915.Wearables are quite useful in monitoring crucial parameters during patient-care in medical units and in fitness assessment. They indicate the cardiac system state of a user from the collected data and provides reliable, relevant, real-time health information about a patient. This Work describes an all-in-one wearable based on Giant magnetoresistance (GMR) sensor. The proposed device can compute heart rate (HR), respiration rate (RR), blood pressure (BP) concurrently, utilizing the magneto plethysmography (MPG) signal received from our wrist. It uses an MSP432 microcontroller and a newly developed compact 'Dual GMR- single magnet' positioning architecture.In this paper, we report about the miniaturized and thin wireless wearable percutaneous arterial oxygen saturation (SpO2) sensor module with bendable build-up substrate. As one of an effective approach for miniaturizing and thinning a wearable device, there is a method of applying the folding structure to the module. In order to realize folding structure, we have studied bending characteristics of the bendable build-up substrate and considered for wearable application. In addition, we developed the SpO2 sensor module with folding structure, and then realized the display system of SpO2 sensing data on a tablet.With rapid advancement in wearable biosensor technology, systems capable of real time, continuous and ambulatory monitoring of vital signs are increasingly emerging and their use can potentially help improve patient outcome. Monitoring continuous body temperature offers insights into its trend, allows early detection of fever and is critical in several diseases and clinical conditions including septicemia, infectious disease and others. There is a complex interaction between physiological and ambient parameters including heart rate, respiratory rate, muscle rigors and shivers, diaphoresis, local humidity, clothing, body, skin and ambient temperatures among others. This article presents feasibility analysis of a wireless biosensor patch device called as VitalPatch in capturing this physio-ambient-thermodynamic interaction to determine core body temperature, and details comparative performance assessments using oral thermometer and ingestible pill as reference devices. selleck Based on a study on a cohort of 30 subjects with reference oral temperature, the proposed method showed a bias of 0.1 ± 0.37 °C, mean absolute error (MAE) of 0.29 ± 0.25 °C. Another cohort of 22 subjects with continuous core body temperature pill as reference showed a bias of 0.16 ± 0.38 °C and MAE of 0.42 ± 0.22 °C.Clinical Relevance- Non-invasive, continuous and real time body temperature monitoring can lead to earlier fever detection and provides remote patient monitoring that can result in improved patient and clinical outcome.Myocardial Infarction (MI) is a fatal heart disease that is a leading cause of death. The silent and recurrent nature of MI requires real-time monitoring on a daily basis through wearable devices. Real-time MI detection on wearable devices requires a fast and energy-efficient solution to enable long term monitoring. In this paper, we propose an MI detection methodology using Binary Convolutional Neural Network (BCNN) that is fast, energy-efficient and outperforms the state-of-the- art work on wearable devices. We validate the performance of our methodology on the well known PTB diagnostic ECG database from PhysioNet. Evaluation on real hardware shows that our BCNN is faster and achieves up to 12x energy efficiency compared to the state-of-the-art work.The measurement of physiological parameters in sweat has long been assumed to offer a non-invasive alternative to conventional blood testing. Recently, advances in sensor technology enable the production of printed sweat sensors applicable for the use in wearable devices. However, the remaining challenge is the determination of the physiological correlation between blood and sweat components. In this study, we conducted ammonia measurements in blood and sweat during a stepwise incremental cycle ergometer test in 40 subjects under completely controlled conditions in a clinical environment to determine the correlation between the ammonium concentrations in blood and sweat. Samples were taken for each workload step separately. Sweat was sampled directly from the upper body, blood was taken from an indwelling cannula at the end of each workload step, respectively. For meaningful classification of the measured quantities, blood lactate and heart rate were monitored additionally. The results for blood ammonium concentration show increasing behavior in good accordance with the established indicators for physical exhaustion, whereas sweat ammonium concentration seems to decrease with workload. This is found to be due to dilution, as sweat rate increases. The presented results provide insight in the correlation between blood and sweat parameters and therefore are of high importance for further development of wearable devices.Clinical Relevance-Sweat sensing opens up new possibilities for non-invasive, continuous in-situ monitoring of physiological parameters for healthcare and sports science applications.The development of wearable devices for healthcare monitoring is of primary interest, in particular for homecare applications. But it is challenging to develop an evaluation framework to test and optimize such a device by following a non-invasive protocol. As well established reference devices do exist for capnometry, we propose a protocol to evaluate and compare the performance of the transcutaneous carbon dioxide monitoring wristband that we develop. We present here this protocol, the signal processing pipeline and the data analysis based on signal alignment and intercorrelation study, and the first results on a cohort of 13 healthy subjects. This test allows demonstrating the influence of the device response time and of the carbon dioxide content in the ambient air.Clinical Relevance-The protocol described here allows to test and optimize the new device in clinical conditions simulating hypo and hypercapnia variations on a subject at rest, as it would be the case at home to monitor the health status of chronic respiratory patients, and to compare the performances with reference devices.