Examination of Potential of Thermopile-Based Contactless Respiratory Gating

To control the spread of coronavirus disease 2019 (COVID-19), it is effective to perform a fast screening of the respiratory rate of the subject at the gate before entering a space to assess the potential risks. In this paper, we examine the potential of a novel yet cost-effective solution, called thermopile-based respiratory gating, to contactlessly screen a subject by measuring their respiratory rate in the scenario with an entrance gate. Based on a customized thermopile array system, we investigate different image and signal processing methods that measure respiratory rate from low-resolution thermal videos, where an automatic region-of-interest selection-based approach obtains a mean absolute error (MAE) of 0.8 breaths per minute. We show the feasibility of thermopile-based respiratory gating and quantify its limitations and boundary conditions in a benchmark (e.g., appearance of face mask, measurement distance and screening time). The technical validation provided by this study is helpful for designing and implementing a respiratory gating solution toward the prevention of the spread of COVID-19 during the pandemic.

Download Full-text

Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion

Sensors ◽

10.3390/s21041184 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1184

Author(s):

Iau-Quen Chung ◽

Jen-Te Yu ◽

Wei-Chi Hu

Keyword(s):

Heart Rate ◽

Data Fusion ◽

Respiratory Rate ◽

Spectral Data ◽

Empirical Mode Decomposition ◽

Cost Effective ◽

Absolute Error ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Mode Decomposition

Cardiopulmonary monitoring is important and useful for diagnosing and managing multiple conditions, such as stress and sleep disorders. Wearable ambulatory systems can provide continuous, comfortable, and inexpensive means for monitoring; it always has been a research subject in recent years. Being simple and cost-effective, electrocardiogram-based commercial products can be found in the market that provides cardiac diagnostic information for assessment, including heart rate measurement and atrial fibrillation identification. Based on a data-driven and self-adaptive approach, this study aims to estimate heart rate and respiratory rate simultaneously from one lead electrocardiogram signal. In contrast to ensemble empirical mode decomposition with principle component analysis, performed in the time domain, our method uses spectral data fusion, together with intrinsic mode functions using ensemble empirical mode decomposition obtains a more accurate heart rate and respiratory rate. Equipped with a rule-based selection of defined frequency levels for respiratory rate (RR) estimation, the proposed method obtains (0.92, 1.32) beat per minute for the heart rate and (2.20, 2.92) breath per minute for the respiratory rate as their mean absolute error and root mean square error, respectively outperforming other existing methods.

Download Full-text

Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽

10.3390/s21113771 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3771

Author(s):

Alexey Kashevnik ◽

Walaa Othman ◽

Igor Ryabchikov ◽

Nikolay Shilov

Keyword(s):

Respiratory Rate ◽

Evaluation System ◽

Body Movement ◽

Absolute Error ◽

The Body ◽

Meditation Practice ◽

Body Parts ◽

Mental Health Training ◽

Negative Thoughts ◽

The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

Download Full-text

Cost-Effective Face Mask Filter Based on Hybrid Composite Nanofibrous Layers with High Filtration Efficiency

Langmuir ◽

10.1021/acs.langmuir.1c00926 ◽

2021 ◽

Author(s):

Walaa A. Abbas ◽

Basamat S. Shaheen ◽

Loujain G. Ghanem ◽

Ibrahim M. Badawy ◽

Mohamed M. Abodouh ◽

...

Keyword(s):

Hybrid Composite ◽

Cost Effective ◽

Face Mask ◽

Filtration Efficiency

Download Full-text

Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Drones ◽

10.3390/drones5030068 ◽

2021 ◽

Vol 5 (3) ◽

pp. 68

Author(s):

Jiwei Fan ◽

Xiaogang Yang ◽

Ruitao Lu ◽

Xueli Xie ◽

Weipeng Li

Keyword(s):

Deep Learning ◽

Autonomous Navigation ◽

Detection Method ◽

Active Role ◽

Absolute Error ◽

Face Mask ◽

Learning Technology ◽

Flight System ◽

Crowd Density ◽

The Mean

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

Download Full-text

Estimation of Building Heights and DEM Accuracy Assessment Using ICESat-2 Data Products

Engineering Proceedings ◽

10.3390/ecsa-8-11442 ◽

2021 ◽

Vol 10 (1) ◽

pp. 37

Author(s):

Goddu Pavan Sai Goud ◽

Ashutosh Bhardwaj

Keyword(s):

