Heterogeneous Structure Omnidirectional Strain Sensor Arrays With Cognitively Learned Neural Networks. Issue 13 (17th February 2023) - 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
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. Abstract : An omnidirectional strain sensor array for electronic skin applications, which can detect and predict both the 360° direction and strain intensity based on machine learning, is demonstrated. By adopting a learning algorithm based on the multiclass–multioutput model, highly accurate prediction of strain information is achieved.