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Journal of Diabetes Science and... Mar 2015Hypoglycemia in infants is common, is difficult to recognize, and may lead to permanent neurologic impairment. Low glucose concentrations and high hematocrits in...
Hypoglycemia in infants is common, is difficult to recognize, and may lead to permanent neurologic impairment. Low glucose concentrations and high hematocrits in newborns pose significant analytic challenges for whole blood glucose meters. Three Bayer glucose monitoring systems were evaluated using 211 blood samples from 162 neonates (age range 5 hours to 29 days, median age 3 days). Hematocrit and whole blood glucose were determined in heparinized whole blood, and plasma glucose was determined using the Roche Cobas 6000. Accuracy was evaluated against plasma concentrations using ISO 15197:2013 and CLSI POCT 12-A3 criteria. Glucose imprecision on the Cobas system was 1.8-2.6% (CV) from 26-610 mg/dL. Imprecision across all meter systems was 2.8% (CV) at 130 mg/dL. Glucose concentrations, hematocrit, and total bilirubin ranged from 20-150 mg/dL, 18 -75%, and 0.5-19.6 mg/dL, respectively. Linear regression analysis of whole blood versus plasma for the 3 combined systems yielded an average slope of 1.06 and correlation coefficient greater than 0.980. Bias between the Contour and Cobas was not significantly correlated with hematocrit. Greater than 99% of meter results were within 15 mg/dL and 20% of plasma results at glucose concentrations ≤ 75 and > 75 mg/dL, respectively. Of meter results, 97% were within 12.5 mg/dL of plasma results at concentrations ≤ 100 mg/dL, while 96% of meter results were within 12.5% of plasma at concentrations > 100 mg/dL. The Bayer CONTOUR Blood Glucose Monitoring Systems exceed ISO 15197:2013 and CLSI criteria in neonatal blood samples.
Topics: Blood Glucose; Female; Humans; Infant, Newborn; Male; Monitoring, Physiologic; Point-of-Care Systems
PubMed: 25377056
DOI: 10.1177/1932296814557669 -
Sensors (Basel, Switzerland) Jul 2023Acoustic and optical sensing modalities represent two of the primary sensing methods within underwater environments, and both have been researched extensively in...
Acoustic and optical sensing modalities represent two of the primary sensing methods within underwater environments, and both have been researched extensively in previous works. Acoustic sensing is the premier method due to its high transmissivity in water and its relative immunity to environmental factors such as water clarity. Optical sensing is, however, valuable for many operational and inspection tasks and is readily understood by human operators. In this work, we quantify and compare the operational characteristics and environmental effects of turbidity and illumination on two commercial-off-the-shelf sensors and an additional augmented optical method, including: a high-frequency, forward-looking inspection sonar, a stereo camera with built-in stereo depth estimation, and color imaging, where a laser has been added for distance triangulation. The sensors have been compared in a controlled underwater environment with known target objects to ascertain quantitative operation performance, and it is shown that optical stereo depth estimation and laser triangulation operate satisfactorily at low and medium turbidites up to a distance of approximately one meter, with an error below 2 cm and 12 cm, respectively; acoustic measurements are almost completely unaffected up to two meters under high turbidity, with an error below 5 cm. Moreover, the stereo vision algorithm is slightly more robust than laser-line triangulation across turbidity and lighting conditions. Future work will concern the improvement of the stereo reconstruction and laser triangulation by algorithm enhancement and the fusion of the two sensing modalities.
PubMed: 37514869
DOI: 10.3390/s23146575 -
Sensors (Basel, Switzerland) Jun 2023Accurate detection and timely treatment of component defects in substations is an important measure to ensure the safe operation of power systems. In this study, taking...
