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| ID | Type | Description | Link |
|---|---|---|---|
| R03HD097740 | U.S. NIH Grant/Contract | View source | |
| DP2HD111709 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) | NIH |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Hip Exoskeleton for Stroke Gait Assistance | Experimental | This study will be conducted on a sample population of stroke subjects (single arm). Subjects will be tested with the hip exoskeleton and baseline. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Hip exoskeleton | Device | 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. |
| 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. 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. | 5 Days |
| 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. | 5 days |
| Biological Joint Work - Level Walking | Mechanical work performed by the lower limb joints during level walking 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during level walking. | 5 days |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 5 days |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Aaron Young, Ph.D. | Georgia Institute of Technology | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Exoskeleton and Prosthetic Intelligent Controls Lab | Atlanta | Georgia | 30332 | United States |
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| ID | Title | Description |
|---|---|---|
| FG000 | 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 the hip exoskeleton and baseline. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
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| ID | Title | Description |
|---|---|---|
| BG000 | 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 the hip exoskeleton and baseline. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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. 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. | This outcome measure was only measured during the hip exoskeleton intervention as it can only be measured if wearing a hip exoskeleton as it is representative of the error with which the deep learning model embedded into the hip exoskeleton microprocessor predicts hip joint moments in stroke patients. | Posted | Mean | Standard Deviation | Coefficient of determination | 5 Days |
From enrollment until study completion, an average of 5 days.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Hip Exoskeleton for Stroke Gait Assistance - Baseline Intervention | This study will be conducted on a sample population of stroke subjects (single arm). Subjects will be tested at baseline. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Aaron Young | Georgia Institute of Technology | 404-385-5306 | aaron.young@me.gatech.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | May 7, 2026 | May 7, 2026 | Prot_SAP_001.pdf |
| ICF | No | No | Yes | Informed Consent Form | Sep 9, 2025 | Mar 25, 2026 | ICF_000.pdf |
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| ID | Term |
|---|---|
| D020521 | Stroke |
| ID | Term |
|---|---|
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
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| ID | Term |
|---|---|
| C074807 | BaseLine dental cement |
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The model used is a repeated measures single arm study
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| Baseline | Other | The intervention will serve as a baseline where participants will be asked to perform the tasks without wearing a hip exoskeleton. |
|
| Biological Joint Work - Incline Walking | Mechanical work performed by the lower limb joints will be quantified during incline walking 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the incline walking. | 5 days |
| Biological Joint Work - Stair Ascent | Mechanical work performed by the lower limb joints will be quantified during stair ascent 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the stair ascent task. | 5 days |
| Biological Joint Work - Sit to Stand | Mechanical work performed by the lower limb joints will be quantified during sit to stand 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the sit to stand task. | 5 days |
| Biological Joint Work - go and Grab | Mechanical work performed by the lower limb joints will be quantified during a go and grab task through biomechanical analysis of motion capture data. In the go and grab task, participants take several steps, lean forward, and pick up a weighted object from a low surface just above ground level. 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the go and grab task. | 5 days |
| 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. | 5 days |
| 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. | 5 days |
| 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. Scores are transformed to a 0-100 scale, with 0 indicating the poorest perceived health status and 100 indicating the best, across domains of disability and health-related quality of life. Higher scores are indicative of improved quality of life. | 5 days |
| 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. | 5 days |
| 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. | 5 days |
| Years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Race (NIH/OMB) | Count of Participants | Participants |
|
| Region of Enrollment | Count of Participants | Participants |
|
| Paretic Side | A measure reflecting the count of participant's paretic side. | Count of Participants | Participants |
|
| Stroke Type | A measure reflecting the count of participant's type of stroke. | Count of Participants | Participants |
|
| Time Since Stroke | A measure reflecting the time since the participant's last known stroke in months. | Mean | Standard Deviation | Months |
|
| Height | A measure reflecting the participant's height in centimeters. | Mean | Standard Deviation | Centimeters |
|
| Weight | A measure reflecting the participant's weight in kilograms. | Mean | Standard Deviation | Kilograms |
|
| Body Mass Index (BMI) | A measure of the participant's weight divided by height, squared. | Mean | Standard Deviation | Kilograms / Meters, Squared |
|
| Self Selected Walking Speed | A measure reflecting the participant's comfortable self-selected walking speed. | Mean | Standard Deviation | Meters per second |
|
