Free-living core body temperature monitoring using a wrist-worn sensor after COVID-19 booster vaccination: a pilot study.

Core body temperature (CBT) is a key vital sign and fever is an important indicator of disease. In the past decade, there has been growing interest for vital sign monitoring technology that may be embedded in wearable devices, and the COVID-19 pandemic has highlighted the need for remote patient monitoring systems. While wrist-worn sensors allow continuous assessment of heart rate and oxygen saturation, reliable measurement of CBT at the wrist remains challenging. In this study, CBT was measured continuously in a free-living setting using a novel technology worn at the wrist and compared to reference core body temperature measurements, i.e., CBT values acquired with an ingestible temperature-sensing pill. Fifty individuals who received the COVID-19 booster vaccination were included. The datasets of 33 individuals were used to develop the CBT prediction algorithm, and the algorithm was then validated on the datasets of 17 participants. Mean observation time was 26.4 h and CBT > 38.0 °C occurred in 66% of the participants. CBT predicted by the wrist-worn sensor showed good correlation to the reference CBT (r = 0.72). Bland-Altman statistics showed an average bias of 0.11 °C of CBT predicted by the wrist-worn device compared to reference CBT, and limits of agreement were - 0.67 to + 0.93 °C, which is comparable to the bias and limits of agreement of commonly used tympanic membrane thermometers. The small size of the components needed for this technology would allow its integration into a variety of wearable monitoring systems assessing other vital signs and at the same time allowing maximal freedom of movement to the user.

Intelligent Posture Monitoring System

With the shift to at-home work due to the Covid-19 pandemic, longer hours are spent sitting in front of a computer without proper ergonomic seating available in most home-office settings. Most home office arrangements often lack the necessary back support needed for prolonged periods of sedentary work. The goal of the proposed system is to automatically track a user's postural positions throughout the day through the use of a non-invasive, wearable system and automatically provide feedback from an algorithm to warn the user to correct or change their poor posture. This is done by placing magnets in the form of a rectangular grid on a shirt as well as an MMR sensor on the chest of the body. The onboard magnetic sensor records the data values from the grid of magnetics, which is then, along with data recorded from the onboard accelerometer, analyzed to determine the position of the user. A trained algorithm recognizes and automatically detects the spinal position of the user from the recorded data points and provides direction to alter their posture. These recommendations act as a warning system and allow the user to self-monitor and correct their own behavior to prevent back and neck pain and reduce the chance of long-lasting damage that can result from poor posture. © 2022 IEEE.

Energy-Efficient Intelligent Pulmonary Auscultation for Post COVID-19 Era Wearable Monitoring Enabled by Two-Stage Hybrid Neural Network

This paper proposes an energy-efficient intelligent pulmonary auscultation system for post COVID-19 era wearable monitoring. This system consists of a tightly coupled two-stage hybrid neural network (TC-TSHNN) model and a corresponding multi-task training paradigm to improve prediction accuracy and generalization ability based on the fact that the number of COVID-19 patients is far less than that of normal people. At the first stage, two-category coarse classification is performed to identify normal and abnormal lung sounds. If the lung sound is abnormal, the second stage would be triggered to perform a four-category fine-grained classification. Besides, discrete wavelet transform is utilized for feature extraction, denoising and data reduction. In addition, advanced lightweight convolutional neural networks are used to reduce the model's computation and improve the model's performance. The hybrid network model can achieve 92% computation reduction and energy saving compared with a direct four-category classification when the input lung sound is normal, which is the majority of cases. Experiment results with inter-patient classification on the COVID-19 lung sound dataset from Tongji Hospital in Wuhan City and the ICBHI'17 dataset show that the proposed TC-TSHNN model can significantly reduce power consumption while maintaining competitive performance against the state-of-the-art work. © 2022 IEEE.

Interdigitated Organic Sensor in Multimodal Facemask's Barrier Integrity and Wearer's Respiration Monitoring.

Facemasks are used as a personal protective equipment in medical services. They became compulsory during the recent COVID-19 pandemic at large. Their barrier effectiveness during various daily activities over time has been the subject of much debate. We propose the fabrication of an organic sensor to monitor the integrity of surgical masks to ensure individuals' health and safety during their use. Inkjet printing of an interdigitated conducting polymer-based sensor on the inner layer of the mask proved to be an efficient and direct fabrication process to rapidly reach the end user. The sensor's integration happens without hampering the mask functionality and preserving its original air permeability. Its resistive response to humidity accumulation allows it to monitor the mask's wetting in use, providing a quantified way to track its barrier integrity and assist in its management. Additionally, it detects the user's respiration rate as a capacitive response to the exhaled humidity, essential in identifying breathing difficulties or a sign of an infection. Respiration evaluations during daily activities show outstanding performance in relation to unspecific motion artifacts and breathing resolution. This e-mask yields an integrated solution for home-based individual monitoring and an advanced protective equipment for healthcare professionals.