Search Results (11)

A low-cost, handheld centrifugal microfluidic system for multiplexed visual detection based on recombinase polymerase amplification (RPA) was developed. A concise centrifugal microfluidic chip featuring four reaction units was developed to run multiplexed RPA amplification in parallel. Additionally, a significantly shrunk-size and cost-effective handheld companion device was developed, incorporating heating, optical, rotation, and sensing modules, to perform multiplexed amplification and visual detection. After one-time sample loading, the metered sample was equally distributed into four separate reactors with high-speed centrifugation. Non-contact heating was adopted for isothermal amplification. A tiny DC motor on top of the chip was used to drive steel beads inside reactors for active mixing. Another small DC motor, which was controlled by an elaborate locking strategy based on magnetic sensing, was adopted for centrifugation and positioning. Visual fluorescence detection was optimized from different sides, including material, surface properties, excitation light, and optical filters. With fluorescence intensity-based visual detection, the detection results could be directly observed through the eyes or with a smartphone. As a proof of concept, the handheld device could detect multiple targets, e.g., different genes of African swine fever virus (ASFV) with the comparable LOD (limit of detection) of 75 copies/test compared to the tube-based RPA. Full article

In facing the outbreak of the pandemic, robots are highly appealing for their non-contact nature. Among them, we have selected the mobile robot manipulator to develop an autonomous system for pandemic prevention, as it possesses both mobility and manipulability. The robot was used as a platform for performing autonomous fever detection, medicine delivery, and environmental disinfection system for the fever station and isolation ward, which are the two primary units that deal with the pandemic in a hospital. The proposed novel algorithms aim to ensure both human safety and comfort by automating fever detection and recognizing medicine taking. Additionally, they address environmental disinfection by effectively covering blind spots. We conducted a series of experiments to evaluate their performance in a hospital-like setting, which was designed specifically for the testing of intelligent medical systems developed in our university. Quantitative assessment was administered to analyze how the introduction of the proposed autonomous system reduced the risk of infection, and feedback was also collected from participants through questionnaires. Full article

An outbreak of infectious diseases has highlighted the importance of the early detection and prevention of high temperatures or fevers, which are some of the main symptoms of many diseases. Thermal cameras have become a promising tool for the detection of fever due to their non-invasive, non-contact and rapidly inspecting nature. By using person identification to analyze temperature data from the corner of the eye (inner canthus), the temperature of individuals can be tracked over time. This could provide information not only about their current temperature but also about long-term temperature anomalies, and therefore can urge people to visit a doctor. This paper is intended as an initial study for the long-term temperature measurement of individuals. The preliminary results show the feasibility of such approach. In the future, a similar procedure is to be used for the detection and recognition of individual diseases. Full article

With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care and reducing healthcare costs. The Internet of Things (IoT) has recently drawn much interest as a potential remote health monitoring remedy. IoT-based systems can gather and analyze a wide range of physiological data, including blood oxygen levels, heart rates, body temperatures, and ECG signals, and then provide real-time feedback to medical professionals so they may take appropriate action. This paper proposes an IoT-based system for remote monitoring and early detection of health problems in home clinical settings. The system comprises three sensor types: MAX30100 for measuring blood oxygen level and heart rate; AD8232 ECG sensor module for ECG signal data; and MLX90614 non-contact infrared sensor for body temperature. The collected data is transmitted to a server using the MQTT protocol. A pre-trained deep learning model based on a convolutional neural network with an attention layer is used on the server to classify potential diseases. The system can detect five different categories of heartbeats: Normal Beat, Supraventricular premature beat, Premature ventricular contraction, Fusion of ventricular, and Unclassifiable beat from ECG sensor data and fever or non-fever from body temperature. Furthermore, the system provides a report on the patient’s heart rate and oxygen level, indicating whether they are within normal ranges or not. The system automatically connects the user to the nearest doctor for further diagnosis if any critical abnormalities are detected. Full article

