Object Recognition Using Soft Sensors
The development of soft sensors with high sensitivities and good response time is currently researched in great interest, especially in healthcare and soft robotics systems. However, there is a lack of study to equip the soft sensors with a smart feature. Therefore, this study proposes a smart glove...
Saved in:
Main Author: | |
---|---|
Format: | Final Year Project / Dissertation / Thesis |
Published: |
2021
|
Subjects: | |
Online Access: | http://eprints.utar.edu.my/4056/1/3E_1602697_FYP_Report_%2D_CHU_CHEN_LIM.pdf http://eprints.utar.edu.my/4056/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my-utar-eprints.4056 |
---|---|
record_format |
eprints |
spelling |
my-utar-eprints.40562021-06-11T21:28:30Z Object Recognition Using Soft Sensors Lim, Chu Chen TK Electrical engineering. Electronics Nuclear engineering The development of soft sensors with high sensitivities and good response time is currently researched in great interest, especially in healthcare and soft robotics systems. However, there is a lack of study to equip the soft sensors with a smart feature. Therefore, this study proposes a smart glove that can recognise objects using a support vector machine (SVM), a supervised machine learning algorithm. The input to the smart glove is obtained from the integrated resistive strain-based flexible sensors. The characterisation of the resistive sensor was done, and the sensitivity was found to be 0.0145 kΩ/°. The glove is able to recognise three distinct object shapes with an accuracy of up to 92%. Through AI-based object recognition and its high accuracy, this glove provides a promising solution for a low-cost soft sensor solution for the area of soft robotics. 2021 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4056/1/3E_1602697_FYP_Report_%2D_CHU_CHEN_LIM.pdf Lim, Chu Chen (2021) Object Recognition Using Soft Sensors. Final Year Project, UTAR. http://eprints.utar.edu.my/4056/ |
institution |
Universiti Tunku Abdul Rahman |
building |
UTAR Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Tunku Abdul Rahman |
content_source |
UTAR Institutional Repository |
url_provider |
http://eprints.utar.edu.my |
topic |
TK Electrical engineering. Electronics Nuclear engineering |
spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering Lim, Chu Chen Object Recognition Using Soft Sensors |
description |
The development of soft sensors with high sensitivities and good response time is currently researched in great interest, especially in healthcare and soft robotics systems. However, there is a lack of study to equip the soft sensors with a smart feature. Therefore, this study proposes a smart glove that can recognise objects using a support vector machine (SVM), a supervised machine learning algorithm. The input to the smart glove is obtained from the integrated resistive strain-based flexible sensors. The characterisation of the resistive sensor was done, and the sensitivity was found to be 0.0145 kΩ/°. The glove is able to recognise three distinct object shapes with an accuracy of up to 92%. Through AI-based object recognition and its high accuracy, this glove provides a promising solution for a low-cost soft sensor solution for the area of soft robotics. |
format |
Final Year Project / Dissertation / Thesis |
author |
Lim, Chu Chen |
author_facet |
Lim, Chu Chen |
author_sort |
Lim, Chu Chen |
title |
Object Recognition Using Soft Sensors |
title_short |
Object Recognition Using Soft Sensors |
title_full |
Object Recognition Using Soft Sensors |
title_fullStr |
Object Recognition Using Soft Sensors |
title_full_unstemmed |
Object Recognition Using Soft Sensors |
title_sort |
object recognition using soft sensors |
publishDate |
2021 |
url |
http://eprints.utar.edu.my/4056/1/3E_1602697_FYP_Report_%2D_CHU_CHEN_LIM.pdf http://eprints.utar.edu.my/4056/ |
_version_ |
1705060932599676928 |
score |
13.160551 |