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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
Included in
Acoustics, Dynamics, and Controls Commons, Biomedical Commons, Biomedical Devices and Instrumentation Commons, Electro-Mechanical Systems Commons, Robotics Commons, Signal Processing Commons