A Deep Learning Approach to Non-linearity in Wearable Stretch Sensors
Copyright © 2019 Oldfrey, Jackson, Smitham and Miodownik..
There is a growing need for flexible stretch sensors to monitor real time stress and strain in wearable technology. However, developing stretch sensors with linear responses is difficult due to viscoelastic and strain rate dependent effects. Instead of trying to engineer the perfect linear sensor we take a deep learning approach which can cope with non-linearity and yet still deliver reliable results. We present a general method for calibrating highly hysteretic resistive stretch sensors. We show results for textile and elastomeric stretch sensors however we believe the method is directly applicable to any physical choice of sensor material and fabrication, and easily adaptable to other sensing methods, such as those based on capacitance. Our algorithm does not require any a priori knowledge of the physical attributes or geometry of the sensor to be calibrated, which is a key advantage as stretchable sensors are generally applicable to highly complex geometries with integrated electronics requiring bespoke manufacture. 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. The correlated data between electrical resistance and measured strain and strain rate are stored. In the second stage the data is passed to a Long Short Term Memory Neural Network (LSTM) which is trained using part of the data set. The ability of the LSTM to predict the strain state given a stream of unseen electrical resistance data is then assessed and the maximum errors established. In the third stage the sensor is removed from the webcam calibration set-up and embedded in the wearable application where the live stream of electrical resistance is the only measure of strain-this corresponds to the proposed use case. Highly accurate stretch topology mapping is achieved for the three commercially available flexible sensor materials tested.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:6 |
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| Enthalten in: |
Frontiers in robotics and AI - 6(2019) vom: 22., Seite 27 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Oldfrey, Ben [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Revised 29.01.2021 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/frobt.2019.00027 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a There is a growing need for flexible stretch sensors to monitor real time stress and strain in wearable technology. However, developing stretch sensors with linear responses is difficult due to viscoelastic and strain rate dependent effects. Instead of trying to engineer the perfect linear sensor we take a deep learning approach which can cope with non-linearity and yet still deliver reliable results. We present a general method for calibrating highly hysteretic resistive stretch sensors. We show results for textile and elastomeric stretch sensors however we believe the method is directly applicable to any physical choice of sensor material and fabrication, and easily adaptable to other sensing methods, such as those based on capacitance. Our algorithm does not require any a priori knowledge of the physical attributes or geometry of the sensor to be calibrated, which is a key advantage as stretchable sensors are generally applicable to highly complex geometries with integrated electronics requiring bespoke manufacture. 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. The correlated data between electrical resistance and measured strain and strain rate are stored. In the second stage the data is passed to a Long Short Term Memory Neural Network (LSTM) which is trained using part of the data set. The ability of the LSTM to predict the strain state given a stream of unseen electrical resistance data is then assessed and the maximum errors established. In the third stage the sensor is removed from the webcam calibration set-up and embedded in the wearable application where the live stream of electrical resistance is the only measure of strain-this corresponds to the proposed use case. Highly accurate stretch topology mapping is achieved for the three commercially available flexible sensor materials tested | ||
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