An interventional study of Exoskeleton Optimization and Endurance Evaluation in Peripheral Arterial Disease, sponsored by University of Nebraska. Terminated at 1 site in United States. Open to participants aged 19 Years to 85 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-06-19.
Sponsored by University of Nebraska · Not applicable, Interventional, and Basic science
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).
1,542 studies on the registry are indexed under Peripheral Arterial Disease; 282 are open to participants now.
This study's enrollment of 9 is below the median of 74 across 1,066 interventional studies indexed under Peripheral Arterial Disease.
Browse Peripheral Arterial Disease studies →University of Nebraska is the lead sponsor of 473 studies on the registry; 66 are open to participants now.
Of its 75 completed or terminated interventional studies of FDA-regulated products, 46 (61%) have results posted.
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Ability to fit in exoskeleton
Exclusion Criteria:
Walking capacity limited by diseases unrelated to PAD, such as:
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.
Other: Exoskeleton Optimization
Endurance of participants using ground reaction force (Bertec treadmill), walking speed (Bertec treadmill), indirect calorimetry (Cosmed), and motion capture (Vicon) will be determined.
Other: Endurance Evaluation
Participants will walk 10-minute trials while an optimization algorithm changes the assistance profile of the exoskeleton.
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.
Time to Convergence
Convergence is determined when the estimated optimal exoskeleton settings vary less than 10%. The time to convergence is measured.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 20 seconds
| Milestone | Optimal Assistance Pattern | Effects on Endurance |
|---|---|---|
| Started | 9 | 0 |
| Completed | 0 | 0 |
| Not completed | 9 | 0 |
Convergence is determined when the estimated optimal exoskeleton settings vary less than 10%. The time to convergence is measured.
No measurements were reported for this outcome.
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.
| % stride cycle | Optimal Assistance Pattern | Effects on Endurance |
|---|---|---|
| Peak Extension Timing | 86 ± 8 | — |
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.
| % stride cycle | Optimal Assistance Pattern | Effects on Endurance |
|---|---|---|
| Peak Flexion Timing | 56 ± 2 | — |
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.
| (Lyapunov exponent is unitless) | Optimal Assistance Pattern | Effects on Endurance |
|---|---|---|
| Largest Lyapunov Exponent | 6.6 ± 2.8 | — |
Collected over 1.5 years. Non-serious events are listed at a 0% frequency threshold.
| Group | Deaths | Serious | Other |
|---|---|---|---|
| Optimal Assistance Pattern | 0/9 (0%) | 0/9 (0%) | 0/9 (0%) |
| Effects on Endurance | — | — | — |
The effect on endurance arm was not analyzed since the preceding optimal assistance pattern aim was not successful based on the predefined convergence criteria.
| Age, Categorical(Participants) | Optimal Assistance Pattern | Effects on Endurance | Total |
|---|---|---|---|
| <=18 years | 0 | 0 | 0 |
| Between 18 and 65 years | 8 | 0 | 8 |
| >=65 years | 1 | 0 | 1 |
| Sex: Female, Male(Participants) | Optimal Assistance Pattern | Effects on Endurance | Total |
|---|---|---|---|
| Female | 4 | 0 | 4 |
| Male | 5 | 0 | 5 |
| Race (NIH/OMB)(Participants) | Optimal Assistance Pattern | Effects on Endurance | Total |
|---|---|---|---|
| American Indian or Alaska Native | 0 | 0 | 0 |
| Asian | 2 | 0 | 2 |
| Native Hawaiian or Other Pacific Islander | 0 | 0 | 0 |
| Black or African American | 1 | 0 | 1 |
| White | 5 | 0 | 5 |
| More than one race | 0 | 0 | 0 |
| Unknown or Not Reported | 1 | 0 | 1 |
| Region of Enrollment(participants) | Optimal Assistance Pattern | Effects on Endurance | Total |
|---|---|---|---|
| United States | 9 | — | 9 |
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