Diabetes is a significant global health issue that demands accurate, accessible, and non-invasive diagnostic methods for effective prevention and treatment. Conventional diagnostic systems are often expensive, painful, time-consuming, and not universally available. In this study, we present a smart system for acetone estimation using simulated breath and a recurrent neural network (RNN) model. The detection system employs a new sensor fabricated from a composite of 1D nanostructured KWO (K₂W₇O₂₂) and 2D nanosheet MXene (Ti₃C₂), designed to measure the chemiresistive response to acetone by mimicking human breath. Resistance data collected by the sensor are used to compute sensitivity values for each acetone concentration (in parts per million, PPM). These values serve as input features for the RNN model, which learns to evaluate health as healthy, high-risk, or diabetic. Trained on acetone concentrations ranging from 0.4 to 2 PPM, the RNN achieves an R² of 99.41% in predicting potential for accurate breath acetone prediction. In future work, we aim to develop a smart device and mobile application based on this model to facilitate real-time diabetes monitoring and prediction.

Wearable sensors and machine learning algorithms are widely used for predicting an individual’s thermal sensation. However, most of the studies are limited to controlled laboratory experiments with inconvenient wearable sensors without considering the dynamic behavior of ambient conditions. In this study, we focused on predicting individual dynamic thermal sensation based on physiological and psychological data. We designed a smart face mask that can measure skin temperature (SKT) and exhaled breath temperature (EBT) and is powered by a rechargeable battery. Real-time human experiments were performed in a subway cabin with twenty male students under natural conditions. The data were collected using a smartphone application, and we created features using the wavelet decomposition technique. The bagged tree algorithm was selected to train the individual model, which showed an overall accuracy and f-1 score of 98.14% and 96.33%, respectively. An individual’s thermal sensation was significantly correlated with SKT, EBT, and associated features.

This study presents a unique method to evaluate the effectiveness of Alternating Pressure Air Mattresses (APAMs) in preventing Pressure Injuries (PIs). We employed a Laser Speckle Blood Flow and Tissue Oxygenation Imager with color image gathering capabilities by Moor Instruments to quantify skin redness and blood perfusion (flux) in a research study involving 29 healthy participants across three surgical support surfaces: foam, gel, and APAM. The methodology combined multimodal imaging with image segmentation and thresholding techniques to correlate visible skin redness with the underlying microcirculatory changes in perfusion (flux). The results showed that APAMs exhibited the most significant initial increase in redness compared to baseline but also demonstrated the faster dissipation of concentrated redness and recovery of blood perfusion (flux) after offloading. A strong correlation (r = 0.63, P < 0.001) was observed between concentrated redness and blood flux for APAMs. This approach goes beyond traditional incidence-based evaluations, providing insights into the dynamic tissue responses to different support surfaces. Our findings suggest that quantifying redness could be a practical and effective way to monitor individual patient responses to support surfaces, potentially leading to more personalized and effective PI prevention strategies.