EMG-Informed Estimation of Human Walking Dynamics for Assistive Robots
type: CreativeWork
description: The demand for gait rehabilitation is increasing globally, and conventional rehabilitation practices cannot cope with this increase. Robotic-based rehabilitation and assistive robots are alternative solutions for gait rehabilitation, but challenges remain to bring this technology into the clinic. Assistive robots would ideally provide a personalized level of assistance based on an individual’s physical and neurological condition, biomechanics, and muscular fatigue. An assistive robot should also produce a smooth movement based on the user's motion intention. Thus, a prediction of motion intention and corresponding adjustment of the robot actuator forces are the fundamental requirements for a controller of an assistive robot. Electromyography (EMG) signals have been used widely for motion intention estimation. However, most EMG-based models are subject or task-specific, requiring complex calibration. Creating an accurate, EMG-based motion estimation model which is generalizable across individuals and experimental conditions is a major challenge and was the goal of this thesis. The chosen application was to predict the motion and moments of the ankle joint during a range of different walking conditions. As such, a set of experiments was designed to collect motionrelated data from 10 individuals during a wide range of activities. Initially, an artificial neural network was designed to predict ankle moment during constant speed walking based on a list of input time series, including the EMG signals of four muscles from each leg and ankle kinematics. The results helped find the list of most important input time series and the length of information required for ankle moment prediction at each step. Next, a machine learning approach was explored, including feature extraction and selection from the input time series. The selected list of features optimized the model training process and was generalizable across individuals to estimate the ankle moment during constant-speed …
abstract: The demand for gait rehabilitation is increasing globally, and conventional rehabilitation practices cannot cope with this increase. Robotic-based rehabilitation and assistive robots are alternative solutions for gait rehabilitation, but challenges remain to bring this technology into the clinic. Assistive robots would ideally provide a personalized level of assistance based on an individual’s physical and neurological condition, biomechanics, and muscular fatigue. An assistive robot should also produce a smooth movement based on the user's motion intention. Thus, a prediction of motion intention and corresponding adjustment of the robot actuator forces are the fundamental requirements for a controller of an assistive robot. Electromyography (EMG) signals have been used widely for motion intention estimation. However, most EMG-based models are subject or task-specific, requiring complex calibration. Creating an accurate, EMG-based motion estimation model which is generalizable across individuals and experimental conditions is a major challenge and was the goal of this thesis. The chosen application was to predict the motion and moments of the ankle joint during a range of different walking conditions. As such, a set of experiments was designed to collect motionrelated data from 10 individuals during a wide range of activities. Initially, an artificial neural network was designed to predict ankle moment during constant speed walking based on a list of input time series, including the EMG signals of four muscles from each leg and ankle kinematics. The results helped find the list of most important input time series and the length of information required for ankle moment prediction at each step. Next, a machine learning approach was explored, including feature extraction and selection from the input time series. The selected list of features optimized the model training process and was generalizable across individuals to estimate the ankle moment during constant-speed …
author: Zarshenas, Homayoon
license: https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
date published: 2022
date modified: 2026-07-24T01:03:56Z
provider: University of Auckland
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: ResearchSpace@Auckland

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2292/61854