Next Generation User-Adaptive Wearable Robots

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About this Research Topic

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Background

Powered exoskeletons and robotic prosthesis have enabled new possibilities for mobility restoration among people with spinal cord injury, stroke, amputation, and other neurological conditions that cause gait disorders. Yet, they are limited only to clinical use and many challenges remain for enabling their use in real-world scenarios such as at home or in a public setting. In this context, creating a symbiotic interaction between the exoskeleton and a human with the help of novel actuation, sensors, and control design is the key to maximize desired mobility outcomes.

Stable interaction is a must to achieve a user-intended walking speed, robust balance despite gait perturbations, and modulation of the robotic assistance as per the need. Critical to these performance criteria is the integration of human intent or voluntary effort into exoskeleton control. However, voluntary effort prediction is challenging due to a lack of accurate sensing modalities. Traditional force and kinematic sensors are often prone to misalignment and miss out reading noisy neural drive and skeletal muscle state. Variations in the voluntary effort due to motor relearning or due to relapsing and remitting symptoms can further compound the control challenge. In some instances, the exoskeletons need to be aware of deteriorating muscle force caused by a functional activity or due to the rapid onset of muscle fatigue, such as during functional electrical stimulation (FES).

Current powered exoskeletons and prosthesis are mostly limited to rehabilitation in a clinical setting. Walking conditions are controlled for user’s safety and still need therapist assistance for a perturbation-free walking. The proposed Research Topic invites theoretical and experimental results that inform new actuation, sensing, and robust/adaptive control methods for powered exoskeletons and prostheses that enable independent mobility in a non-clinical setting.

Topics of Interest include, but are not limited to:

• New machine learning techniques for human-in-the-loop optimization of a prosthetic, exoskeleton or shared exoskeleton-FES control.
• Deploying novel sensing technologies that prioritize human effort and minimize FES-induced muscle fatigue.
• Adaptive control of exoskeletons based on biomechanical variables (joint torque, moment, or power, joint stiffness, metabolic energy cost minimization/optimization),
• Assistive robotics for augmentation while optimizing user function for a specific task
• Long-term adjustment of exoskeleton assistance over extended use, i.e., increasing or decreasing assistive levels or differing control modes as someone’s capability improves or declines.

Keywords: Exoskeletons, Wearable Robots, Human-in-the-Loop, Rehabilitation, Functional Electrical Stimulation

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