Chest‐Laminated and Sweat‐Permeable E‐Skin for Speech and Motion Artifact‐Insensitive Cough Detection

Yang, Xiaopeng; Zhang, Menglun; Gong, Shaobo; Sun, Mingchao; Xie, Mengying; Niu, Pengfei

doi:10.1002/admt.202201043

Cited by 7 publications

(2 citation statements)

References 54 publications

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“…As an alternative to microphones, other novel sensors like chest-laminated electronic skin (e-skin) have been proposed for reliable cough detection [19]. In this line, automatic cough detection methods using accelerometers are being explored to enable continuous monitoring of cough frequency and severity.…”

Section: Introductionmentioning

confidence: 99%

Cough Detection Using Acceleration Signals and Deep Learning Techniques

Sanchez-Morillo,

Sales-Lerida,

Priego-Torres

et al. 2024

Electronics

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Cough is a frequent symptom in many common respiratory diseases and is considered a predictor of early exacerbation or even disease progression. Continuous cough monitoring offers valuable insights into treatment effectiveness, aiding healthcare providers in timely intervention to prevent exacerbations and hospitalizations. Objective cough monitoring methods have emerged as superior alternatives to subjective methods like questionnaires. In recent years, cough has been monitored using wearable devices equipped with microphones. However, the discrimination of cough sounds from background noise has been shown a particular challenge. This study aimed to demonstrate the effectiveness of single-axis acceleration signals combined with state-of-the-art deep learning (DL) algorithms to distinguish intentional coughing from sounds like speech, laugh, or throat noises. Various DL methods (recurrent, convolutional, and deep convolutional neural networks) combined with one- and two-dimensional time and time–frequency representations, such as the signal envelope, kurtogram, wavelet scalogram, mel, Bark, and the equivalent rectangular bandwidth spectrum (ERB) spectrograms, were employed to identify the most effective approach. The optimal strategy, which involved the SqueezeNet model in conjunction with wavelet scalograms, yielded an accuracy and precision of 92.21% and 95.59%, respectively. The proposed method demonstrated its potential for cough monitoring. Future research will focus on validating the system in spontaneous coughing of subjects with respiratory diseases under natural ambulatory conditions.

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Section: Introductionmentioning

confidence: 99%

Cough Detection Using Acceleration Signals and Deep Learning Techniques

Sanchez-Morillo,

Sales-Lerida,

Priego-Torres

et al. 2024

Electronics

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“…[19] In addition, tight contact with complex surfaces cannot be guaranteed, resulting in monitoring failure due to delamination in the area of interest. [20] Researchers have made significant efforts toward realizing the low modulus of e-skin systems. Two of the most effective approaches benefit from fundamental research on high-performance materials and developments in the field of structural mechanics.…”

mentioning

confidence: 99%

Highly Sensitive Piezoelectric E‐Skin Design Based on Electromechanical Coupling Concept

Yang

Zhang

Xie

et al. 2023

Adv Elect Materials

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Stretchable electronic skin (e‐skin) paves the way for applications that exceed the scope of intrinsic rigid devices and hard‐to‐stretch sensors. The broad application range of flexible e‐skins benefits from device architectures that can simultaneously provide mechanical flexibility and superior sensitivity. Classic fractal design provides a simple architecture to achieve the desired flexibility through structural design for improved wear comfort, but at the expense of sensor sensitivity. In this study, the proposed method addresses the trade‐off between stretchability and sensitivity in fractal design. A high‐sensitivity e‐skin is obtained by eliminating the effect of negative charge on the output by applying the concept of electromechanical coupling. This concept for designing e‐skins with high sensitivity is validated through the delicate patterning of hard‐to‐stretch functional materials. Further, human speech signals are acquired through the integration of e‐skin with signal processing circuits, and speech pattern recognition is realized using machine learning. The stretchable e‐skin with an enhanced gauge factor illustrates the wider application of this concept for improving the sensitivity of stretchable electronic functional materials.

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