BROWSE
Detail
Journal Article
Heterogeneous Structure Omnidirectional Strain Sensor Arrays With Cognitively Learned Neural Networks
Cited 53 time in 
Share 
Share
- Authors
- Jun Ho Lee, Seong Hyun Kim, Jae Sang Heo, Jee Young Kwak, Chan Woo Park, Insoo Kim, Minhyeok Lee, Ho-Hyun Park, Yong-Hoon Kim, Su Jae Lee, Sung Kyu Park
- Issue Date
- 2023-03
- Citation
- Advanced Materials, v.35, no.13, pp.1-11
- ISSN
- 0935-9648
- Publisher
- Wiley
- Language
- English
- Type
- Journal Article
- 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 45째) 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?뱈ultioutput 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 its혻neural network algorithm exhibits overall strain intensity and direction accuracy around 98% 짹 2% over a strain range of ≈0??30% in various surface stimuli environments.
- KSP Keywords
- Accurate Recognition, Classification algorithm, Coupled with, Gauge Factor, Heterogeneous structure, Heterogeneous surface, High accuracy, Highly accurate, Machine learning (ml), Network Algorithm, Neural Network Classification