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Advancing Gait Identification and An...

Vlach, Jared A.

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Advancing Gait Identification and Analysis with a Soft, Compact 3D Force Sensing Insole System.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advancing Gait Identification and Analysis with a Soft, Compact 3D Force Sensing Insole System./
Author:	Vlach, Jared A.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	187 p.
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Biomedical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31300319
ISBN:	9798382823744

Advancing Gait Identification and Analysis with a Soft, Compact 3D Force Sensing Insole System.
Vlach, Jared A.

Advancing Gait Identification and Analysis with a Soft, Compact 3D Force Sensing Insole System. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 187 p.

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--University of Nevada, Reno, 2024.

The field of smart insoles has seen tremendous improvement throughout the last 25 years, driven primarily by the miniaturization of technology and introduction of novel sensing methods. These innovations have broadened the scope of applications in areas such as general health monitoring, disease identification, and sports analysis. However, despite these strides, many research-driven insole advancements still suffer from limitations, including rigidity, bulkiness, high costs, discomfort, and a focus on measurements predominantly in the normal direction. This research targets to address these shortcomings by developing a small, soft, flexible, cost-effective, resilient, and ergonomically designed insole system. Moreover, the goal is to optimize sensing locations to enable three-dimensional measurement capabilities, encompassing both normal and shear stress. Three distinct iterations of insole designs were tested during this study, each targeting improvements across the aforementioned parameters. The initial design underwent experimental validation, achieving an average accuracy of 89.51% with a maximum normalized error metric of 16.41%. Subsequent iterations focused on enhancing durability and practical usability. The second design exhibited promising results, demonstrating an average accuracy of 92.79% with a maximum normalized error metric of 12.22%. Notably, it showcased expected performance across various gait patterns, capturing data from three different force directions at each analysis point. The third design, while still undergoing refinement, displayed considerable improvements, boasting an average accuracy of 94.35% with a maximum normalized error metric of 7.79%. Ongoing research is addressing early limiting factors encountered during practical testing, with the aim of further enhancing performance and robustness.Overall, these designs promote the development of compact, comfortable insole systems featuring optimized sensing capabilities for comprehensive force detection in three dimensions. Moreover, the cost-effectiveness of these designs underscores their accessibility and potential for widespread adoption. Future investigations will focus on leveraging smaller sensors for refined sensing locations and enhancing adhesive properties to bolster the durability of miniaturized sensors.

ISBN: 9798382823744Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Gait analysis

Advancing Gait Identification and Analysis with a Soft, Compact 3D Force Sensing Insole System.
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245 1 0 $a Advancing Gait Identification and Analysis with a Soft, Compact 3D Force Sensing Insole System.
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300 $a 187 p.
500 $a Source: Masters Abstracts International, Volume: 85-12.
500 $a Advisor: Shen, Yantao.
502 $a Thesis (M.S.)--University of Nevada, Reno, 2024.
520 $a The field of smart insoles has seen tremendous improvement throughout the last 25 years, driven primarily by the miniaturization of technology and introduction of novel sensing methods. These innovations have broadened the scope of applications in areas such as general health monitoring, disease identification, and sports analysis. However, despite these strides, many research-driven insole advancements still suffer from limitations, including rigidity, bulkiness, high costs, discomfort, and a focus on measurements predominantly in the normal direction. This research targets to address these shortcomings by developing a small, soft, flexible, cost-effective, resilient, and ergonomically designed insole system. Moreover, the goal is to optimize sensing locations to enable three-dimensional measurement capabilities, encompassing both normal and shear stress. Three distinct iterations of insole designs were tested during this study, each targeting improvements across the aforementioned parameters. The initial design underwent experimental validation, achieving an average accuracy of 89.51% with a maximum normalized error metric of 16.41%. Subsequent iterations focused on enhancing durability and practical usability. The second design exhibited promising results, demonstrating an average accuracy of 92.79% with a maximum normalized error metric of 12.22%. Notably, it showcased expected performance across various gait patterns, capturing data from three different force directions at each analysis point. The third design, while still undergoing refinement, displayed considerable improvements, boasting an average accuracy of 94.35% with a maximum normalized error metric of 7.79%. Ongoing research is addressing early limiting factors encountered during practical testing, with the aim of further enhancing performance and robustness.Overall, these designs promote the development of compact, comfortable insole systems featuring optimized sensing capabilities for comprehensive force detection in three dimensions. Moreover, the cost-effectiveness of these designs underscores their accessibility and potential for widespread adoption. Future investigations will focus on leveraging smaller sensors for refined sensing locations and enhancing adhesive properties to bolster the durability of miniaturized sensors.
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653 $a Self-decoupling method
653 $a Signal analysis
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710 2 $a University of Nevada, Reno. $b Biomedical Engineering. $3 1017910
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793 $a English
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