Full metadata
Title
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, 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%.
Date Created
2018-12
Contributors
- Booth, Jayden Charles (Author)
- Chae, Junseok (Thesis director)
- Chen, Ang (Committee member)
- Electrical Engineering Program (Contributor)
- Barrett, The Honors College (Contributor)
Topical Subject
Extent
11 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Series
Academic Year 2018-2019
Handle
https://hdl.handle.net/2286/R.I.50796
Level of coding
minimal
Cataloging Standards
System Created
- 2018-10-30 12:40:46
System Modified
- 2021-08-11 04:09:57
- 3 years 3 months ago
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