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This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one breathing cycle into three types: break, normal, and wheeze, this study not only identifies abnormal sounds within each breath but also captures comprehensive data on their location, duration, and relationships within entire respiratory cycles, including atypical patterns. This innovative strategy is based on a combination of a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network model, enabling real-time analysis of respiratory sounds. Notably, it stands out for its capacity to handle continuous data, distinguishing it from conventional lung sound classification algorithms. The study utilizes a substantial dataset consisting of 535 respiration cycles from diverse sources, including the Child Sim Lung Sound Simulator, the EMTprep Open-Source Database, Clinical Patient Records, and the ICBHI 2017 Challenge Database. Achieving a classification accuracy of 90%, the exceptional result metrics encompass the identification of each breath cycle and simultaneous detection of the abnormal sound, enabling the real-time wheeze counting of all respirations. This innovative wheeze counter holds the promise of revolutionizing research on predicting lung diseases based on long-term breathing patterns and offers applicability in clinical and non-clinical settings for on-the-go detection and remote intervention of exacerbated respiratory symptoms. [ABSTRACT FROM AUTHOR]

Highly packable and deployable electronics offer a variety of advantages in electronics and robotics by facilitating spatial efficiency. These electronics must endure extreme folding during packaging and tension to maintain a rigid structure in the deployment state. Here, we present foldable and robustly deployable electronics inspired by Plantago, characterized by their tolerance to folding and tension due to integration of tough veins within thin leaf. The primary design approach for these electronics involves a high resistance to folding and tension, achieved through a thin multilayered electronic composite, which manages the neutral axis and incorporates tough Kevlar. The fabricated electronics can be folded up to 750,000 times without malfunctions and endure pulling an object 6667 times heavier than itself without stretching. Such robust electronics can be used as a deployable robot with sensor arrays, demonstrating practical applicability, as it maintains their mechanical and electrical properties during inflation from the packaged state.