Heterogeneous Structure Omnidirectional Strain Sensor Arrays With Cognitively Learned Neural Networksopen access
- Authors
- Lee, J.H.; Kim, S.H.; Heo, J.S.; Kwak, J.Y.; Park, C.W.; Kim, I.; Lee, Minhyeok; Park, Ho-Hyun; Kim, Y.-H.; Lee, S.J.; Park, Sung Kyu
- Issue Date
- Mar-2023
- Publisher
- John Wiley and Sons Inc
- Keywords
- direction recognition; machine learned strain sensors; omnidirectional strain sensors; strain sensor; stretchable electronics
- Citation
- Advanced Materials, v.35, no.13
- Journal Title
- Advanced Materials
- Volume
- 35
- Number
- 13
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/66381
- DOI
- 10.1002/adma.202208184
- ISSN
- 0935-9648
1521-4095
- Abstract
- Mechanically stretchable strain sensors gain tremendous attention for bioinspired skin sensation systems and artificially intelligent tactile sensors. However, high-accuracy detection of both strain intensity and direction with simple device/array structures is still insufficient. To overcome this limitation, an omnidirectional strain perception platform utilizing a stretchable strain sensor array with triangular-sensor-assembly (three sensors tilted by 45°) coupled with machine learning (ML) -based neural network classification algorithm, is proposed. The strain sensor, which is constructed with strain-insensitive electrode regions and strain-sensitive channel region, can minimize the undesirable electrical intrusion from the electrodes by strain, leading to a heterogeneous surface structure for more reliable strain sensing characteristics. The strain sensor exhibits decent sensitivity with gauge factor (GF) of ≈8, a moderate sensing range (≈0–35%), and relatively good reliability (3000 stretching cycles). More importantly, by employing a multiclass–multioutput behavior-learned cognition algorithm, the stretchable sensor array with triangular-sensor-assembly exhibits highly accurate recognition of both direction and intensity of an arbitrary strain by interpretating the correlated signals from the three-unit sensors. The omnidirectional strain perception platform with its neural network algorithm exhibits overall strain intensity and direction accuracy around 98% ± 2% over a strain range of ≈0–30% in various surface stimuli environments.
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