NCT07393971

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

Controller Development to Enable Individualized Assistance in Robotic Ankle Exoskeletons

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 6 (estimated)
• Sponsor: University of Nebraska · Academic / Other
• Sex: All
• Age: 19 Years – 35 Years
• Healthy volunteers: Accepted

Summary

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.

Detailed description

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.

Conditions

• Healthy Young Adults

Interventions

Type	Name	Description
DEVICE	Adaptive Torque Control System for Ankle Exoskeleton	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.

Timeline

• Start date: 2026-02-01
• Primary completion: 2026-08-01
• Completion: 2026-12-01
• First posted: 2026-02-06
• Last updated: 2026-02-06

Locations

1 site across 1 country: United States

Regulatory

• FDA-regulated device study