Improving Balance and Energetics of Walking Using a Hip Exoskeleton

NCT05447884 | Unknown

• 1 other identifier: 24671
• Study Type: interventional
• Target: 100
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Hip exoskeleton; Assistance personalization; Walking balance; Walking energetics; Reinforcement learning; Adaptive optimal control; Learning-based control; Gait analysis; Human-in-the-loop optimization; Human-robot-interaction

Outcome Measures

Primary Outcomes (2)

• Human lower limb joints angular position

  The investigators will measure the angular position \[rad\] on the left and right hip joints by using the embedded incremental encoders that are installed on the hip exoskeleton. The investigators will measure the angular position \[rad\] on the left and right knee and ankle joints using the motion capture system containing the 3-dimensional coordinates of reflective markers.

  Through study completion, an average of 55 months.

• Human lower limb joints angular velocity

  The investigators will measure the angular velocity \[rad/sec\] on the left and right hip joints by using the embedded incremental encoders that are installed on the hip exoskeleton. The investigators will measure the angular velocity \[rad/sec\] on the left and right knee and ankle joints using the motion capture system containing the 3-dimensional coordinates of reflective markers. The calculation of the angular velocity is the time-derivative of the angular position in the unit of \[mm\].

  Through study completion, an average of 55 months.

Secondary Outcomes (6)

• Human walking stride length

  Through study completion, an average of 55 months.

• Human walking symmetry

  Through study completion, an average of 55 months.

• Human lower limb joints torque

  Through study completion, an average of 55 months.

• Human lower limb joints power

  Through study completion, an average of 55 months.

• Human lower limb muscles activity

  Through study completion, an average of 55 months.

Study Arms (2)

Group A - Participants without neurological disorders

EXPERIMENTAL

Individuals without any neurological disorders will be recruited (Group A). Usually, participants from this group are able to walk normally on different terrains and at multiple typical walking speeds.

Other: A zero impedance mode, controlling a wearable bilateral hip exoskeleton; Other: A personalized optimal assistance mode, controlling a wearable bilateral hip exoskeleton; Other: A free walking mode, without wearing a wearable bilateral hip exoskeleton

Group B - Participants with paretic stroke

EXPERIMENTAL

Individuals with paretic stroke will be recruited (Group S). Usually, participants from this group have limited hip joint motion of range, weakened hip joint flexion or extension, or both flexion and extension functionalities, but they can also walk independently.

Other: A zero impedance mode, controlling a wearable bilateral hip exoskeleton; Other: A personalized optimal assistance mode, controlling a wearable bilateral hip exoskeleton; Other: A free walking mode, without wearing a wearable bilateral hip exoskeleton

Interventions

A zero impedance mode, controlling a wearable bilateral hip exoskeleton OTHER

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.

Group A - Participants without neurological disorders; Group B - Participants with paretic stroke

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

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.

Group A - Participants without neurological disorders; Group B - Participants with paretic stroke

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

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.

Group A - Participants without neurological disorders; Group B - Participants with paretic stroke

Eligibility Criteria

• Age: 18 Years - 64 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• Between 18 and 64 years old
• Live in the United States
• Able to understand study requirements and sign an informed consent
• Have full range of motion in your hip joint
• Able to walk normally without any assistance.

You may not qualify if:

• Cannot follow instructions or provide feedback due to cognitive or language limitations
• Suffered from a stroke that affects balance or walking
• Use an electronically controlled medical device, such as a pacemaker, implanted defibrillator, or drug pump
• Pregnancy
• Experience numbness, tingling, muscle weakness, pain, or paralysis in any part of your body
• Cannot walk or balance without help from a person or a tool, such as a walker or cane
• Limited movement in your hip or ankle
• You have any skin-related allergies or irritation to adhesives
• Have blood circulation, heart, metabolic, or cognitive disorders, including but not limited to: Peripheral vascular disease, Pitting edema, Heart disease, Diabetes (uncontrolled), Seizures, and Cognitive diagnoses that affect their ability to process information.
• Between 18 and 64 years old
• Live in the United States
• Able to understand study requirements and sign an informed consent
• Have weakness on one side of their body due to a stroke within the past 6 months
• Have the doctor confirm that the subjects had a stroke within the past 6 months
• Can walk without any assistance for at least 6 minutes and 1000 feet (a little less than a quarter-mile)

Sponsors & Collaborators

• North Carolina State University: lead
• University of North Carolina, Chapel Hill: collaborator

Study Sites (1)

North Carolina State University

Raleigh, North Carolina, 27695, United States

RECRUITING

Related Publications (3)

• M. Li, Y. Wen, X. Gao, J. Si, and H. Huang, "Toward expedited impedance tuning of a robotic prosthesis for personalized gait assistance by reinforcement learning control," IEEE Trans. Robot., vol. 38, no. 1, pp. 407-420, 2022.

  RESULT

• Wen Y, Si J, Brandt A, Gao X, Huang HH. Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis. IEEE Trans Cybern. 2020 Jun;50(6):2346-2356. doi: 10.1109/TCYB.2019.2890974. Epub 2019 Jan 16.

  PMID: 30668514 RESULT

• X. Tu, M. Li, M. Liu, J. Si, and H. H. Huang, "A data-driven reinforcement learning solution framework for optimal and adaptive personalization of a hip exoskeleton," in 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 10 610- 10 616.

  RESULT

Related Links

• We have recently started to design a new hip exoskeleton. Our innovation lies in the design of active hip abduction/adduction joints, beyond the powered hip flexion/extension, in order to assist gait and dynamic postural stability at the same time.

MeSH Terms

Conditions

Stroke

Condition Hierarchy (Ancestors)

Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases

Central Study Contacts

Qiang Zhang, Ph.D.

CONTACT

412-628-4758; qzhang25@ncsu.edu

Laura Rohrbaugh

CONTACT

919-513-3840; lasmith6@ncsu.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Jackson Family Distinguished Professor

Study Record Dates

• First Submitted: June 28, 2022
• First Posted: July 7, 2022
• Study Start: June 1, 2022
• Primary Completion: December 31, 2024
• Study Completion: December 31, 2025
• Last Updated: December 20, 2023

Record last verified: 2023-12

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)