Improving Balance and Energetics of Walking Using a Hip Exoskeleton

Summary

Robotic lower limb exoskeletons aim to improve or augment limb functions. Automatic modulation of robotic assistance is very important because it can increase the assistive outcomes and guarantee safety when using exoskeletons.

However, this automatic assistance adjustment is challenging due to person-to-person and day-to-day variations, as well as the time-varying complex human-machine-interaction forces. In recent years, human-in-the-loop optimization methods have been investigated to reduce participants' metabolic costs by providing personalized assistance from robotic exoskeletons. However, metabolic cost measure is noisy and the experimental protocol is usually relatively long. In addition, the influence of exoskeleton control on this human state in terms of energetic cost is unclear and indirect. More importantly, the optimization by reducing metabolic cost is found to affect human gait patterns and cause undesired outcomes. In this study, new evaluation measures other than metabolic cost will be investigated to optimize the assistance from a powered hip exoskeleton based on a reinforcement learning method. It is hypothesized that the new reinforcement learning-based optimal control approach will produce personalized torque assistance, reduce human volitional effort, and improve balance and other performance during walking tasks. Both participants without and with neurological disorders will be included in this study.

Conditions

• Stroke

Interventions

OTHER

A zero impedance mode, controlling a wearable bilateral hip exoskeleton

The bilateral hip exoskeleton has two degrees of freedom to enable the hip joint extension and flexion movement on both left and right sides. The zero impedance mode will not provide any assistance or resistance to the hip joints.

OTHER

A personalized optimal assistance mode, controlling a wearable bilateral hip exoskeleton

The personalized optimal assistance mode includes both individualized hip flexion and hip extension assistance, which is determined by using the reinforcement learning-based automatic control parameters tuning during walking tasks. Therefore, the personalized optimal assistance will be able to improve the walking gait performance and reduce the energetic consumption.

OTHER

A free walking mode, without wearing a wearable bilateral hip exoskeleton

The free walking mode will not include the usage of the wearable bilateral hip exoskeleton, and the human walking subjects will conduct pure natural walking tasks.

Sponsors & Collaborators

• University of North Carolina, Chapel Hill

  collaborator OTHER

• North Carolina State University

  lead OTHER

Study Design

Allocation

NON_RANDOMIZED

Purpose

TREATMENT

Masking

NONE

Model

CROSSOVER

Eligibility

Min Age

18 Years

Max Age

64 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2022-06-01

Primary Completion

2024-12-31

Completion

2025-12-31

Countries

• United States

Study Locations