| Fugl Meyer Assessment - Lower Extremity | A measure reflecting the participant's lower extremity functionality. This is a standardized impairment based index to evaluate motor function, reflex activity, voluntary movement, and coordination. Measured on a scale from 0 to 34. Higher on the scale reflects higher functionality. | Mean | Standard Deviation | Unit on a scale |
|
| Mini Balance Evaluations Systems Test (Mini-BESTest) | A measure to reflect a participant's balance. The Minibestest is a 14-item clinical assessment tool designed to evaluate dynamic balance and postural control across four key components: anticipatory postural adjustments, reactive postural control, sensory orientation, and dynamic gait. Measured on a scale from 0 - 28. Higher on the scale reflects better balance. | Mean | Standard Deviation | Units on a scale |
|
| Assistive Device | A count of assistive devices worn by participants when enrolling in the study. | Count of Participants | Participants |
|
| ID | Title | Description |
|---|
| OG000 | 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 the hip exoskeleton and baseline. |
|
|
| Primary | 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 participant was excluded due to self-reported physiological condition unrelated to study intervention. 1 participant excluded due to physical difficulty unrelated to study intervention. | Posted | Mean | Standard Deviation | Watts / Kilogram | 5 days |
|
|
|
|
| Primary | Biological Joint Work - Level Walking | Mechanical work performed by the lower limb joints during level walking 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during level walking. | 1 participant was excluded due to self-reported physiological condition unrelated to study intervention. 1 participant excluded due to invalid trial data. | Posted | Mean | Standard Deviation | Watts per Kilogram | 5 days |
|
|
|
|
| Secondary | 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. | One participant excluded due to self-reported physiological symptoms unrelated to the study intervention. | Posted | Mean | Standard Deviation | Meters per Second | 5 days |
|
|
|
|
| Secondary | 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. | One participant excluded due to self-reported physiological symptoms unrelated to the study intervention. | Posted | Mean | Standard Deviation | Seconds | 5 days |
|
|
|
|
| Secondary | 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. | One participant excluded due to self-reported physiological symptoms unrelated to the study intervention. One participant excluded due to physical difficulty unrelated to the study intervention | Posted | Mean | Standard Deviation | Meters | 5 days |
|
|
|
|
| Secondary | 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. Scores are transformed to a 0-100 scale, with 0 indicating the poorest perceived health status and 100 indicating the best, across domains of disability and health-related quality of life. Higher scores are indicative of improved quality of life. | One participant excluded due to self-reported physiological symptoms unrelated to the study intervention. | Posted | Mean | Standard Deviation | Units on a scale | 5 days |
|
|
|
|
| Secondary | 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. | One participant excluded due to self-reported physiological symptoms unrelated to the study intervention. | Posted | Mean | Standard Deviation | Units on a scale | 5 days |
|
|
|
|
| Secondary | 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. | One participant excluded due to self-reported physiological symptoms unrelated to the study intervention. | Posted | Mean | Standard Deviation | Meters per Second | 5 days |
|
|
|
|
| Primary | Biological Joint Work - Incline Walking | Mechanical work performed by the lower limb joints will be quantified during incline walking 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the incline walking. | 1 participant was excluded due to self-reported physiological condition unrelated to study intervention. 2 participant excluded due to invalid trial data. | Posted | Mean | Standard Deviation | Watts per Kilogram | 5 days |
|
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|
|
| Primary | Biological Joint Work - Stair Ascent | Mechanical work performed by the lower limb joints will be quantified during stair ascent 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the stair ascent task. | One participant excluded due to self-reported physiological symptoms unrelated to the study intervention. | Posted | Mean | Standard Deviation | Joules per Kilogram per repetition | 5 days |
|
|
|
|
| Primary | Biological Joint Work - Sit to Stand | Mechanical work performed by the lower limb joints will be quantified during sit to stand 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the sit to stand task. | 1 participant was excluded due to self-reported physiological condition unrelated to study intervention. 1 participant did not complete the task due to fatigue from the testing session. | Posted | Mean | Standard Deviation | Joules per Kilogram per repetition | 5 days |
|
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|
|
| Primary | Biological Joint Work - go and Grab | Mechanical work performed by the lower limb joints will be quantified during a go and grab task through biomechanical analysis of motion capture data. In the go and grab task, participants take several steps, lean forward, and pick up a weighted object from a low surface just above ground level. 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 work will be calculated by integrating positive joint powers, providing comprehensive quantification of joint energy generation at each joint during the go and grab task. | One participant excluded due to self-reported physiological symptoms unrelated to the study intervention. One participant did not complete the task due to fatigue from the testing session. | Posted | Mean | Standard Deviation | Joules per Kilogram per repetition | 5 days |
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|
| 0 |
| 12 |
| 0 |
| 12 |
| 0 |
| 12 |
| EG001 | Hip Exoskeleton for Stroke Gait Assistance - Hip Exoskeleton Intervention | This study will be conducted on a sample population of stroke subjects (single arm). Subjects will be tested with a hip exoskeleton. | 0 | 12 | 0 | 12 | 0 | 12 |
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| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |