Healthy Young Adults
Conditions
Keywords
robotic ankle exoskeleton, human-in-the-loop optimization, wearable robotics, musculoskeletal modeling, muscle activation analysis
Brief 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.
Interventions
DEVICEAdaptive 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.
Sponsors
University of Nebraska
Madonna Rehabilitation Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DEVICE_FEASIBILITY
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
19 Years to 35 Years
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Successful Real-Time Operation of the Robotic Ankle Exoskeleton Controller | 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
| Measure | Time frame | Description |
|---|---|---|
| Net Metabolic Rate During Exoskeleton-Assisted Walking Measured by Indirect Calorimetry | 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. |
| Estimated Metabolic Rate Derived From Joint-Space Musculoskeletal Modeling | 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. |
| Estimated Lower-Limb Muscle Activation Derived From Joint-Space Musculoskeletal Modeling | 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. |
| Lower-Limb Muscle Activation Measured by Surface Electromyography During Walking | 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. |
| During treadmill walking trials conducted at a single study visit | 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. |
Countries
United States
Contacts
CONTACTFarah Fallahtafi, PhD
PRINCIPAL_INVESTIGATORFarah Fallahtafti, PhD
Department of Biomechanics, University of Nebraska at Omaha
Outcome results
None listed