In Advanced materials (Deerfield Beach, Fla.)
Mechanically stretchable strain sensors have gained tremendous attentions in bio-inspired 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, we propose an omnidirectional strain perception platform utilizing a stretchable strain sensor array with triangular-sensor-assembly (three sensors tilted by 45 °) coupled with machine learning-based neural network classification algorithm. 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 exhibited decent sensitivity with gauge factor of ∼8, a moderate sensing range (0 ∼ 35%), and relatively good reliability (3,000 stretching cycles). More importantly, by employing a multiclass-multioutput behavior-learned cognition algorithm, the stretchable sensor array with triangular-sensor-assembly exhibited 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 neural network algorithm exhibited overall strain intensity and direction accuracy around 98±2% over a strain range of 0 ∼ 30% in various surface stimuli environments. This article is protected by copyright. All rights reserved.
Lee Jun Ho, 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
2023-Jan-05
Direction recognition, Machine learned strain sensor, Omnidirectional strain sensor, strain sensor, stretchable electronics
