ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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A Truly In-shoe Force Measurement System

Description
In this work, the development of a novel and a truly in-shoe force measurement system is reported. The device consists of a shoe insole with six thin film piezoresistive sensors and the main circuit board. The piezoresistive sensors are used for the measurement of plantar pressure during daily human activities.…

In this work, the development of a novel and a truly in-shoe force measurement system is reported. The device consists of a shoe insole with six thin film piezoresistive sensors and the main circuit board. The piezoresistive sensors are used for the measurement of plantar pressure during daily human activities. The motion sensor mounted on the main circuit board captures kinematic data. In addition, the main circuit board is responsible for the wireless transmission of the data from all the sensors in real-time using BLE protocol. It is housed within the midsole of the shoe, under the medial arch of the foot. The real-time quantitative data and its analyses, enables athletic performance evaluation, biomedical ailment detection, and everyday fitness tracking. A test subject walked 20 steps on a flat surface at a comfortable speed wearing a shoe fitted with the insole and the main circuit board. Measurements were captured using a BLE enabled laptop and the test results were validated for accuracy. From the real-time data captured, the number of steps walked, the speed and the plantar pressure applied can be clearly established. Moreover, additional kinematic data from the motion sensor was captured. Further processing of kinematic data using techniques such as machine learning is essential to get meaningful inferences.
ContributorsBadarinath, Abhishek (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2018