iART: An intelligent assistive robotic therapy system for home-based stroke rehabilitation

robotic rehabilitation;machine learning;reinforcement learning;lstm;stroke

Pareek, Shrey

关键词: robotic rehabilitation;    machine learning;    reinforcement learning;    lstm;    stroke;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/107933/PAREEK-DISSERTATION-2020.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

This dissertation presents the design and evaluation of iART : an Intelligent Assistant for Robotic Therapy. iART is a robot-assisted therapy system designed for home-based upper limb stroke rehabilitation.Stroke is the leading cause of motor impairments and serious long term disability in the United States. These impairments severely limit a patient's ability to lead a normal independent life, and requires them to participate in hospital-based stroke rehabilitation. Recent years have seen the advent of robotic rehabilitation systems as a home-based alternative to hospital-centric stroke therapy. These systems comprise of a robotic device that assists/resists a patient's movements as they perform virtual therapy exercises. In this dissertation, we describe a novel intelligent robotic therapy system that can provide adaptive assistance to patients as they perform virtual therapy tasks. iART comprises of five robot-assisted therapy games/tasks along with an artificial intelligence (AI) agent that adapts the degree of robotic assistance based on a patient's performance. As with any traditional robotic rehabilitation system, iART enables a therapist to remotely monitor a therapy session and suggest changes. Additionally, iART employs an AI that uses surface electromyography (sEMG) and data from the robotic device to monitor a patient's performance/engagement levels in realtime and adapt the system accordingly. The realization of an AI agent to monitor a patient is the key contribution of this work. The dissertation also proposes the use of LSTM-based imitation learning and reinforcement learning towards the realization of an adaptive robotic therapy assistant.This dissertation is divided into three parts. The first part includes a description of existing robot/haptics-based stroke rehabilitation systems. It also introduces the key components of iART and provides a preliminary evaluation of the system. The concept of mental engagement in therapy is introduced as well. Part two delves deeper into the study of mental engagement and its importance towards the success of robotic rehabilitation. It describes a robot-based and an sEMG-based methodology adopted in iART towards monitoring and ensuring patient engagement.Part three explores two novel mechanisms for adaptive assistance viz. learning from demonstration and reinforcement learning. The applications of these paradigms in robotic rehabilitation are fairly nascent and this dissertation serves as one of the initial forays into these domains.