Heterogeneous Structure Omnidirectional Strain Sensor Arrays With Cognitively Learned Neural Networks

Bibliographische Detailangaben
Veröffentlicht in:	Advanced materials (Deerfield Beach, Fla.). - 1998. - 35(2023), 13 vom: 15. März, Seite e2208184
1. Verfasser:	Lee, Jun Ho (VerfasserIn)
Weitere Verfasser:	Kim, Seong Hyun, Heo, Jae Sang, Kwak, Jee Young, Park, Chan Woo, Kim, Insoo, Lee, Minhyeok, Park, Ho-Hyun, Kim, Yong-Hoon, Lee, Su Jae, Park, Sung Kyu
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2023
Zugriff auf das übergeordnete Werk:	Advanced materials (Deerfield Beach, Fla.)
Schlagworte:	Journal Article direction recognition machine learned strain sensors omnidirectional strain sensors strain sensor stretchable electronics

Beschreibung
Zusammenfassung:	© 2023 Wiley-VCH GmbH. 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
Beschreibung:	Date Completed 29.03.2023 Date Revised 29.03.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE
ISSN:	1521-4095
DOI:	10.1002/adma.202208184