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Textile-based electromagnetic soft strain sensors for fast frequency movement and their application in wearable devices measuring multi-axial hip joint angles during running

Resource type
Thesis type
(Thesis) M.A.Sc.
Date created
2020-03-05
Authors/Contributors
Abstract
Wearable in situ multi-axis motion tracking with inductive sensors and machine learning is presented. The production, characterization, and use of a modular and size adjustable inductive sensor for kinematic motion tracking are introduced. The sensor was highly stable and able to track high frequency (>15Hz) and high strain rates (>450%/s). Four sensors were used to fabricate a pair of motion capture shorts. A random forest machine learning algorithm was used to predict the sagittal, transverse, and frontal hip joint angle using the raw signals from the sport shorts strain sensors during running with a cohort of 12 participants against a gold standard optical motion capture system to an accuracy as high as R2 = 0.98 and an RMSE of 2° in all three planes. This present study provides an alternative strain sensor to those typically used (piezoresistive/capacitive) for soft wearable motion capture devices with distinct advantages that could find applications in smart wearable devices, robotics, or direct integration into textiles.
Document
Identifier
etd20754
Copyright statement
Copyright is held by the author.
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Menon, Carlo
Download file Size
etd20754.pdf 2.89 MB

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