FRR: A new strategy for task-agnostic control of robotic exoskeletons by estimating underlying biological effort using deep learning

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

Human mobility and physical activity are strongly correlated with quality of life, participation, depression, and mortality. There is a critical need for improving locomotor performance in ambulation tasks for individuals with physically demanding jobs or individuals with lower limb impairment due to disease, old age, or injury. Robotic exoskeletons have the potential to assist people to increase their community mobility, independence, and quality of life. Lower-limb robotic exoskeletons have outstanding potential to augment and restore human movement; however, current solutions lack personalization to the user and generalization across activities, constraining exoskeleton technology significantly. This project advances exoskeleton technology to support human users in broad and diverse environments. This research project takes a unique approach of using large data driven approaches with AI (artificial intelligence) to enable generalization of exoskeleton control across numerous tasks of daily living including both cyclic walking tasks as well as more discrete tasks such as cutting, jumping, and stepping over an obstacle. The approach includes collecting natural human data wearing exoskeletons across numerous different tasks and creating AI systems that recognize key internal states of the human, such as their lower limb joint efforts. This creates a control system that is extensible across both new human users as well as novel lower limb tasks. This has tremendously broad impact across society, such as improving community and outdoor ambulation in clinical populations, enhancing human capability. This research in the program for Foundational Research in Robotics is supported by the Computer and Information Science and Engineering Directorate's Division of Computer and Network Systems (CISE/CNS), and the Engineering Directorate's Division of Civil, Mechanical and Manufacturing Innovation (ENG/CMMI). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.