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NCT Number: NCT07393971

Development of a Real-time Controller to Estimate Walking Performance Using a Bilateral Ankle Exoskeleton

This study is developing and testing a new controller for a robotic ankle exoskeleton (Biomotum) that can adjust itself in real time to better support people while they walk. The system learns how each person moves and automatically changes the amount and timing of assistance to make walking feel easier and more efficient. By using information from the person wearing the device, the exoskeleton can quickly find the level of support that works best for them. The long-term goal is to create personalized walking assistance that can help people with mobility limitations move more comfortably and with less effort.

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Key information

Conditions

Healthy Young Adults

Age range

19 year–35 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Biomechanics Research Building, University of Nebraska at Omaha

Omaha, Nebraska, 68108, United States

About this study

This project aims to develop and test a real-time adaptive controller for a robotic ankle exoskeleton (Biomotum) that personalizes assistance to each user by minimizing metabolic cost and optimizing muscle activation patterns during walking. Using human-in-the-loop optimization and advanced musculoskeletal modeling, the controller will dynamically adjust torque magnitude and timing to achieve optimal performance more quickly than current methods.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • able to walk independently on a treadmill for 10 minutes,
  • free of neurological, cardiovascular, pulmonary, or musculoskeletal conditions that limit walking and exercising,
  • no current lower extremity pain or injury,
  • able to wear an exoskeleton and safety harness, can provide informed consent

Exclusion criteria

  • history of neurological disease that affected gait or balance,
  • current or recent lower extremity musculoskeletal injury or surgery,
  • chronic lower extremity pain during walking,
  • inability to participate in moderate-intensity exercise,
  • require an assistive device for walking,
  • any metabolic or systemic diseases that may be exacerbated by exercise

Treatment and study plan

Adaptive Torque Control System for Ankle Exoskeleton

Device

This intervention uses a robotic ankle exoskeleton equipped with a real-time adaptive controller that adjusts plantarflexion torque based on each participant's walking mechanics. Unlike standard exoskeleton controllers that use fixed or pre-programmed assistance levels, this system employs human-in-the-loop optimization to continuously update torque magnitude and timing during treadmill walking. The controller integrates metabolic estimations, kinematic data, and musculoskeletal modeling to identify individualized assistance patterns that reduce walking effort and improve muscle activation efficiency. Participants complete multiple walking trials while the controller automatically modifies assistance to determine the optimal personalized settings.

Primary outcomes

  1. Successful Real-Time Operation of the Robotic Ankle Exoskeleton Controller

    Time frame: through study completion, an average of 1 year

    Device feasibility will be evaluated by the successful real-time operation of the robotic ankle exoskeleton and adaptive controller during treadmill walking. Feasibility is defined as the controller's ability to continuously generate, update, and apply assistive torque in real time based on incoming biomechanical and physiological data without system failure, interruption, or safety-related termination. Successful operation will be confirmed by continuous controller function and synchronized data acquisition across walking trials.

Secondary outcomes

  1. Net Metabolic Rate During Exoskeleton-Assisted Walking Measured by Indirect Calorimetry

    Time frame: through study completion, an average of 1 year

    Net oxygen consumption (VO₂) and carbon dioxide production (VCO₂) will be measured during treadmill walking using indirect calorimetry (Cosmed K5, Cosmed USA Inc., Chicago, IL). Metabolic rate will be calculated using standard equations during steady-state walking conditions. Measurements will be collected at regular intervals to characterize metabolic demand under different exoskeleton assistance configurations.

  2. Estimated Metabolic Rate Derived From Joint-Space Musculoskeletal Modeling

    Time frame: through study completion, an average of 1 year

    Estimated metabolic rate will be derived from joint-space musculoskeletal models using kinematic and kinetic data collected during treadmill walking. Model-based estimates will be computed on a stride-by-stride basis to provide an indirect estimate of metabolic demand that can be compared with direct measurements from indirect calorimetry.

  3. Estimated Lower-Limb Muscle Activation Derived From Joint-Space Musculoskeletal Modeling

    Time frame: through study completion, an average of 1 year

    Lower-limb muscle activation patterns will be estimated using joint-space musculoskeletal models based on motion capture and ground reaction force data collected during treadmill walking. Estimated muscle activation values will be computed on a stride-by-stride basis to characterize neuromuscular engagement during exoskeleton-assisted gait.

  4. Lower-Limb Muscle Activation Measured by Surface Electromyography During Walking

    Time frame: through study completion, an average of 1 year

    Muscle activation of lower-limb muscles (e.g., tibialis anterior, gastrocnemius medialis, gastrocnemius lateralis, soleus) will be measured during treadmill walking using surface electromyography (Delsys). EMG signals will be collected continuously and processed to quantify muscle activation patterns during exoskeleton-assisted gait.

  5. During treadmill walking trials conducted at a single study visit

    Time frame: through study completion, an average of 1 year

    Controller parameter convergence will be assessed during human-in-the-loop optimization trials by evaluating changes in controller gain and timing parameters across successive walking bouts. Convergence is defined as stabilization of controller parameters within a predefined range during the optimization process.

Study contacts

Contact information is provided by the study sponsor or research team.

Farah Fallahtafi, PhD

CONTACT

[email protected]

4025543075

Sponsors and collaborators

Lead sponsor

University of Nebraska

Other

Collaborators

  • Madonna Rehabilitation Hospital

Registry information

Official study title

Controller Development to Enable Individualized Assistance in Robotic Ankle Exoskeletons

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Feb 6, 2026
Registry last updated
Jun 2, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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