Recently, stretchable strain sensor is considered as the promising technology for various application, such as wearable electronics, healthcare monitoring, and robotic skin/arms, due to its mechanical stretchability and multi-axial sensing capabilities. The recent research for stretchable strain sensors have made tremendous efforts to significantly improve key deterministic parameters, including sensitivity, durability, sensing range, and response time. Although various high-performance strain sensors have been demonstrated using a range of mechanistic device structures, most conventional strain sensors are only capable of detecting uniaxial strain and lack the ability to measure directional variable motions. In order to overcome this challenge, novel sensor devices with geometrically engineered architectures, composed of multidimensionally stacked channels or cross-shaped detecting electrodes have been developed. These architectures were developed to address the challenge of achieving multidirectional strain sensing, by employing isotropic conducting pathways. While these advancements are significant, the complicated and intermixed pathways in the channel region can cause unsatisfactory linearity and make it difficult to determine the strain direction under complex stimulation environments. In addition, it is worth noting that many recent studies have utilized machine learning techniques, such as Neural network to classify data obtained by strain sensor. However, in order for Neural network to be applied effectively in industry, optimization processes such as model lightweighting are necessary. Here, we propose a simply designed array of wearable strain sensor capable of detecting and interpreting the direction and intensity of strain through a neural network algorithm trained through cognitive learning. The wearable strain sensor arrays comprises a heterogeneous surface architecture consisting of a stretchable substrate made of polydimethylsiloxane (PDMS), strain-insensitive rigid electrode regions made of polyethylene terephthalate (PET) film, and a strain-sensing channel region composed of multi-walled carbon nanotube (MWCNT) with carbon black (CB) nanofiber dispersed in a PDMS matrix. We utilized a mechanism based on the change in the number of conducting pathways between MWCNT and CB depending on the direction of strain. The use of a heterogeneous device architecture can reduce the negative effects of electrode strain on electrical interruptions, resulting in more reliable strain sensing characteristics. This wearable strain sensor arrays exhibited high sensitivity with a gauge factor of approximately 8, a wide strain sensing range of 0 to 35%, and excellent reliability after 3,000 stretching cycles. To detect strain from any direction, Strain sensor array was made using three sensors arranged at 45-degree tilted angles. Furthermore, to achieve high-accuracy detection of both the intensity and direction of strain, a machine learning algorithm known as a multi-output neural network was employed. A neural network was constructed for strain sensing, which showed an accuracy of over 95% for both strain intensity and direction. In addition, optimization of the strain sensing system’s neural network was done by adjusting the number of sensors and input data dimension to achieve a lightweight model. By employing this method, not only data obtained from the wearable strain sensor arrays be efficiently handled, but also the strain intensity and direction can be accurately classified.

Carbon Based Omnidirectional Wearable Strain Sensor Arrays with Optimized Multi-Output Neural Networks

II: Flexible, Printed and Wearable Electronics and Sensors

technical paper

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EMC 2023

The Electronic Materials Conference (EMC) is the premier annual forum on the preparation, characterization and use of electronic materials. 

JEE YOUNG KWAK

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Carbon Based Omnidirectional Wearable Strain Sensor Arrays with Optimized Multi-Output Neural Networks

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