Spatial Data ◽

Rural Areas ◽

Urban Areas ◽

Accuracy Assessment ◽

Urban Expansion ◽

Population Expansion ◽

Cost Effective ◽

Absolute Error ◽

Google Earth ◽

Surface Geometry

The use of remote sensing for urban monitoring is a very reliable and cost-effective method for studying urban expansion in horizontal and vertical dimensions. The advantage of multi-temporal spatial data and high data accuracy is useful in mapping urban vertical aspects like the compactness of urban areas, population expansion, and urban surface geometry. This study makes use of the ‘Ice, cloud, and land elevation satellite-2′ (ICESat-2) ATL 03 photon data for building height estimation using a sample of 30 buildings in three experimental sites. A comparison of computed heights with the heights of the respective buildings from google image and google earth pro was done to assess the accuracy and the result of 2.04 m RMSE was obtained. Another popularly used method by planners and policymakers to map the vertical dimension of urban terrain is the Digital Elevation Model (DEM). An assessment of the openly available DEM products—TanDEM-X and Cartosat-1 has been done over Urban and Rural areas. TanDEM-X is a German earth observation satellite that uses InSAR (Synthetic Aperture Radar Interferometry) technique to acquire DEM while Cartosat-1 is an optical stereo acquisition satellite launched by the Indian Space Research Organization (ISRO) that uses photogrammetric techniques for DEM acquisition. Both the DEMs have been compared with ICESat-2 (ATL-08) Elevation data as the reference and the accuracy has been evaluated using Mean error (ME), Mean absolute error (MAE) and Root mean square error (RMSE). In the case of Greater Hyderabad Municipal Corporation (GHMC), RMSE values 5.29 m and 7.48 m were noted for TanDEM-X 90 and CartoDEM V3 R1 respectively. While the second site of Bellampalli Mandal rural area observed 5.15 and 5.48 RMSE values for the same respectively. Therefore, it was concluded that TanDEM-X has better accuracy as compared to the CartoDEM V3 R1.

Download Full-text

Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts

Energies ◽

10.3390/en14227587 ◽

2021 ◽

Vol 14 (22) ◽

pp. 7587

Author(s):

Conor Lynch ◽

Christian O’Leary ◽

Preetham Govind Kolar Sundareshan ◽

Yavuz Akin

Keyword(s):

Electricity Market ◽

Electricity Consumption ◽

Cost Effective ◽

Absolute Error ◽

Electricity Price ◽

Gradient Boosting ◽

Avant Garde ◽

Electricity Price Forecasting ◽

Gradient Boosting Machine ◽

Extreme Gradient Boosting

In response to the inherent challenges of generating cost-effective electricity consumption schedules for dynamic systems, this paper espouses the use of GBM or Gradient Boosting Machine-based models for electricity price forecasting. These models are applied to data streams from the Irish electricity market and achieve favorable results, relative to the current state-of-the-art. Presently, electricity prices are published 10 h in advance of the trade day of interest. Using the forecasting methodology outlined in this paper, an estimation of these prices can be made available one day in advance of the official price publication, thus extending the time available to plan electricity utilization from the grid to be as cost effectively as possible. Extreme Gradient Boosting Machine (XGBM) models achieved a Mean Absolute Error (MAE) of 9.93 for data from 30 September 2018 to 12 December 2019 which is an 11.4% improvement on the avant-garde. LGBM models achieve a MAE score 9.58 on more recent data: the full year of 2020.

Download Full-text

A Cost-Effective Method for Bulge Prediction in Two Point Incremental Forming

10.25518/esaform21.419 ◽

2021 ◽

Author(s):

Jaekwang Shin ◽

Ankush Bansal ◽

Randy Cheng ◽

Alan Taub ◽

Mihaela Banu

Keyword(s):

Shape Function ◽

Incremental Forming ◽

Computational Cost ◽

Region Of Interest ◽

Computation Time ◽

Mesh Size ◽

Cost Effective ◽

Summation Method ◽

Fem Modeling ◽

Forming Defects

Accurate prediction of the defects occurring in incrementally formed parts has been gaining attention in recent years. This interest is because accurate predictions can overcome the limitation in the advancement of incremental forming in industrial-scale implementation, which has been held back by the increase in the cost and development time due to trial and error methods. The finite element method has been widely utilized to predict the defects in the formed part, e.g., bulge. However, the computation time of running these models and their mesh-size dependency in predicting the forming defects represent barriers in adopting these models as part of CAD-FEM-CAE platforms. Thus, robust analytical and data-driven algorithms must be developed for a cost-effective design of complex parts. In this paper, a new analytical model is proposed to predict the bulge location and geometry in two point incremental forming of an aerospace aluminum alloy AA7075-O for a 67° truncated cone. First, the algorithm calculates the region of interest based on the part geometry. A novel shape function and weighted summation method are then utilized to calculate the amplitude of the instability produced by material accumulation during forming, leading to a bulge on the unformed portion of the sample. It was found that the geometric profile of the part influences the shape function, which is a function created to incorporate the effects of process parameter and boundary condition. The calculated profile in each direction is finalized into one 3-dimensional profile, compared with the experimental results for validation. The proposed model has proven to predict an accurate bulge profile with 95% accuracy comparing with experiments with less than 5% computational cost of FEM modeling.

