Experimental results from over 138 nocturnal sleep periods from 92 respondents show that our system provides 66-70% accuracy for Deep, Light, Wake, REM sleep stages and 71-77% accuracy for 2-classes (Deep/REM vs. Light/Wake stages) prediction classifications. The proposed alarm system wakes up the user at the moment when Easy Wake (Wake/Light) stage is the most probable.One of the challenges in examining development of newborns is measuring activities which are correlated to their health. Oral feeding is the most important factor in an infant's healthy development. Here, we present a new device that can measure intraoral and expression pressures produced in a newborn's mouth by non-nutritive sucking. We then develop a method to extract time-intervals that a sucking has occurred. To show an application of this device, we use Apgar score as a reference of the general health of newborns, and we evaluate these scores with the non-nutritive sucking patterns demonstrated by the infants. We show that for the pairs of infant with the same background but different Apgar scores, those with lower Apgar scores have lower pressure amplitudes while sucking. Importance of non-nutritive sucking skills in the development of newborns and ease of using our device make it useful for clinical studies of infantile health.Resistive pulse sensors (RPS) are based on the detection principle of partial and non-permanent obstruction of an electrically conducting channel. The integration of RPS in microfluidics has the potential for detections at the molecular level. Current challenges involve limitations in fabrication technology, most notably the finite structure accuracy and fabrication repeatability, which have a direct and strong impact on RPS device performance. In this work, we analyzed the geometrical structure and performance of a nanofabricated RPS device and iteratively used the experimental data to propose an adequate numerical model which also accounts for fabrication imperfections beyond the optical resolution limit. The proposed model for a nano-RPS was validated and able to augment the understating of the structure and operation of a microdevice.Clinical Relevance- This work is part of a greater effort to bring microfluidics devices closer to patients for bedside analysis of blood, or other human fluids, for instance. These devices can potentially perform screening for multiple targets in one sample. New devices often need to go through design, prototyping and bench tests, simulation models as the one presented can increase the chances of the device to get to the market in reduced time.Children, particularly those with atypical or delayed development, have a reduced ability to self-regulate their emotions and behaviour. After a number of anxiety or stress provoking events, this reduced regulatory ability can result in a meltdown. Extrinsic signals of an impending meltdown are often recognised and acted on by clinicians or parents. These external indications are also accompanied by internal physiological changes, such as increase in heart rate, skin electrodermal activity, and skin temperature. These physiological signals may be used to predict impending meltdown events and facilitate earlier and effective carer intervention, especially in complex management cases. We present a preliminary study using a wearable sensor system for continuous monitoring of physiological signals to measure and predict emotional changes in school-aged children. Our models are able to correctly classify the behavioural state of a child with 68% mean global model accuracy and up to 85% for person-dependent models. Prediction of emotion and identification of impending meltdowns will potentially assist parents, carers, teachers and clinicians to manage stress and problem behaviours before they escalate, and support self-management strategies throughout the variety of normal daily life.This paper proposes cuffless and continuous blood pressure estimation utilising Photoplethysmography (PPG) signals and state of the art recurrent network models, namely, Long Short Term Memory and Gated Recurrent Units. The models were validated on wide range of varying blood pressure and PPG signals acquired from the Multiparameter Intelligent Monitoring in Intensive Care database. Many features were extracted from the PPG waveform and several machine learning techniques were employed in an attempt to eliminate collinearity and reduce the size of input feature vector. Consequently, the most effective features for blood pressure estimation were selected. Experimental results show that the accuracy of the proposed methods outperform traditional models applied in the literature. The results satisfy the American National Standards of the Association for the Advancement of Medical Instrumentation.Automatic monitoring of daily living activities can greatly improve the possibility of living autonomously for frail individuals. Pose recognition based on skeleton tracking data is promising for identifying dangerous situations and trigger external intervention or other alarms, while avoiding privacy issues and the need for patient compliance.  https://www.selleckchem.com/products/AZD2281(Olaparib).html  Here we present the benefits of pre-processing Kinect-recorded skeleton data to limit the several errors produced by the system when the subject is not in ideal tracking conditions. The accuracy of our two hidden layers MLP classifier improved from about 82% to over 92% in recognizing actors in four different poses standing, sitting, lying and dangerous sitting.Lactate is an important biomarker with a significant diagnostic and prognostic ability in relation to life-threatening conditions and diseases such as sepsis, diabetes, cancer, pulmonary and kidney diseases, to name a few. The gold standard method for the measurement of lactate relies on blood sampling, which due to its invasive nature, limits the ability of clinicians in frequent monitoring of patients' lactate levels. Evidence suggests that the optical measurement of lactate holds promise as an alternative to blood sampling. However, achieving this aim requires better understanding of the optical behavior of lactate. The present study investigates the potential deviations of absorbance from the Beer-Lambert law in high concentrations of lactate. To this end, a number of nonlinear models namely support vector machines with quadratic, cubic and quartic kernels and radial basis function kernel are compared with the linear principal component regression and linear support vector machine. Interestingly, it is shown that even in extremely high concentrations of lactate (600 mmol/L) in a phosphate buffer solution, the linear models surpass the performance of the other models.