Accurate detection and timely treatment of component defects in substations is an important measure to ensure the safe operation of power systems. In this study, taking substation meters as an example, a dataset of common meter defects, such as a fuzzy or damaged dial on the meter and broken meter housing, is constructed from the images of manual inspection in power systems. There are several challenges involved in accurately detecting defects in substation meter images, such as the complex background, different meter sizes and large differences in the shapes of meter defects. Therefore, this paper proposes the PHAM-YOLO (Parallel Hybrid Attention Mechanism You Only Look Once) network for automatic detection of substation meter defects. In order to make the network pay attention to the key areas against the complex background of the meter defect images and the differences between different defect features, a Parallel Hybrid Attention Mechanism (PHAM) module is designed and added to the backbone of YOLOv5. PHAM integration of local and non-local correlation information can highlight these differences while remaining focused on the meter defect features. To improve the expressive ability of the feature map, a Spatial Pyramid Pooling Fast (SPPF) module is introduced, which pools the input feature map using a continuous fixed convolution kernel, fusing the feature maps of different receptive fields. Bounding box regression (BBR) is the key way to determine object positioning performance in defect detection. EIOU (Efficient Intersection over Union) is, therefore, introduced as a boundary loss function to solve the ambiguity of the CIOU (Complete Intersection Over Union) loss function, making the BBR regression more accurate. The experimental results show that the Average Precision Mean (mAP), Precision (P) and Recall (R) of the proposed PHAM-YOLO network in the dataset are 78.3%, 78.3%, and 79.9%, respectively, with mAP being improved by 2.7% compared to the original model and higher than SSD, Fast R-CNN, etc.
Topics: Algorithms; Records; Spine
PubMed: 37447900
DOI: 10.3390/s23136052 -
PeerJ. Computer Science 2023Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by...
Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by professionals. To improve work efficiency and verification accuracy, this research integrates machine learning into the scheme of online verification and management of gateway meter flow in the power system. The approach begins by applying the Faster Region Convolutional Neural Network (Faster-RCNN) model and the Single Shot MultiBox Detector (SSD) model to the recognition system for dial readings. Then, the collected measurement data is pre-processed, excluding data collected under light load conditions. Next, an estimation error model and a solution equation for the electricity meter are established based on the pre-processed data. The operation error of the electricity meter is estimated, and the estimation accuracy is verified using the limited memory recursive least squares algorithm (LMRLSA). Furthermore, business assistant decision-making is carried out by combining the remote verification results with the estimation outcomes. The proposed dial reading recognition system is tested using 528 images of meter readings, achieving an accuracy of 98.49%. In addition, the influence of various parameters on the error results of the electricity meter is also explored. The results demonstrate that a memory length ranging from 600 to 1,200 and a line loss error of less than 5% yield the most suitable accuracy for estimating the electricity meter error. Meanwhile, it is advisable to remove measurement data collected under light load to avoid unnecessary checks. The experiments manifest that the proposed algorithm can properly eliminate the influence of old measurement data on the error parameter estimation, thereby enhancing the accuracy of the estimation. The adjustment of the memory length ensures real-time performance in estimating meter errors and enables online monitoring. This research has certain reference significance for achieving the online verification and management of gateway meter flow in the power system.
PubMed: 38077539
DOI: 10.7717/peerj-cs.1581 -
Sensors (Basel, Switzerland) Aug 2023Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate...
Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate electric energy monitoring. In increasingly complex and interconnected electric systems, this device will be essential for a wide range of applications, such as smart grids, by introducing a real-time energy monitoring system. In light of this, smart meters can offer greater opportunities for sustainable and efficient energy use and improve the utilization of energy sources, especially those that are nonrenewable. According to the 2020 International Energy Agency (IEA) report, nonrenewable energy sources represent 65% of the global supply chain. The smart meter developed in this work is based on the ESP32 microcontroller and easily accessible components since it includes a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of developing the smart meters was to enhance the software and simplify the hardware. Unlike traditional meters that calculate electrical parameters by means of complex circuits in hardware, this project performed the calculations directly on the microcontroller. This procedure reduced the complexity of the hardware by simplifying the meter design. Owing to the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters could be achieved without the need for additional circuits. This software-driven approach with simplified hardware led to benefits, such as reduced production costs, lower energy consumption, and a meter with improved accuracy, as well as updates on flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the collected data to be transmitted to remote monitoring systems for later analysis. The innovative feature of this smart meter lies in the fact that it has readily available components, along with the ESP32 chip, which results in a low-cost smart meter with performance that is comparable to other meters available on the market. Moreover, it is has the capacity to incorporate IoT and artificial intelligence applications. The developed smart meter is cost effective and energy efficient, and offers benefits with regard to flexibility, and thus represents an innovative, efficient, and versatile solution for smart grid applications.