Monkeypox (MPX) is a disease caused by monkeypox virus (MPXV). It is a contagious disease and has associated symptoms of skin lesions, rashes, fever, and respiratory distress lymph swelling along with numerous neurological distresses. This can be a deadly disease, and the latest outbreak of it has shown its spread to Europe, Australia, the United States, and Africa. Typically, diagnosis of MPX is performed through PCR, by taking a sample of the skin lesion. This procedure is risky for medical staff, as during sample collection, transmission and testing, they can be exposed to MPXV, and this infectious disease can be transferred to medical staff. In the current era, cutting-edge technologies such as IoT and artificial intelligence (AI) have made the diagnostics process smart and secure. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. Keeping in view the importance of these cutting-edge technologies, this paper presents a non-invasive, non-contact, computer-vision-based method for diagnosis of MPX by analyzing skin lesion images that are more smart and secure compared to traditional methods of diagnosis. The proposed methodology employs deep learning techniques to classify skin lesions as MPXV positive or not. Two datasets, the Kaggle Monkeypox Skin Lesion Dataset (MSLD) and the Monkeypox Skin Image Dataset (MSID), are used for evaluating the proposed methodology. The results on multiple deep learning models were evaluated using sensitivity, specificity and balanced accuracy. The proposed method has yielded highly promising results, demonstrating its potential for wide-scale deployment in detecting monkeypox. This smart and cost-effective solution can be effectively utilized in underprivileged areas where laboratory infrastructure may be lacking. Full article

The SARS-CoV-2 virus pandemic has shown that the use of a contact thermometer to verify the elevated body temperature of a suspected person carries a risk of spreading disease. The perfect solution seems to be the use of thermal imaging as a diagnostic method in fever evaluation. The aim of the research is to develop an algorithm for thermovision measurements in fever screening standards in the context of the impact of various weather conditions on the temperature of people entering the public institution. Each examined person had two thermal images of the face—AP and lateral projection. Using a T1020 FLIR thermal camera with a resolution of 1024 × 768 pixels; the mean temperature was measured from the area of the forehead, the maximum forehead, the corners of the eyes, the inside of the mouth and the external auditory canal temperature. On the other hand, using classic contact thermometers, the temperature in the armpit and ear was measured. The obtained preliminary results showed very strong and positive correlations between the temperature in the ear measured with an ear thermometer and the maximum, minimum and average forehead temperature. These correlations oscillate at approximately r = 0.6, but the highest value of Spearman coefficient was obtained for the mean temperature of the forehead. Moreover, high correlations were also obtained between the temperature in the ear, measured with an ear thermometer, and the maximum temperature in the corners of the eyes and in the ear, measured with a thermal imaging camera. These values were, respectively, r = 0.54, r = 0.65. In summarizing, remote body temperature measurement taken with a thermal camera can be useful in the assessment of the body’s core temperature. Full article

African swine fever (ASF) is a devastating viral disease in pigs and is therefore economically important for the swine industry. ASF is characterized by a short incubation period and immediate death, making the early identification of ASF-infected pigs essential. This pilot-scale study evaluates whether the infrared thermography (IRT) technique can be used as a diagnostic tool to detect changes in skin temperature (T_sk) during the early stages of disease development in experimentally ASF-infected pigs. Clinical symptoms and rectal temperatures (T_core) were recorded daily, and IRT readings during the experimental ASF infection were analyzed. All infected pigs died at 5–8 days post inoculation (dpi), and the incubation period was approximately 4 dpi. The average T_core increased from 0 dpi (38.9 ± 0.3 °C) to 7 dpi (41.0 ± 0.5 °C) and decreased by 8 dpi (39.8 ± 0 °C). The maximum T_sk of ASF-infected pigs increased from 2 (35.0 °C) to 3 dpi (38.5 °C). The mean maximum T_sk observed from three regions on the skin (ear, inguinal, and neck) significantly increased from 2 to 3 dpi. This study presents a non-contact method for the early detection of ASF in infected pigs using thermal imaging at 3 days after ASF infection. Full article

