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...
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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 |
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Engineering Human joint mobility Range of motion Joint function Conductive fabric Fall prediction Wearable knee brace device Motion capture |
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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 |
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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. |
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Chou Siaw Meng |
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Chou Siaw Meng Kwek, Cherilyn Le Qi |
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Final Year Project |
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Kwek, Cherilyn Le Qi |
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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 |
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Fall prediction using a wearable conductive fabric |
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Fall prediction using a wearable conductive fabric |
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fall prediction using a wearable conductive fabric |
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Nanyang Technological University |
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2024 |
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https://hdl.handle.net/10356/177519 |
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