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| ID | Type | Description | Link |
|---|---|---|---|
| P20GM109090 | U.S. NIH Grant/Contract | View source |
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The second arm was not completed since the first arm was not successful based on the convergence criteria.
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| Name | Class |
|---|---|
| National Institute of General Medical Sciences (NIGMS) | NIH |
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Exoskeletons, wearable devices that assist with walking, can improve mobility in clinical populations. With exoskeletons, it is crucial to optimize the assistance profile. Recent studies describe algorithms (i.e., human-in-the-loop) to optimize the assistance profile with real-time metabolic measurements. The needed duration of current human-in-the-loop (HITL) algorithms range from 20 minutes to 1 hour which is longer than the average duration that most patients with peripheral artery disease (PAD) can walk. Because of this limited walking duration, it is often not possible for patients with PAD to reach steady-state metabolic cost, which makes these measurements are not useful for optimizing exoskeletons. In this study, investigators intend to develop and evaluate HITL optimization methods for exoskeletons and use the information to design and evaluate a portable hip exoskeleton. Shorter and more clinically feasible HITL optimization strategies based on experiments in healthy adults might allow utilizing these optimization strategies to become available for patient populations such as patients with PAD.
Exoskeletons, wearable devices that assist with walking, can improve mobility in clinical populations. With exoskeletons, it is crucial to optimize the assistance profile. Recent studies describe algorithms (i.e., human-in-the-loop) to optimize the assistance profile with real-time metabolic measurements. The needed duration of current human-in-the-loop (HITL) algorithms range from 20 minutes to 1 hour which is longer than the average duration that most patients with peripheral artery disease (PAD) can walk. Because of this limited walking duration, it is often not possible for patients with PAD to reach steady-state metabolic cost, which makes these measurements are not useful for optimizing exoskeletons. Shorter and more clinically feasible HITL optimization strategies based on experiments in healthy adults might allow utilizing these optimization strategies to become available for patient populations such as patients with PAD.
This study will test different methods for optimizing exoskeletons. It will consist of an habituation session to the hip exoskeleton, an optimization session to find the optimal actuation settings using an algorithm that converges toward the optimum based on real-time measurements (human-in-the-loop algorithm) and a post-test at the end of optimization session to compare different conditions. The outcomes will be evaluated by surface electromyography, exoskeleton sensors, ground reaction force, walking speed, indirect calorimetry, and motion capture (Vicon).
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Optimal Assistance Pattern | Experimental | An optimization algorithm will change the assistance pattern on the hip exoskeleton during walking sessions and the optimal assistance pattern will be determined when gait variability is minimized. |
|
| Endurance Effectds | Experimental | Endurance of participants using ground reaction force (Bertec treadmill), walking speed (Bertec treadmill), indirect calorimetry (Cosmed), and motion capture (Vicon) will be determined. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Exoskeleton Optimization | Other | Participants will walk 10-minute trials while an optimization algorithm changes the assistance profile of the exoskeleton. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Time to Convergence | Convergence is determined when the estimated optimal exoskeleton settings vary less than 10%. The time to convergence is measured. | 10 minutes |
| Peak Extension Timing | The time to peak extension moment of exoskeleton is measured by plotting the exoskeleton moment versus stride cycle percentage and finding the timing when the peak in the extension moment occurs expressed in percent of the stride cycle. | 20 seconds |
| Peak Flexion Timing | The time to peak flexion moment of exoskeleton is measured by plotting the flexion moment versus stride cycle percentage and finding the timing when the peak in the flexion moment occurs expressed in percent of the stride cycle. | 20 seconds |
| Largest Lyapunov Exponent | Largest Lyapunov exponent (the rate of separation of infinitesimally close trajectories) of lower limb kinematics is determined. Largest Lyapunov exponent is calculated using Wolf's algorithm. The theoretical range is from zero to plus infinity. Zero indicates an entirely stable periodic movement pattern. Higher values indicate more unstable and chaotic movement patterns. Lower values are considered better, and higher values are considered worse for gait stability. | 20 seconds |
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Inclusion Criteria:
Ability to provide written consent
Chronic claudication history
Ankle-brachial index < 0.90 at rest
Stable blood pressure, lipids, and diabetes for > 6 weeks
Ability to walk on a treadmill for multiple five-minute spans
Ability to fit in exoskeleton
Exclusion Criteria:
Resting pain or tissue loss due to peripheral artery disease (PAD, Fontaine stage III and IV)
Foot ulceration
Acute lower extremity event secondary to thromboembolic disease or acute trauma
Walking capacity limited by diseases unrelated to PAD, such as:
Acute injury or pain in lower extremity
Current illness
Inability to follow visual cues due to blindness
Inability to follow auditory cues due to deafness
Pregnant
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| Name | Affiliation | Role |
|---|---|---|
| Philippe Malcolm | University of Nebraska | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Nebraska Omaha | Omaha | Nebraska | 68182 | United States |
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No enrolled participants were tested in the endurance arm because the preceding optimal assistance arm was not successful based on the convergence criteria.
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| ID | Title | Description |
|---|---|---|
| FG000 | Optimal Assistance Pattern | An optimization algorithm will change the assistance pattern on the hip exoskeleton during walking sessions and the optimal assistance pattern will be determined when gait variability is minimized. Exoskeleton Optimization: Participants will walk 10-minute trials while an optimization algorithm changes the assistance profile of an exoskeleton. |
| FG001 | Effects on Endurance | The effects on endurance of participants using ground reaction force (Bertec treadmill), walking speed (Bertec treadmill), indirect calorimetry (Cosmed), and motion capture (Vicon) will be determined. Endurance Evaluation: Participants will walk 2 trials at a speed of 1 meter per second until the participant indicates claudication or a maximum duration of 6 minutes. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
The effect on endurance arm was not analyzed since the preceding optimal assistance pattern aim was not successful based on the predefined convergence criteria.
