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.
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
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,
Single group
Adaptive Torque Control System for Ankle Exoskeleton
DeviceThis 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.
Successful Real-Time Operation of the Robotic Ankle Exoskeleton Controller
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.
Net Metabolic Rate During Exoskeleton-Assisted Walking Measured by Indirect Calorimetry
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
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
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
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.
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University of Nebraska
Lead sponsor
Madonna Rehabilitation Hospital
Collaborator
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