device is worn.
The intervention will serve as a baseline where participants will be asked to perform the tasks without wearing an ankle exoskeleton.
Georgia Institute of Technology
Atlanta, Georgia, United States
Temporal Convolutional Network (TCN) model performance (Joint moment estimation accuracy)
This outcome represents the error with which the deep learning model embedded into our ankle exoskeleton's microprocessor predicts ankle joint moments in stroke patients. Specifically, the coefficient of determination (R²) is computed between the predicted ankle joint moments and the ground truth measurements. Ground truth measurements are obtained from a laboratory-grade force plate system and inverse dynamics calculations. Ankle 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 ankle joint moments. This metric provides a comprehensive assessment of the exoskeleton's ability to accurately estimate ankle 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 two conditions: without the exoskeleton, with the exoskeleton in a powered state. Metabolic cost will be calculated from respiratory gas exchange data (VO₂ and VCO₂) using Brockway equations \[1\] for energy expenditure. Comparisons between the two conditions will be conducted to assess the effectiveness of the exoskeleton with respect to metabolic cost. Energy expenditure (kilojoule/minute) = 16.58 VO₂ (Liters/minute) +4.51VCO₂ (Liters/minute) \[1\] Brockway, J. M. "Derivation of formulae used to calculate energy expenditure in man." Human nutrition. Clinical nutrition 41.6 (1987): 463-471.
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 (temporal)
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 (spatial)
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
Interlimb propulsion asymmetry (kinetic)
This will be measured as the participant walks on an instrumented treadmill with force plates, by calculating the peak anterior-posterior propulsive impulse generated by the paretic and non-paretic limbs during late stance. A propulsion symmetry index will then be calculated using the difference between the propulsive impulses of the two limbs, expressed as a percentage. A value of 0 indicates perfect symmetry, while greater values reflect larger propulsion asymmetry between limbs, with lower paretic propulsion indicating impaired forward progression capacity.
Time frame: 1 year
Trailing limb angle (kinematic)
This will be measured as the participant walks on an instrumented treadmill via motion capture, by calculating the sagittal-plane angle between the vertical axis and the line connecting the hip joint center to the ankle joint center at the moment of contralateral initial contact. Values will be reported in degrees, with larger angles indicating greater trailing-limb extension and push-off capacity during gait.
Time frame: 1 year
Anterior ground reaction force
This will be measured as the participant walks on an instrumented treadmill with force plates, by extracting the peak anterior component of the ground reaction force during late stance. The peak value will be expressed as a percentage of body weight, with higher values reflecting stronger forward propulsion generated by the limb.
Time frame: 1 year
Paretic ankle dorsiflexion angle
This will be measured as the participant walks on an instrumented treadmill via motion capture, by calculating the sagittal-plane angle of the paretic ankle joint between the foot and shank segments during swing and stance phases. The peak dorsiflexion angle during swing will be extracted to assess foot clearance. Values will be reported in degrees, with larger dorsiflexion angles indicating greater ankle mobility and reduced risk of foot drop.
Time frame: 1 year
Ten Meter Walk test (10 mwt)
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
6 minute walk test (6MWT)
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
Maximum walking speed test
This will be measured as the fastest treadmill walking speed that the participant can sustain for approximately one minute. The maximum speed will be recorded under conditions with and without the exoskeleton. This test assesses functional capability and physical performance when higher exertion is required over a short duration.
Time frame: 1 year
Modified Stroke Impact Scale
This self-report questionnaire is designed specifically for individuals post-stroke to assess the impact of stroke on multiple domains of health and function, including strength, hand function, mobility, activities of daily living, emotion, memory, communication, and participation. Scores are standardized from 0 to 100, with higher scores indicating less impact of stroke and better functional outcomes.
Time frame: 1 year
The Activities-specific Balance Confidence (ABC) Scale
This survey measures the participant's confidence in performing various ambulatory activities without losing balance or becoming unsteady. Participants rate their confidence on a scale from 0% (no confidence) to 100% (complete confidence) across 16 daily activities. Higher scores indicate greater balance confidence and reduced perceived fall risk.
Time frame: 1 year