Fall prediction using a wearable conductive fabric

Our human joint mobility is vital to perform many activities of daily (ADL) needs, and any reduction in the range of motion (ROM) on the joint could have hinted at some form of lesions within the limb. Hence gathering reliable and precise data of the joint function over an extended period is imperat...

Full description

Saved in:
Bibliographic Details
Main Author: Kwek, Cherilyn Le Qi
Other Authors: Chou Siaw Meng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/177519
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-177519
record_format dspace
spelling sg-ntu-dr.10356-1775192024-06-01T16:51:53Z Fall prediction using a wearable conductive fabric Kwek, Cherilyn Le Qi Chou Siaw Meng School of Mechanical and Aerospace Engineering Rehabilitation Research Institute of Singapore (RRIS) MSMCHOU@ntu.edu.sg Engineering Human joint mobility Range of motion Joint function Conductive fabric Fall prediction Wearable knee brace device Motion capture Our human joint mobility is vital to perform many activities of daily (ADL) needs, and any reduction in the range of motion (ROM) on the joint could have hinted at some form of lesions within the limb. Hence gathering reliable and precise data of the joint function over an extended period is imperative for clinical assessment. However, most of these measurement approaches utilized either non-wearable systems (NWS) or wearable systems (WS) that are too rigid and interfere with limb movement. Therefore, there is a need to look at a new breed of WS measurement devices that the user can wear and track their kinematic data without interfering with the limb motion. Over the past decade, conductive fabrics (CF), has evolved as an emerging trend to measure kinematic parameters due to their comfort & soft property. The aim of this project is to determine the feasibility of the wearable conductive fabrics for fall prediction. Testing methodology focus on a wearable knee brace device made using conductive fabric (CF) to perform human joint motion sensing. Testing data collected using this wearable knee brace device will be measured against golden standard joint motion measurement method, Motion Capture. Motion Capture data collected will be used as ground truth to determine if wearable knee brace device has an acceptable reaction time to perform fall predictions. Key findings from this study shows that there is a delay in reaction time in wearable conductive fabrics as compared to Motion Capture. Insights from this study will determine the accuracy of wearable conductive fabrics knee brace and its suitability to be used for fall prediction. Bachelor's degree 2024-05-29T05:42:44Z 2024-05-29T05:42:44Z 2024 Final Year Project (FYP) Kwek, C. L. Q. (2024). Fall prediction using a wearable conductive fabric. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177519 https://hdl.handle.net/10356/177519 en 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
Human joint mobility
Range of motion
Joint function
Conductive fabric
Fall prediction
Wearable knee brace device
Motion capture
spellingShingle Engineering
Human joint mobility
Range of motion
Joint function
Conductive fabric
Fall prediction
Wearable knee brace device
Motion capture
Kwek, Cherilyn Le Qi
Fall prediction using a wearable conductive fabric
description Our human joint mobility is vital to perform many activities of daily (ADL) needs, and any reduction in the range of motion (ROM) on the joint could have hinted at some form of lesions within the limb. Hence gathering reliable and precise data of the joint function over an extended period is imperative for clinical assessment. However, most of these measurement approaches utilized either non-wearable systems (NWS) or wearable systems (WS) that are too rigid and interfere with limb movement. Therefore, there is a need to look at a new breed of WS measurement devices that the user can wear and track their kinematic data without interfering with the limb motion. Over the past decade, conductive fabrics (CF), has evolved as an emerging trend to measure kinematic parameters due to their comfort & soft property. The aim of this project is to determine the feasibility of the wearable conductive fabrics for fall prediction. Testing methodology focus on a wearable knee brace device made using conductive fabric (CF) to perform human joint motion sensing. Testing data collected using this wearable knee brace device will be measured against golden standard joint motion measurement method, Motion Capture. Motion Capture data collected will be used as ground truth to determine if wearable knee brace device has an acceptable reaction time to perform fall predictions. Key findings from this study shows that there is a delay in reaction time in wearable conductive fabrics as compared to Motion Capture. Insights from this study will determine the accuracy of wearable conductive fabrics knee brace and its suitability to be used for fall prediction.
author2 Chou Siaw Meng
author_facet Chou Siaw Meng
Kwek, Cherilyn Le Qi
format Final Year Project
author Kwek, Cherilyn Le Qi
author_sort Kwek, Cherilyn Le Qi
title Fall prediction using a wearable conductive fabric
title_short Fall prediction using a wearable conductive fabric
title_full Fall prediction using a wearable conductive fabric
title_fullStr Fall prediction using a wearable conductive fabric
title_full_unstemmed Fall prediction using a wearable conductive fabric
title_sort fall prediction using a wearable conductive fabric
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/177519
_version_ 1814047066638254080