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Sensors (Basel, Switzerland) Nov 2021The SHFT device is a novel running wearable consisting of two pods connected to your smartphone issuing several running metrics based on accelerometer and gyroscope...
The SHFT device is a novel running wearable consisting of two pods connected to your smartphone issuing several running metrics based on accelerometer and gyroscope technology. The purpose of this study was to investigate the reliability and validity of the power output (PO) metric produced by the SHFT device. To assess reliability, 12 men ran on an outdoor track at 10.5 km·h and 12 km·h on two consecutive days. To assess validity, oxygen uptake (VO) and SHFT data from eight men and seven women were collected during incremental submaximal running tests on an indoor treadmill on one to four separate days (34 tests in total). SHFT reliability on the outdoor track was strong with coefficients of variance (CV) of 1.8% and 2.4% for 10.5 and 12 km·h, respectively. We observed a very strong linear relationship between PO and VO (r = 0.54) within subjects, and a very strong linear relationship within each subject within each treadmill test (r = 0.80). We conclude that SHFT provides a reliable running power estimate and that a very strong relationship between SHFT-Power and metabolic rate exists, which places SHFT as one of the leading commercially available running power meters.
Topics: Exercise Test; Humans; Male; Oxygen Consumption; Reproducibility of Results; Running
PubMed: 34833596
DOI: 10.3390/s21227516 -
The Science of the Total Environment Dec 2023The Mexican Atlantic coast is vulnerable to sea level rise due to its low, sandy shorelines with extensive adjacent wetlands. The increasing trends at the regional level...
The Mexican Atlantic coast is vulnerable to sea level rise due to its low, sandy shorelines with extensive adjacent wetlands. The increasing trends at the regional level are similar to global trends (~3 ± 0.04 mm/year): between 1.8 mm/year in Alvarado, Veracruz, to 3.6 mm/year in Isla Mujeres, Quintana Roo. A synthetic model was applied to Mexican Atlantic coast under two sea level rise scenarios for the year 2100. Our objectives were: 1) to identify potentially floodable zones in the face of a sea level rise of one and two meters on the Mexican Atlantic coast with a synthetic model using SRTM and LiDAR topographic data; 2) to determine vegetation and land use affected in the potentially floodable zones; and 3) quantify the vulnerable human population. With topographic data we identified low areas (one and two meters) to assess potentially floodable zones; these were intersected with data layers of vegetation, land use, and human population. Deltaic zones, coastal lagoons and low-lying areas of the Yucatan Peninsula were regions with the largest potentially floodable surface. In the one-meter sea rise scenario, 581,674 ha were identified as potentially floodable, and 896,151 in the two-meter scenario. The most vulnerable vegetation and land use types were wetlands, such as cattail marshes (tulares; ~29 %) and mangroves (~27 %), as well as cultivated grasslands (~6 %). The indirectly affected coastal population could be approximately 5.5 million in these scenarios (~33 %), and the directly affected population could range between 124,000 and 440,000 (~0.72 and 2.55 %, respectively). These results indicate that there will be strong effects in economic, social, and environmental impacts on the Atlantic coast of Mexico in the event of a one- and two-meters sea level rise. This type of work will enable proposal conservation and adaptation strategies for human populations and coastal cities.
PubMed: 37591386
DOI: 10.1016/j.scitotenv.2023.166317 -
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 -
Journal of the American Geriatrics... Nov 2022Community-dwelling older adults experiencing hip fracture often fail to achieve adequate walking capacity following surgery and rehabilitation. Effects of psychological... (Randomized Controlled Trial)
Randomized Controlled Trial
BACKGROUND
Community-dwelling older adults experiencing hip fracture often fail to achieve adequate walking capacity following surgery and rehabilitation. Effects of psychological factors on post-fracture walking capacity are poorly understood. Accordingly, this paper investigates effects of psychological resilience on observed walking capacity measures in older adults following hip fracture, controlling for important covariates.
METHODS
Data were drawn from the Community Ambulation Project, a clinical trial of 210 community-dwelling adults aged ≥60 years who experienced a minimal trauma hip fracture and were randomized to one of two 16-week home-based physical therapist-guided interventions. Psychological resilience was measured at study baseline using the 6-item Brief Resilience Scale (BRS); scores were classified into groups in order to distinguish levels of self-reported resilience. Walking capacity was assessed at study baseline and 16 weeks later using 4-Meter Gait Speed (4MGS), 50-Foot Walk Test (50FWT), and 6-Minute Walk Distance (SMWD). In multivariate analyses of covariance in which 16-week follow-up values of each walking measure were outcomes, covariates included clinical trial arm, gender, age, and baseline values of: walking measure corresponding to the outcome; body mass index; depressive symptom severity; degree of psychological optimism; cognitive status; informal caregiver need; and days from hospital admission to randomization.
RESULTS
Increases between baseline and 16 weeks later in mean gait speed in meters/sec (m/s) and walking distance in meters (m) in 4MGS, 50FWT and SMWD were 0.06 m/s (p = 0.061), 0.11 m/s (p < 0.01), and 25.5 m (p = 0.056) greater, respectively, in the most resilient BRS group compared to the least resilient BRS group.
CONCLUSION
Higher levels of psychological resilience were associated with greater walking speed and distance. Psychological resilience represents a potentially clinically important pathway and intervention target, toward the goal of improving walking capacity among older adults known to have substantial residual disability following hip fracture.
Topics: Humans; Aged; Resilience, Psychological; Walking; Hip Fractures; Walking Speed; Walk Test
PubMed: 35856155
DOI: 10.1111/jgs.17930 -
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 -
International Journal of Pharmaceutical... 2021Water activity refers to the amount of water in a system that is available for microbial growth. Commercial water activity meters are precision instruments with the...
Water activity refers to the amount of water in a system that is available for microbial growth. Commercial water activity meters are precision instruments with the ability to determine water activity to within 0.001 units and carry prices of $10,000 or more. The purpose of the study was to build a robust water activity meter from commercially available components and measure the water activity of liquids commonly used in compounded formulations. SHT-85 sensors were connected to an Adafruit Feather HUZZAH microcontroller. Standard salt slurries and common oral liquid vehicles were monitored in airtight containers until equilibrium was reached. Standard curves were used to convert sensor outputs to water activity values. The standard curves were linear with R2 >0.99. Oral liquid vehicles showed water activity values between 0.62 and 0.99. Samples equilibrated within 9.5 hours in 16-ounce jars or 2.5 hours in 20-mL vials. Stirring the sample during measurement reduced equilibration time in 16-ounce jars, but not in 20-mL vials. The inexpensive meter was accurate and precise in measuring the water activity of standards and selected oral vehicles. An accurate and precise water activity meter was constructed at a cost of approximately $150. Common oral formulation components have a water activity of >0.6, the United States Pharmacopeial threshold for requiring a preservative. Pharmacists should use caution when diluting preserved vehicles.
Topics: Humans; Pharmacists; Water
PubMed: 33503009
DOI: No ID Found -
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