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Access Type

Open Access

Document Type

thesis

Degree Program

Mechanical Engineering

Degree Type

Master of Science in Mechanical Engineering (M.S.M.E.)

Year Degree Awarded

2013

Month Degree Awarded

May

Keywords

backbone exoskeleton, intent recognition, real-time, sEMG, IMU, decision tree

Abstract

This thesis presents an activity mode intent recognition approach for safe, robust and reliable control of powered backbone exoskeleton. The thesis presents the background and a concept for a powered backbone exoskeleton that would work in parallel with a user. The necessary prerequisites for the thesis are presented, including the collection and processing of surface electromyography signals and inertial sensor data to recognize the user’s activity. The development of activity mode intent recognizer was described based on decision tree classification in order to leverage its computational efficiency. The intent recognizer is a high-level supervisory controller that belongs to a three-level control structure for a powered backbone exoskeleton. The recognizer uses surface electromyography and inertial signals as the input and CART (classification and regression tree) as the classifier. The experimental results indicate that the recognizer can extract the user’s intent with minimal delay. The approach achieves a low recognition error rate and a user-unperceived latency by using sliding overlapped analysis window. The approach shows great potential for future implementation on a prototype backbone exoskeleton.

DOI

https://doi.org/10.7275/4013227

First Advisor

Frank C Sup

COinS