Search

Home detection of urine is of great significance for health assessment and long-term surveillance of some chronic diseases. However, the most reported sensors require complicated pretreatment of samples or operations, thus limiting their application in home detection. Herein, a self-calibrated potentiometric sensor based on polystyrene-Au nanocomposites has been developed for the determination of Ca2+, consisting of a sensing channel, a calibration channel and a reference channel. By transporting Ca2+ ionophore and ion exchanger into polystyrene nanoparticles, the sensing nanocomposites were synthesized with the functions of target recognition and ion-electron transduction. The construction of the sensor required the modification of the nanocomposites by only drop-casting, which greatly simplified the preparation process and thus increased the stability and reproducibility. Furthermore, the calibration channel provided the same basic potential as the sensing channel in real samples. By subtracting the potential value of calibration channel from that of sensing channel could eliminate the interference of background matrix on target response, thus improving the accuracy of the results in the actual urinalysis. The developed self-calibrated potentiometric sensor showed a near Nernstian response to Ca2+ in a range of $1.0\times 10$ −4 - $1.0\times 10$ −2 M with a slope of 26.3 ± 1.7 mV/decade and intercept of 126.2 ± 5.3 mV and the detection limit is $4.2\times 10$ −5 M. In the application to real urine samples, the results were in good agreement with those obtained by ICP-AES, suggesting this self-calibrated potentiometric Ca2+ in urine and long-term health monitoring. [ABSTRACT FROM AUTHOR]

Spatiotemporal data, and more specifically origin–destination matrices, are critical inputs to mobility studies for transportation planning and urban management purposes. Traditionally, high-cost and hard-to-update household travel surveys are used to produce large-scale origin–destination flow information of individuals' whereabouts. In this paper, we propose a methodology to estimate origin–destination (O–D) matrices based on passively-collected cellular network signalling data of millions of anonymous mobile phone users in the Rhône-Alpes region, France. Unlike Call Detail Record (CDR) data which rely only on phone usage, signalling data include all network-based records providing higher spatiotemporal granularity. The explored dataset, which consists of time-stamped traces from 2G and 3G cellular networks with users' unique identifier and cell tower locations, is used to first analyse the cell phone activity degree indicators of each user in order to qualify the mobility information involved in these records. These indicators serve as filtering criteria to identify users whose device transactions are sufficiently distributed over the analysed period to allow studying their mobility. Trips are then extracted from the spatiotemporal traces of users for whom the home location could be detected. Trips have been derived based on a minimum stationary time assumption that enables to determine activity (stop) zones for each user. As a large, but still partial, fraction of the population is observed, scaling is required to obtain an O–D matrix for the full population. We propose a method to perform this scaling and we show that signalling data-based O–D matrix carries similar estimations as those that can be obtained via travel surveys. [ABSTRACT FROM AUTHOR]

Sleep quality is directly related to overall wellness and can reveal symptoms of several diseases. However, the term “sleep quality” still lacks a definitional consensus and is commonly assessed in sleep labs with polysomnography, comprising high costs, or through sleep questionnaires, a highly subjective technique. Multiple methods have been proposed to address the estimation of sleep quality, and devices were developed to conduct the examination in the subject's home. The objective of this paper is to analyze the methods and the devices presented in the literature, assessing the development of objective markers that could lead to an improvement of the subjective sleep experience understanding, leading to developments in the treatment of sleep quality deficits. A systematic review was conducted, selecting research articles published from 2000 to 2018, and two research questions were formulated, specifically, “what methods for sleep quality assessment have been developed” and “what kind of measures are employed by the devices that have been developed to estimate sleep quality.” The research trend for the assessment of sleep quality is based on the sleep macrostructure, and it was verified that despite the convenience and considerable popularity among the consumers of home health monitoring of devices, such as actigraphs, the validity of these tools regarding the estimation of sleep quality still needs to be systematically examined. A detailed resume of the key findings and the identified challenges are presented, ascertaining the main gaps in the current state of the art.

One of the most common sleep-related disorders is obstructive sleep apnea, characterized by a reduction of airflow while breathing during sleep and cause significant health problems. This disorder is mainly diagnosed in sleep labs with polysomnography, involving high costs and stress for the patient. To address this situation multiple systems have been proposed to conduct the examination and analysis in the patient's home, using sensors to detect physiological signals that are examined by algorithms. The objective of this research is to review publications that show the performance of different devices for ambulatory diagnosis of sleep apnea. Commercial systems that were examined by an independent research group and validated research projects were selected. In total 117 articles were analysed, including a total of 50 commercial devices. Each article was evaluated according to diagnostic elements, level of automatisation implemented and the deducted level of evidence and quality rating. Each device was categorized using the SCOPER categorization system, including an additional proposed category, and a final comparison was performed to determine the sensors that provided the best results. [ABSTRACT FROM AUTHOR]

All-solid-state potentiometric sensors that are easily miniaturized and arrayed are widely used in home diagnostics. However, changes in sample matrix compositions tend to affect the basic potential of the potentiometric sensor, and pH of sample could change the response slope, thus affecting the detection reliability. This study takes the detection of glucose in urine as a model to increase the reliability of potentiometric sensors in home detection. PtAu/CNTs nanozyme modified by diboronic acid has been designed, showing better catalytic selectivity for glucose by experiments and theoretical calculations. Moreover, glucose electrode group in a multi-calibration glucose potentiometric sensing array can realize the basic potential calibration of sensing channel by the calibration channel. Meanwhile, the pH electrode group can not only measure the urine pH, but also calibrate the response slope of the glucose electrode group, thus improving the reliability of home detection. [Display omitted] • A multi-calibrated glucose potentiometric (MCGP) sensing array was designed. • The MCGP sensing array can calibrate the basic potential and response slope. • The MCGP sensing array can improve the reliability of home detection. • Diboronic acid-PtAu/CNTs (SDBA-PtAu/CNTs) nanozyme was designed. • SDBA-PtAu/CNTs showed better catalytic selectivity for glucose. [ABSTRACT FROM AUTHOR]

• Accurate home detection is crucial for mobility analysis with CDR data. • We benchmark existing heuristics for home prediction with CDR data. • Our detailed validation data strongly improves on prior validation attempts. • Labelled predictive modelling outperforms the heuristic approach. • Social network variables and models improve the predictive performance. Correctly identifying the home location is crucial for human mobility analysis with telecom data, more specifically call detail record (CDR) data. To that end, multiple heuristics have been developed in literature. Nevertheless, due to the lack of ground truth home location data, no study has thoroughly validated these widely used methods so far. We present a detailed performance analysis of existing home detection heuristics, using a unique dataset that enables this important validation on the lowest level, being the level of the cell tower. Our research indicates that simple heuristics surprisingly outperform their more complex counterparts. The benchmark study revealed that the best heuristic is able to identify the home location with an average error of approximately 4.5 km and selects the correct home tower in 60.69% of the cases. Based on the insights provided by our study, we propose a new heuristic that increases the accuracy to 61% and lowers the average distance error to 4.365 km. Secondly, if the home location is known for possibly only a fraction of the instances, we propose a labelled predictive modelling approach. Adding social network based variables in this predictive model further enhances the predictive performance. Our best model reduces the average distance error to 2.848 km and selects the correct home location in 72.08% of the cases. Furthermore, this result provides an indication of the upper bound for home detection with CDR data. Finally, models that only make use of social network based data are developed as well. Results show that even without using data of the focal individual, these models are able to select the correct home tower in 37.65% of the cases and achieve an average distance error of 8.1 km. [ABSTRACT FROM AUTHOR]