PubMed: 37631747
DOI: 10.3390/s23167210 -
Nature Communications Jun 2021Digital devices are the essential building blocks of any modern electronic system. Fibres containing digital devices could enable fabrics with digital system...
Digital devices are the essential building blocks of any modern electronic system. Fibres containing digital devices could enable fabrics with digital system capabilities for applications in physiological monitoring, human-computer interfaces, and on-body machine-learning. Here, a scalable preform-to-fibre approach is used to produce tens of metres of flexible fibre containing hundreds of interspersed, digital temperature sensors and memory devices with a memory density of ~7.6 × 10 bits per metre. The entire ensemble of devices are individually addressable and independently operated through a single connection at the fibre edge, overcoming the perennial single-fibre single-device limitation and increasing system reliability. The digital fibre, when incorporated within a shirt, collects and stores body temperature data over multiple days, and enables real-time inference of wearer activity with an accuracy of 96% through a trained neural network with 1650 neuronal connections stored within the fibre. The ability to realise digital devices within a fibre strand which can not only measure and store physiological parameters, but also harbour the neural networks required to infer sensory data, presents intriguing opportunities for worn fabrics that sense, memorise, learn, and infer situational context.
Topics: Body Temperature; Digital Technology; Electronics; Humans; Machine Learning; Memory; Monitoring, Physiologic; Neural Networks, Computer; Remote Sensing Technology; Textiles; User-Computer Interface; Wearable Electronic Devices
PubMed: 34083521
DOI: 10.1038/s41467-021-23628-5 -
Journal of Diabetes Science and... Sep 2021Blood glucose meters remain an effective tool for blood glucose monitoring (BGM) but not all meters provide the same level of insight beyond the numerical glucose result.
BACKGROUND
Blood glucose meters remain an effective tool for blood glucose monitoring (BGM) but not all meters provide the same level of insight beyond the numerical glucose result.
OBJECTIVE
To investigate healthcare professional (HCP) perceptions of four meters and how these meters support the achievement of self-management goals recommended by diabetes clinical practice guidelines.
METHODS
Three hundred and fifty-three HCPs from five countries reviewed the features and benefits of four meters using interactive webpages and then responded to statements about the utility of each meter and ranked each meter in terms of clinical value.
RESULTS
Meter D ranked significantly higher in terms of clinical utility for all 13 guideline questions (70%-84%, < .05) compared to other meters. Endocrinologists (69%-85%), primary care physicians (PCP; 63%-80%), and diabetes nurses (DN; 80%-89%) consistently ranked meter D highest for all guideline questions. DNs ranked selected questions significantly higher compared to PCPs (8 of 13) or endocrinologists (3 of 13; < .05). Meter D achieved strong endorsement from HCPs in France and Germany, followed by the United States and Canada, with comparatively lower responses from Italian HCPs ( < 0.05). With respect to self-management, 80% of HCPs selected meter D as their first choice for patients with type 1 diabetes to help patients improve diabetes management or understand their numbers to help them stay in range.
CONCLUSIONS
HCPs had strong preference for a meter providing additional insights, messages, and guidance direct to the patient to support achievement of self-management goals recommended by diabetes clinical practice guidelines.
