Exoskeleton Variability Optimization

University of Nebraska9 enrolled

Overview

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).

Study Type

Eligibility

Sex: ALLMin age: 19 YearsMax age: 85 YearsHealthy volunteers:

Medical Language ↔ Plain English

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 * Waist circumference 78 to 92 centimeters (31 to 36 inches) * Thigh circumference 48 to 60 centimeters (19 to 24 inches) * Minimal thigh length 28 centimeters (11 inches) 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: * Neurological disorders * Musculoskeletal disorders (arthritis, scoliosis, stroke, spinal injury, etc.) * History of ankle instability * Knee injury * Diagnosed joint laxity * Lower limb injury * Surgery within the past 12 months * Joint replacement * Pulmonary disease or breathing disorders * Cardiovascular disease * Vestibular disorder * 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

Outcomes

Primary Outcomes

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

Exoskeleton Variability Optimization

Overview

Conditions

Interventions

Eligibility

Locations (1)

Outcomes

Primary Outcomes