User intention detection algorithm for a lower limb exoskeleton using kinematics and kinetics data

Lower limb exoskeletons for rehabilitation purposes can help address the need for rehabilitation within victims of lower limb mobility impairments. To provide effective control over the motion of the exoskeleton, an accurate gait phase detection system must be utilized to identify the intent of the...

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Main Author: Boey, Chung Yin
Other Authors: Ang Wei Tech
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176644
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1766442024-05-25T16:51:57Z User intention detection algorithm for a lower limb exoskeleton using kinematics and kinetics data Boey, Chung Yin Ang Wei Tech School of Mechanical and Aerospace Engineering Rehabilitation Research Institute of Singapore (RRIS) WTAng@ntu.edu.sg Engineering Gait phase detection Lower limb orthosis Assistive device Lower limb exoskeletons for rehabilitation purposes can help address the need for rehabilitation within victims of lower limb mobility impairments. To provide effective control over the motion of the exoskeleton, an accurate gait phase detection system must be utilized to identify the intent of the user and provide the necessary assistance to guide the user along the intended trajectory. The Hidden Markov Model (HMM) is a commonly used machine learning method to predict the gait phase of the user based on sensor data. In this experiment, Inertial Measurement Units are used to measure lower limb angles, angular velocity, and angular acceleration in the sagittal plane, and Force Sensitive Resistors are used to determine contact points of the user’s feet. Data from 4 subjects were collected and labelled using a threshold-based algorithm to train an HMM. Subjects were tasked to perform various walking activities on level ground, obstacle crossing, inclined surfaces, and staircase. Results from the HMM were validated with the labelled data. 4 gait phases were detected by the algorithm, Foot Flat (FF), Heel Off (HO), Swing (SW), and Initial Contact (IC). Results show possible omission of the FSR and only using IMUs for an HMM algorithm to predict gait phases for a user wearing an exoskeleton in real-time. Bachelor's degree 2024-05-19T23:55:47Z 2024-05-19T23:55:47Z 2024 Final Year Project (FYP) Boey, C. Y. (2024). User intention detection algorithm for a lower limb exoskeleton using kinematics and kinetics data. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176644 https://hdl.handle.net/10356/176644 en B003 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Gait phase detection
Lower limb orthosis
Assistive device
spellingShingle Engineering
Gait phase detection
Lower limb orthosis
Assistive device
Boey, Chung Yin
User intention detection algorithm for a lower limb exoskeleton using kinematics and kinetics data
description Lower limb exoskeletons for rehabilitation purposes can help address the need for rehabilitation within victims of lower limb mobility impairments. To provide effective control over the motion of the exoskeleton, an accurate gait phase detection system must be utilized to identify the intent of the user and provide the necessary assistance to guide the user along the intended trajectory. The Hidden Markov Model (HMM) is a commonly used machine learning method to predict the gait phase of the user based on sensor data. In this experiment, Inertial Measurement Units are used to measure lower limb angles, angular velocity, and angular acceleration in the sagittal plane, and Force Sensitive Resistors are used to determine contact points of the user’s feet. Data from 4 subjects were collected and labelled using a threshold-based algorithm to train an HMM. Subjects were tasked to perform various walking activities on level ground, obstacle crossing, inclined surfaces, and staircase. Results from the HMM were validated with the labelled data. 4 gait phases were detected by the algorithm, Foot Flat (FF), Heel Off (HO), Swing (SW), and Initial Contact (IC). Results show possible omission of the FSR and only using IMUs for an HMM algorithm to predict gait phases for a user wearing an exoskeleton in real-time.
author2 Ang Wei Tech
author_facet Ang Wei Tech
Boey, Chung Yin
format Final Year Project
author Boey, Chung Yin
author_sort Boey, Chung Yin
title User intention detection algorithm for a lower limb exoskeleton using kinematics and kinetics data
title_short User intention detection algorithm for a lower limb exoskeleton using kinematics and kinetics data
title_full User intention detection algorithm for a lower limb exoskeleton using kinematics and kinetics data
title_fullStr User intention detection algorithm for a lower limb exoskeleton using kinematics and kinetics data
title_full_unstemmed User intention detection algorithm for a lower limb exoskeleton using kinematics and kinetics data
title_sort user intention detection algorithm for a lower limb exoskeleton using kinematics and kinetics data
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176644
_version_ 1800916210149228544