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| ID | Title | Description |
|---|---|---|
| BG000 | Optimal Assistance Pattern | An optimization algorithm will change the assistance pattern on the hip exoskeleton during walking sessions and the optimal assistance pattern will be determined when gait variability is minimized. Exoskeleton Optimization: Participants will walk 10-minute trials while an optimization algorithm changes the assistance profile of an exoskeleton. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| 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 | Time to Convergence | Convergence is determined when the estimated optimal exoskeleton settings vary less than 10%. The time to convergence is measured. | The time to convergence for the optimal assistance pattern could not be reported since the convergence criterion was not achieved. The effect on endurance arm was not analyzed since the preceding optimal assistance pattern aim was not successful based on the predefined convergence criteria. | Posted | 10 minutes |
|
1.5 years
The effect on endurance arm was not analyzed since the preceding optimal assistance pattern aim was not successful based on the predefined convergence criteria.
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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 | Optimal Assistance Pattern | An optimization algorithm will change the assistance pattern on the hip exoskeleton during walking sessions and the optimal assistance pattern will be determined when gait variability is minimized. Exoskeleton Optimization: Participants will walk 10-minute trials while an optimization algorithm changes the assistance profile of an exoskeleton. |
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The effect on endurance arm was not analyzed since the preceding optimal assistance pattern aim was not successful based on the predefined convergence criteria.
| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Philippe Malcolm | University of Nebraska-Omaha | 617-487-1148 | pmalcolm@unomaha.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 | Nov 3, 2023 | Apr 23, 2025 | Prot_SAP_000.pdf |
| ICF | No | No | Yes | Informed Consent Form | Oct 17, 2024 | Apr 23, 2025 | ICF_001.pdf |
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| ID | Term |
|---|---|
| D058729 | Peripheral Arterial Disease |
| ID | Term |
|---|---|
| D050197 | Atherosclerosis |
| D001161 | Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |
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| Endurance Evaluation | Other | Participants will walk 2 trials at a speed of 1 meter per second until the participant indicates claudication or a maximum duration of 6 minutes, which ever comes first. |
|
| BG001 | Effects on Endurance | The effects on endurance of participants using ground reaction force (Bertec treadmill), walking speed (Bertec treadmill), indirect calorimetry (Cosmed), and motion capture (Vicon) will be determined. Endurance Evaluation: Participants will walk 2 trials at a speed of 1 meter per second until the participant indicates claudication or a maximum duration of 6 minutes. |
| BG002 | Total | Total of all reporting groups |
| Participants |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Race (NIH/OMB) | Count of Participants | Participants |
|
| Region of Enrollment | Number | participants |
|
| OG001 | Endurance Effectds | The effects on endurance of participants using ground reaction force (Bertec treadmill), walking speed (Bertec treadmill), indirect calorimetry (Cosmed), and motion capture (Vicon) will be determined. Endurance Evaluation: Participants will walk 2 trials at a speed of 1 meter per second until the participant indicates claudication or a maximum duration of 6 minutes. |
|
| Primary | Peak Extension Timing | The time to peak extension moment of exoskeleton is measured by plotting the exoskeleton moment versus stride cycle percentage and finding the timing when the peak in the extension moment occurs expressed in percent of the stride cycle. | The effect on endurance arm was not analyzed since the preceding optimal assistance pattern aim was not successful based on the predefined convergence criteria. | Posted | Mean | Standard Deviation | % stride cycle | 20 seconds |
|
|
|
| Primary | Peak Flexion Timing | The time to peak flexion moment of exoskeleton is measured by plotting the flexion moment versus stride cycle percentage and finding the timing when the peak in the flexion moment occurs expressed in percent of the stride cycle. | The effect on endurance arm was not analyzed since the preceding optimal assistance pattern aim was not successful based on the predefined convergence criteria. | Posted | Mean | Standard Deviation | % stride cycle | 20 seconds |
|
|
|
| Primary | Largest Lyapunov Exponent | Largest Lyapunov exponent (the rate of separation of infinitesimally close trajectories) of lower limb kinematics is determined. Largest Lyapunov exponent is calculated using Wolf's algorithm. The theoretical range is from zero to plus infinity. Zero indicates an entirely stable periodic movement pattern. Higher values indicate more unstable and chaotic movement patterns. Lower values are considered better, and higher values are considered worse for gait stability. | The effect on endurance arm was not analyzed since the preceding optimal assistance pattern aim was not successful based on the predefined convergence criteria. | Posted | Mean | Standard Deviation | (Lyapunov exponent is unitless) | 20 seconds |
|
|
|
| 0 |
| 9 |
| 0 |
| 9 |
| 0 |
| 9 |
| EG001 | Effects on Endurance | The effects on endurance of participants using ground reaction force (Bertec treadmill), walking speed (Bertec treadmill), indirect calorimetry (Cosmed), and motion capture (Vicon) will be determined. Endurance Evaluation: Participants will walk 2 trials at a speed of 1 meter per second until the participant indicates claudication or a maximum duration of 6 minutes. | 0 | 0 | 0 | 0 | 0 | 0 |
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| D002318 |
| Cardiovascular Diseases |
| D016491 | Peripheral Vascular Diseases |