A Deep Learning Approach to Non-linearity in Wearable Stretch Sensors.
A Deep Learning Approach to Non-linearity in Wearable Stretch Sensors.
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DOI：
10.3389/frobt.2019.00027
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发表时间：
2019
影响因子：
3.4
通讯作者：
Miodownik M
中科院分区：
文献类型：
--
作者：
Oldfrey B;Jackson R;Smitham P;Miodownik M
在可穿戴技术中，对柔性拉伸传感器的需求日益增长，以监测真实的时间应力和应变。然而，由于粘弹性和应变率相关效应，开发具有线性响应的拉伸传感器是困难的。我们没有试图设计出完美的线性传感器，而是采用了一种深度学习方法，这种方法可以科普非线性，但仍然可以提供可靠的结果。提出了一种标定高滞后电阻式拉伸传感器的通用方法。我们展示了纺织品和弹性体拉伸传感器的结果，但是我们相信该方法直接适用于传感器材料和制造的任何物理选择，并且易于适应其他传感方法，例如基于电容的传感方法。我们的算法不需要对传感器的物理属性或几何形状进行校准的任何先验知识，这是一个关键的优势，因为可拉伸传感器通常适用于高度复杂的几何形状，其中集成了需要定制制造的电子产品。该方法包括三个阶段。第一阶段需要一个校准步骤，其中使用网络摄像头测量传感器材料的应变，同时通过一组基于arduino的电子设备测量电气响应。在该数据收集阶段，通过在对应于实际使用中的应变和应变率的应变和应变率范围内拉动传感器来手动施加应变。存储电阻与测量的应变和应变速率之间的相关数据。在第二阶段，数据被传递到长短期记忆神经网络（LSTM），该网络使用部分数据集进行训练。然后评估LSTM在给定一系列不可见电阻数据的情况下预测应变状态的能力，并确定最大误差。在第三阶段，传感器从网络摄像头校准设置中移除，并嵌入到可穿戴应用中，其中电阻的实时流是应变的唯一度量-这与建议的用例相对应。高度准确的拉伸拓扑映射实现了三个市售的柔性传感器材料测试。
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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影响因子：
17.1
作者：
Boland, Conor S.;Khan, Umar;Coleman, Jonathan N.
通讯作者：
Coleman, Jonathan N.
DOI：
10.1007/978-1-4419-0052-4_1
发表时间：
2009-01-01
期刊：
R THROUGH EXCEL: A SPREADSHEET INTERFACE FOR STATISTICS, DATA ANALYSIS, AND GRAPHICS
影响因子：
--
作者：
Heiberger, Richard M.;Neuwirth, Erich
通讯作者：
Neuwirth, Erich
影响因子：
19
作者：
Lee, Seulah;Shin, Sera;Lee, Taeyoon
通讯作者：
Lee, Taeyoon
影响因子：
4.1
作者：
Fassler, A.;Majidi, C.
通讯作者：
Majidi, C.
影响因子：
2.2
作者：
Laszczak, P.;Jiang, L.;Zahedi, S.
通讯作者：
Zahedi, S.