Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention.

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.

Vital signal sensing and manipulation of a microscale organ with a multifunctional soft gripper.

Soft grippers that incorporate functional materials are important in the development of mechanically compliant and multifunctional interfaces for both sensing and stimulating soft objects and organisms. In particular, the capability for firm and delicate grasping of soft cells and organs without mechanical damage is essential to identify the condition of and monitor meaningful biosignals from objects. Here, we report a millimeter-scale soft gripper based on a shape memory polymer that enables manipulating a heavy object (payload-to-weight ratio up to 6400) and grasping organisms at the micro/milliscale. The silver nanowires and crack-based strain sensor embedded in this soft gripper enable simultaneous measurement of the temperature and pressure on grasped objects and offer temperature and mechanical stimuli for the grasped object. We validate our miniaturized soft gripper by demonstrating that it can grasp a snail egg while simultaneously applying a moderate temperature stimulation to induce hatching process and monitor the heart rate of a newborn snail. The results present the potential for widespread utility of soft grippers in the area of biomedical engineering, especially in the development of conditional or closed-loop interfacing with microscale biotissues and organisms.

FEP Encapsulated Crack-Based Sensor for Measurement in Moisture-Laden Environment.

Among many flexible mechanosensors, a crack-based sensor inspired by a spider's slit organ has received considerable attention due to its great sensitivity compared to previous strain sensors. The sensor's limitation, however, lies on its vulnerability to stress concentration and the metal layers' delamination. To address this issue of vulnerability, we used fluorinated ethylene propylene (FEP) as an encapsulation layer on both sides of the sensor. The excellent waterproof and chemical resistance capability of FEP may effectively protect the sensor from damage in water and chemicals while improving the durability against friction.

Study on the oxidation of copper nanowire network electrodes for skin mountable flexible, stretchable and wearable electronics applications.

Copper nanowires (Cu NWs) are suitable material as an electrode for flexible, stretchable and wearable devices due to their excellent mechanical properties, high transparency, good conductivity, and low cost, but oxidation problem limits their practical use and application. In order to use Cu NWs as an electrode for advanced flexible, stretchable and wearable devices attached directly to the skin, the influence of the body temperature on the oxidation of Cu NWs needs to be investigated. In this paper, the oxidation behavior of Cu NWs at high temperature (more than 80 °C) as well as body temperature is studied which has been remained largely questionable to date, and an effective encapsulation method is proposed to prevent the oxidation of Cu NWs electrode in the range of body temperatures.