Oldfrey, Benjamin Matthew;
(2020)
A 3D printing and deep-learning approach to wearable stretch sensors.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
This thesis covers the development of fabrication and calibration methods for soft stretch sensors. A variety of material approaches are discussed that are applicable to soft strain sensing. Printing methods are discussed in detail as a method of producing sensors via cavity channels within inert elastomer substrates that can then be manually filled with active compounds, and the direct syringe printing of nanocomposites. Next is presented a general method for calibrating highly hysteretic resistive stretch sensors and the results for this applied to 1D and 2D sensors. The method involves three-stages. The first stage requires a calibration step in which the strain of the sensor material is measured using a webcam while the electrical response is measured via a set of arduino-based electronics. During this data collection stage, the strain is applied manually by pulling the sensor over a range of strains and strain rates corresponding to the realistic in-use strain and strain rates. In the second stage the data is passed to a bespoke neural network architecture and trained on part of the dataset. The ability of the networks to predict the strain state given a stream of unseen electrical resistance data is then assessed. In the third stage, the sensor system is removed from the camera and used as desired. The final section discusses such use case - the fabrication of bespoke prosthetic socket liners with growth tracking for child prosthetics. Using 3D scanning technology to create the desired shape of the liner, I show my method for producing a castable mould, and the embedding of stretch sensors within it to achieve potential growth tracking of the residual limb.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | A 3D printing and deep-learning approach to wearable stretch sensors |
| Event: | UCL (University College London) |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2020. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. - Some third party copyright material has been removed from this e-thesis. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10099495 |
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