The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their ...physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring. Most current devices generate so many false-positive alerts that devices cannot be used for routine clinical practice. This paper explores principled machine learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. We adopt a one-class support vector machine formulation, proposing a formulation for determining the free parameters of the model using partial area under the ROC curve, a method arising from the unique requirements of performing online analysis with data from patient-worn sensors. There are few clinical evaluations of machine learning techniques in the literature, so we present results from a study at the Oxford University Hospitals NHS Trust devised to investigate the large-scale clinical use of patient-worn sensors for predictive monitoring in a ward with a high incidence of patient mortality. We show that our system can combine routine manual observations made by clinical staff with the continuous data acquired from wearable sensors. Practical considerations and recommendations based on our experiences of this clinical study are discussed, in the context of a framework for personalized monitoring.
Ultraflexible optical devices have been used extensively in next-generation wearable electronics owing to their excellent conformability to human skins. Long-term health monitoring also requires the ...integration of ultraflexible optical devices with an energy-harvesting power source; to make devices self-powered. However, system-level integration of ultraflexible optical sensors with power sources is challenging because of insufficient air operational stability of ultraflexible polymer light-emitting diodes. Here we develop an ultraflexible self-powered organic optical system for photoplethysmogram monitoring by combining air-operation-stable polymer light-emitting diodes, organic solar cells, and organic photodetectors. Adopting an inverted structure and a doped polyethylenimine ethoxylated layer, ultraflexible polymer light-emitting diodes retain 70% of the initial luminance even after 11.3 h of operation under air. Also, integrated optical sensors exhibit a high linearity with the light intensity exponent of 0.98 by polymer light-emitting diode. Such self-powered, ultraflexible photoplethysmogram sensors perform monitoring of blood pulse signals as 77 beats per minute.
Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems ...automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.
Since the beginning of the 21st century, electronic monitoring (EM) has emerged as a cost‐efficient supplement to existing catch monitoring programmes in fisheries. An EM system consists of various ...activity sensors and cameras positioned on vessels to remotely record fishing activity and catches. The first objective of this review was to describe the state of play of EM in fisheries worldwide and to present the insights gained on this technology based on 100 EM trials and 12 fully implemented programmes. Despite its advantages, and its global use for monitoring, progresses in implementation in some important fishing regions are slow. Within this context, the second objective was to discuss more specifically the European experiences gained through 16 trials. Findings show that the three major benefits of EM were as follows: (a) cost‐efficiency, (b) the potential to provide more representative coverage of the fleet than any observer programme and (c) the enhanced registration of fishing activity and location. Electronic monitoring can incentivize better compliance and discard reduction, but the fishing managers and industry are often reluctant to its uptake. Improved understanding of the fisher's concerns, for example intrusion of privacy, liability and costs, and better exploration of EM benefits, for example increased traceability, sustainability claims and market access, may enhance implementation on a larger scale. In conclusion, EM as a monitoring tool embodies various solid strengths that are not diminished by its weaknesses. Electronic monitoring has the opportunity to be a powerful tool in the future monitoring of fisheries, particularly when integrated within existing monitoring programmes.
Spaceborne NO2 column observations from two high‐resolution instruments, Tropospheric Monitoring Instrument (TROPOMI) on board Sentinel‐5 Precursor and Ozone Monitoring Instrument (OMI) on Aura, ...reveal unprecedented NO2 decreases over China, South Korea, western Europe, and the United States as a result of public health measures enforced to contain the coronavirus disease outbreak (Covid‐19) in January–April 2020. The average NO2 column drop over all Chinese cities amounts to −40% relative to the same period in 2019 and reaches up to a factor of ~2 at heavily hit cities, for example, Wuhan, Jinan, while the decreases in western Europe and the United States are also significant (−20% to −38%). In contrast with this, although Iran is also strongly affected by the disease, the observations do not show evidence of lower emissions, reflecting more limited health measures.
Key Points
Satellite NO2 data show substantial decreases by 40% on average over Chinese cities due to lockdown measures against the coronavirus outbreak
Western Europe and United States display robust NO2 decreases in 2020, 20–38% relative to the same period in 2019
Satellite NO2 data above Iran, a region strongly affected by coronavirus, do not show clear evidence of lower emissions
Abstract
Context
Use of continuous glucose monitoring (CGM) is increasing for insulin-requiring patients with diabetes. Although data on glycemic profiles of healthy, nondiabetic individuals exist ...for older sensors, assessment of glycemic metrics with new-generation CGM devices is lacking.
Objective
To establish reference sensor glucose ranges in healthy, nondiabetic individuals across different age groups using a current generation CGM sensor.
Design
Multicenter, prospective study.
Setting
Twelve centers within the T1D Exchange Clinic Network.
Patients or Participants
Nonpregnant, healthy, nondiabetic children and adults (age ≥6 years) with nonobese body mass index.
Intervention
Each participant wore a blinded Dexcom G6 CGM, with once-daily calibration, for up to 10 days.
Main Outcome Measures
CGM metrics of mean glucose, hyperglycemia, hypoglycemia, and glycemic variability.