Download Full-text

Multi-Scale Convolutional Neural Network for Accurate Corneal Segmentation in Early Detection of Fungal Keratitis

Journal of Fungi ◽

10.3390/jof7100850 ◽

2021 ◽

Vol 7 (10) ◽

pp. 850

Author(s):

Veena Mayya ◽

Sowmya Kamath Shevgoor ◽

Uma Kulkarni ◽

Manali Hazarika ◽

Prabal Datta Barua ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Region Of Interest ◽

Cost Effective ◽

Fungal Keratitis ◽

Contact Lens Wear ◽

Multi Scale ◽

Effective Interventions ◽

Prolonged Contact ◽

Segmented Images

Microbial keratitis is an infection of the cornea of the eye that is commonly caused by prolonged contact lens wear, corneal trauma, pre-existing systemic disorders and other ocular surface disorders. It can result in severe visual impairment if improperly managed. According to the latest World Vision Report, at least 4.2 million people worldwide suffer from corneal opacities caused by infectious agents such as fungi, bacteria, protozoa and viruses. In patients with fungal keratitis (FK), often overt symptoms are not evident, until an advanced stage. Furthermore, it has been reported that clear discrimination between bacterial keratitis and FK is a challenging process even for trained corneal experts and is often misdiagnosed in more than 30% of the cases. However, if diagnosed early, vision impairment can be prevented through early cost-effective interventions. In this work, we propose a multi-scale convolutional neural network (MS-CNN) for accurate segmentation of the corneal region to enable early FK diagnosis. The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. The model trained on the segmented images in the region of interest, achieved a diagnostic accuracy of 88.96%. The features learnt by the model emphasize that it can correctly identify dominant corneal lesions for detecting FK.

Download Full-text

Technical validation of a face mask adapted for dry powder inhalation in the equine species

Equine Veterinary Journal ◽

10.1111/j.2042-3306.1997.tb03161.x ◽

1997 ◽

Vol 29 (6) ◽

pp. 471-476 ◽

Cited By ~ 6

Author(s):

D. H. DUVIVIER ◽

D. VOTION ◽

S. VANDENPUT ◽

T. ART ◽

P. LEKEUX

Keyword(s):

Face Mask ◽

Dry Powder Inhalation ◽

Dry Powder ◽

Technical Validation

Download Full-text

Multivariate Analysis for Assessing Sources, and Potential Risks of Polycyclic Aromatic Hydrocarbons in Lisbon Urban Soils

Minerals ◽

10.3390/min9030139 ◽

2019 ◽

Vol 9 (3) ◽

pp. 139 ◽

Cited By ~ 3

Author(s):

Anabela Cachada ◽

Ana Dias ◽

Amélia Reis ◽

Eduardo Ferreira da Silva ◽

Ruth Pereira ◽

...

Keyword(s):

Risk Assessment ◽

Polycyclic Aromatic Hydrocarbons ◽

Urban Planning ◽

Aromatic Hydrocarbons ◽

Urban Soils ◽

Cost Effective ◽

City Center ◽

Polycyclic Aromatic ◽

Potential Risks ◽

The City

Urban soils quality may be severely affected by polycyclic aromatic hydrocarbons (PAHs) contamination, as is the case of Lisbon (Portugal). However, to conduct a risk assessment analysis in an urban area can be a very difficult task due to the patchy nature and heterogeneity of these soils. Thus, the present study aims to provide an example on how to perform the first tier of a risk assessment plan in the case of urban soils using a simpler, cost effective, and reliable framework. Thus, a study was conducted in Lisbon to assess the levels of PAH, their potential risks to the environment and human health, and to identify their major sources. Source apportionment was performed by studying PAHs profiles, their relationship with potentially toxic elements, and general characteristics of soil using multivariate statistical methods. Results showed that geostatistical tools are useful for evaluating the spatial distribution and major inputs of PAHs in urban soils, as well as to identify areas of potential concern, showing their usefulness in risk assessment analysis and urban planning. Particularly, the prediction maps obtained allowed for a clear identification of areas with the highest levels of PAHs (close to the airport and in the city center). The high concentrations found in soils from the city center should be a result of long-term accumulation due to diffuse pollution mostly from traffic (through atmospheric emissions, tire debris and fuel exhaust, as well as pavement debris). Indeed, most of the sites sampled in the city center were historical gardens and parks. The calculation of potential risks based on different models showed that there is a high discrepancy among guidelines, and that risks will be extremely associated with the endpoint or parameters used in the different models. Nevertheless, this initial approach based on total levels was useful for identifying areas where a more detailed risk assessment is needed (close to the airport and in the city center). Therefore, the use of prediction maps can be very useful for urban planning, for example, by crossing information obtained with land uses, it is possible to define the most problematic areas (e.g., playgrounds and schools).

Download Full-text