Topics: Blood Glucose; Blood Glucose Self-Monitoring; Delivery of Health Care; Goals; Humans; Perception; Self-Management; United States
PubMed: 32772855
DOI: 10.1177/1932296820946112 -
Sensors (Basel, Switzerland) Dec 2022More insight into in-field mechanical power in cyclical sports is useful for coaches, sport scientists, and athletes for various reasons. To estimate in-field mechanical... (Review)
Review
More insight into in-field mechanical power in cyclical sports is useful for coaches, sport scientists, and athletes for various reasons. To estimate in-field mechanical power, the use of wearable sensors can be a convenient solution. However, as many model options and approaches for mechanical power estimation using wearable sensors exist, and the optimal combination differs between sports and depends on the intended aim, determining the best setup for a given sport can be challenging. This review aims to provide an overview and discussion of the present methods to estimate in-field mechanical power in different cyclical sports. Overall, in-field mechanical power estimation can be complex, such that methods are often simplified to improve feasibility. For example, for some sports, power meters exist that use the main propulsive force for mechanical power estimation. Another non-invasive method usable for in-field mechanical power estimation is the use of inertial measurement units (IMUs). These wearable sensors can either be used as stand-alone approach or in combination with force sensors. However, every method has consequences for interpretation of power values. Based on the findings of this review, recommendations for mechanical power measurement and interpretation in kayaking, rowing, wheelchair propulsion, speed skating, and cross-country skiing are done.
Topics: Humans; Sports; Athletes; Mechanical Phenomena; Bicycling; Wearable Electronic Devices; Biomechanical Phenomena
PubMed: 36616649
DOI: 10.3390/s23010050 -
Iranian Journal of Medical Sciences Jan 2020Vision plays an important role in supporting efficient locomotion. The present study aimed to measure the physiological cost index (PCI) and some kinematic parameters of...
BACKGROUND
Vision plays an important role in supporting efficient locomotion. The present study aimed to measure the physiological cost index (PCI) and some kinematic parameters of preferred walking and jogging in blind and sighted students.
METHODS
A cross-sectional study was conducted among blind (n=18) and sighted (n=27) students aged 8-16 years. The following parameters were measured during a standard test procedure: step length (meter), cadence (steps/min), mean speed (meter/min), and the PCI of preferred walking (PCI) and jogging (PCI) over a distance of 100 meters.
RESULTS
Univariate linear regression analysis revealed that the weight of an individual as well as the test duration were significant predictors of heart rate (HR) and PCI. Overall, the PCI (beats/meter) of sighted (PCI=0.22±0.08 and PCI=0.24±0.07) and blind students (PCI=0.27±0.07 and PCI=0.31±0.08) were significantly different (all P≤0.05). In addition, the speed of preferred walking (PW) in sighted students was significantly higher than that of the blind students (67±8 versus 62.8±9 m/min; all P≤0.05), while this difference was insignificant in jogging mode (105±9 versus 102±11 m/min).
CONCLUSION
Although the blind students were familiar with the ambient environment and the walking route, they demonstrated a different pattern of PW and jogging modes with respect to kinematic parameters. We also demonstrated that the blind students spent more energy (i.e., PCI) to achieve a lower or equal gait kinematics compared to the sighted students.
PubMed: 32038055
DOI: 10.30476/ijms.2019.45386 -
Journal of Physical Therapy Science Feb 2022[Purpose] This study aimed to investigate the absolute intra-rater and inter-rater reliabilities during the measurement of muscle hardness, which is used to evaluate...
[Purpose] This study aimed to investigate the absolute intra-rater and inter-rater reliabilities during the measurement of muscle hardness, which is used to evaluate physical therapy. Moreover, we examined the effects of using different equipment types and their positioning on the intra-rater and inter-rater reliabilities. [Participants and Methods] Participants of this study comprised 12 healthy adult male individuals. Two experts and two beginners measured the muscle hardness of the lumbar erector spinae and rectus femoris using three types of hardness meters at two positions, including when the muscle was relaxed and stretched. [Results] Intra-rater fixed bias was observed during some measurements by both experts and beginners. Inter-rater fixed bias was observed during measurements by some experts and not the beginners. [Conclusion] In this study, the measurement of muscle hardness demonstrated a need to reconsider the measurement position and acclimation time. These examinations require the consideration of relative and absolute reliabilities.
PubMed: 35221515
DOI: 10.1589/jpts.34.122