Analysis of Preload-Dependent Reversible Mechanical Interlocking Using Beetle-Inspired Wing Locking Device

We report an analysis of preload-dependent reversible interlocking between regularly arrayed, high aspect ratio (AR) polymer micro- and nanofibers. Such a reversible interlocking is inspired from the wing-locking device of a beetle where densely populated microhairs (termed microtrichia) on the cuticular surface form numerous hair-to-hair contacts to maximize lateral shear adhesion. To mimic this, we fabricate various high AR, vertical micro- and nanopillars on a flexible substrate and investigate the shear locking force with different preloads (0.1–10 N/cm²). A simple theoretical model is developed based on the competition between van der Waals (VdW) attraction and deflection forces of pillars, which can explain the preload-dependent maximum deflection, tilting angle, and total shear adhesion force

Predicted probabilities of the 1d CNN+LSTM model.

(a) Raw data from the input and (b) Mel spectrogram. (c) Predicted probabilities from the trained 1D-CNN-LSTM model.</p

Number of hard-labeled segments by database.

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.</div

Schematics of soft-labeling, and hard-labeling process.

The left graph shows 9 seconds of example lung sound and soft labeled annotation. The right graph shows the magnified scope (from 7 to 8.2 seconds) of the left graph with a hard labeled annotation.</p

Research trends in lung sound analysis and related papers.

Research trends in lung sound analysis and related papers.</p

Probability visualization of 3 comparative classifiers.

(A) Random Forest classifier, (B) K-Nearest Neighbors, (C) Multi-layer perceptron. (TIF)</p

Probability visualization of noisy test data.

(A) prediction probabilities of original noisy data, (B) predictions of noise reduced data (The number of standard deviations above the noise is set to ‘0.1’, and mode of stationary set to ‘True’), and (C) different setting of noise reduced data (default setting from library). (TIF)</p

Performance of the trained 1D CNN+LSTM model.

(A) Evaluation in three indicators: Accuracy, F1-score, and ROC-AUC score. (B) Confusion matrix of test data with normalization, and (C) without normalization and calculated sensitivity and specificity of each label.</p

Comparison of wheeze between normal by raw signal and Mel spectrogram.

In some cases, there is coexistence of normal and wheeze sound in isolated breathing cycle. (TIF)</p

Number of lung sound signals by database.

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.</div