During the pandemic of coronavirus disease-2019 (COVID-19), medical practitioners need non-contact devices to reduce the risk of spreading the virus. People with COVID-19 usually experience fever and have difficulty breathing. Unsupervised care to patients with respiratory problems will be the main reason for the rising death rate. Periodic linearly increasing frequency chirp, known as frequency-modulated continuous wave (FMCW), is one of the radar technologies with a low-power operation and high-resolution detection which can detect any tiny movement. In this study, we use FMCW to develop a non-contact medical device that monitors and classifies the breathing pattern in real time. Patients with a breathing disorder have an unusual breathing characteristic that cannot be represented using the breathing rate. Thus, we created an Xtreme Gradient Boosting (XGBoost) classification model and adopted Mel-frequency cepstral coefficient (MFCC) feature extraction to classify the breathing pattern behavior. XGBoost is an ensemble machine-learning technique with a fast execution time and good scalability for predictions. In this study, MFCC feature extraction assists machine learning in extracting the features of the breathing signal. Based on the results, the system obtained an acceptable accuracy. Thus, our proposed system could potentially be used to detect and monitor the presence of respiratory problems in patients with COVID-19, asthma, etc. Full article

The World Health Organization (WHO) has declared COVID-19 a pandemic. We review and reduce the clinical literature on diagnosis of COVID-19 through symptoms that might be remotely detected as of early May 2020. Vital signs associated with respiratory distress and fever, coughing, and visible infections have been reported. Fever screening by temperature monitoring is currently popular. However, improved noncontact detection is sought. Vital signs including heart rate and respiratory rate are affected by the condition. Cough, fatigue, and visible infections are also reported as common symptoms. There are non-contact methods for measuring vital signs remotely that have been shown to have acceptable accuracy, reliability, and practicality in some settings. Each has its pros and cons and may perform well in some challenges but be inadequate in others. Our review shows that visible spectrum and thermal spectrum cameras offer the best options for truly noncontact sensing of those studied to date, thermal cameras due to their potential to measure all likely symptoms on a single camera, especially temperature, and video cameras due to their availability, cost, adaptability, and compatibility. Substantial supply chain disruptions during the pandemic and the widespread nature of the problem means that cost-effectiveness and availability are important considerations. Full article

Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs; meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 °C, the minimum error at 0.124 °C, and the MSE at 0.159 °C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs′ internal body temperature. Full article

Background: In the last two decades, infrared thermography (IRT) has been applied in quarantine stations for the screening of patients with suspected infectious disease. However, the fever-based screening procedure employing IRT suffers from low sensitivity, because monitoring body temperature alone is insufficient for detecting infected patients. To overcome the drawbacks of fever-based screening, this study aims to develop and evaluate a multiple vital sign (i.e., body temperature, heart rate and respiration rate) measurement system using RGB-thermal image sensors. Methods: The RGB camera measures blood volume pulse (BVP) through variations in the light absorption from human facial areas. IRT is used to estimate the respiration rate by measuring the change in temperature near the nostrils or mouth accompanying respiration. To enable a stable and reliable system, the following image and signal processing methods were proposed and implemented: (1) an RGB-thermal image fusion approach to achieve highly reliable facial region-of-interest tracking, (2) a heart rate estimation method including a tapered window for reducing noise caused by the face tracker, reconstruction of a BVP signal with three RGB channels to optimize a linear function, thereby improving the signal-to-noise ratio and multiple signal classification (MUSIC) algorithm for estimating the pseudo-spectrum from limited time-domain BVP signals within 15 s and (3) a respiration rate estimation method implementing nasal or oral breathing signal selection based on signal quality index for stable measurement and MUSIC algorithm for rapid measurement. We tested the system on 22 healthy subjects and 28 patients with seasonal influenza, using the support vector machine (SVM) classification method. Results: The body temperature, heart rate and respiration rate measured in a non-contact manner were highly similarity to those measured via contact-type reference devices (i.e., thermometer, ECG and respiration belt), with Pearson correlation coefficients of 0.71, 0.87 and 0.87, respectively. Moreover, the optimized SVM model with three vital signs yielded sensitivity and specificity values of 85.7% and 90.1%, respectively. Conclusion: For contactless vital sign measurement, the system achieved a performance similar to that of the reference devices. The multiple vital sign-based screening achieved higher sensitivity than fever-based screening. Thus, this system represents a promising alternative for further quarantine procedures to prevent the spread of infectious diseases. Full article