Results
A total of 153 participants (age 7 to 80 years) were included in the analyses. Mean average glucose was 98 to 99 mg/dL (5.4 to 5.5 mmol/L) for all age groups except those over 60 years, in whom mean average glucose was 104 mg/dL (5.8 mmol/L). The median time between 70 to 140 mg/dL (3.9 to 7.8 mmol/L) was 96% (interquartile range, 93 to 98). Mean within-individual coefficient of variation was 17 ± 3%. Median time spent with glucose levels >140 mg/dL was 2.1% (30 min/d), and median time spent with glucose levels <70 mg/dL (3.9 mmol/L) was 1.1% (15 min/d).
Conclusion
By assessing across age groups in a healthy, nondiabetic population, normative sensor glucose data have been derived and will be useful as a benchmark for future research studies.
This study provides normative sensor glucose data in a healthy, nondiabetic population of children and adults.
Blood glucose level needs to be monitored regularly to manage the health condition of hyperglycemic patients. The current glucose measurement approaches still rely on invasive techniques which are ...uncomfortable and raise the risk of infection. To facilitate daily care at home, in this article, we propose an intelligent, noninvasive blood glucose monitoring system which can differentiate a user's blood glucose level into normal, borderline, and warning based on smartphone photoplethysmography (PPG) signals. The main implementation processes of the proposed system include 1) a novel algorithm for acquiring PPG signals using only smartphone camera videos; 2) a fitting-based sliding window algorithm to remove varying degrees of baseline drifts and segment the signal into single periods; 3) extracting characteristic features from the Gaussian functions by comparing PPG signals at different blood glucose levels; 4) categorizing the valid samples into three glucose levels by applying machine learning algorithms. Our proposed system was evaluated on a data set of 80 subjects. Experimental results demonstrate that the system can separate valid signals from invalid ones at an accuracy of 97.54<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> and the overall accuracy of estimating the blood glucose levels reaches 81.49<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula>. The proposed system provides a reference for the introduction of noninvasive blood glucose technology into daily or clinical applications. This article also indicates that smartphone-based PPG signals have great potential to assess an individual's blood glucose level.
Great leaps forward in scientific understanding are often spurred by innovations in technology. The explosion of miniature sensors that are driving the boom in consumer electronics, such as smart ...phones, gaming platforms, and wearable fitness devices, are now becoming available to ecologists for remotely monitoring the activities of wild animals. While half a century ago researchers were attaching balloons to the backs of seals to measure their movement, today ecologists have access to an arsenal of sensors that can continuously measure most aspects of an animal's state (e.g., location, behavior, caloric expenditure, interactions with other animals) and external environment (e.g., temperature, salinity, depth). This technology is advancing our ability to study animal ecology by allowing researchers to (1) answer questions about the physiology, behavior, and ecology of wild animals in situ that would have previously been limited to tests on model organisms in highly controlled settings, (2) study cryptic or wide-ranging animals that have previously evaded investigation, and (3) develop and test entirely new theories. Here we explore how ecologists are using these tools to answer new questions about the physiological performance, energetics, foraging, migration, habitat selection, and sociality of wild animals, as well as collect data on the environments in which they live.
Goal: Continuous blood pressure (BP) monitoring can provide invaluable information about individuals' health conditions. However, BP is conventionally measured using inconvenient cuff-based ...instruments, which prevents continuous BP monitoring. This paper presents an efficient algorithm, based on the pulse arrival time (PAT), for the continuous and cuffless estimation of the systolic BP, diastolic blood pressure (DBP), and mean arterial pressure (MAP) values. Methods: The proposed framework estimates the BP values through processing vital signals and extracting two types of features, which are based on either physiological parameters or whole-based representation of vital signals. Finally, the regression algorithms are employed for the BP estimation. Although the proposed algorithm works reliably without any need for calibration, an optional calibration procedure is also suggested, which can improve the system's accuracy even further. Results: The proposed method is evaluated on about a thousand subjects using the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) standards. The method complies with the AAMI standard in the estimation of DBP and MAP values. Regarding the BHS protocol, the results achieve grade A for the estimation of DBP and grade B for the estimation of MAP. Conclusion: We conclude that by using the PAT in combination with informative features from the vital signals, the BP can be accurately and reliably estimated in a noninvasive fashion. Significance: The results indicate that the proposed algorithm for the cuffless estimation of the BP can potentially enable mobile health-care gadgets to monitor the BP continuously.
When mounted on the skin, modern sensors, circuits, radios, and power supply systems have the potential to provide clinical-quality health monitoring capabilities for continuous use, beyond the ...confines of traditional hospital or laboratory facilities. The most well-developed component technologies are, however, broadly available only in hard, planar formats. As a result, existing options in system design are unable to effectively accommodate integration with the soft, textured, curvilinear, and time-dynamic surfaces of the skin. Here, we describe experimental and theoretical approaches for using ideas in soft microfluidics, structured adhesive surfaces, and controlled mechanical buckling to achieve ultralow modulus, highly stretchable systems that incorporate assemblies of high-modulus, rigid, state-of-the-art functional elements. The outcome is a thin, conformable device technology that can softly laminate onto the surface of the skin to enable advanced, multifunctional operation for physiological monitoring in a wireless mode.
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