Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Wireless Machine-learning Enabled Reconfigurable ""Button-type"" Pressure Sensors for Gait Analysis

Description
This paper introduces a wireless reconfigurable “button-type” pressure sensor system, via machine learning, for gait analysis application. The pressure sensor system consists of an array of independent button-type pressure sensing units interfaced with a remote computer. The pressure sensing unit contains pressure-sensitive resistors, readout electronics, and a wireless Bluetooth module,…

This paper introduces a wireless reconfigurable “button-type” pressure sensor system, via machine learning, for gait analysis application. The pressure sensor system consists of an array of independent button-type pressure sensing units interfaced with a remote computer. The pressure sensing unit contains pressure-sensitive resistors, readout electronics, and a wireless Bluetooth module, which are assembled within footprint of 40 × 25 × 6mm3. The small-footprint, low-profile sensors are populated onto a shoe insole, like buttons, to collect temporal pressure data. The pressure sensing unit measures pressures up to 2,000 kPa while maintaining an error under 10%. The reconfigurable pressure sensor array reduces the total power consumption of the system by 50%, allowing extended period of operation, up to 82.5 hrs. A robust machine learning program identifies the optimal pressure sensing units in any given configuration at an accuracy of up to 98%.
ContributorsBooth, Jayden Charles (Author) / Chae, Junseok (Thesis director) / Chen, Ang (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12