Integrating deep learning algorithms for the lung segmentation and body-part-specific anatomical classification with Medical Imaging and Data Resource Center (MIDRC)

Medical Imaging and Data Resource Center (MIDRC) has been built to support AI-based research in response to the COVID-19 pandemic. One of the main goals of MIDRC is to make data collected in the repository ready for AI analysis. Due to data heterogeneity, there is a need to standardize data and make data-mining easier. Our study aims to stratify imaging data according to underlying anatomy using open-source image processing tools. The experiments were performed using Google Colaboratory on computed tomography (CT) imaging data available from the MIDRC. We adopted the existing open-source tools to process CT series (N=389) to define the image sub-volumes according to body part classification, and additionally identified series slices containing specific anatomic landmarks. Cases with automatically identified chest regions (N=369) were then processed to automatically segment the lungs. In order to assess the accuracy of segmentation, we performed outlier analysis using 3D shape radiomics features extracted from the left and right lungs. Standardized DICOM objects were created to store the resulting segmentations, regions, landmarks and radiomics features. We demonstrated that the MIDRC chest CT collections can be enriched using open-source analysis tools and that data available in MIDRC can be further used to evaluate the robustness of publicly available tools. © 2023 SPIE.

Modelling Proper and Improper Sitting Posture of Computer Users Using Machine Vision for a Human–Computer Intelligent Interactive System during COVID-19

Featured ApplicationThe study could be used for sitting posture monitoring in a work-from-home setup. This could also be used for rehabilitation purposes of patients who has posture-related problems.Human posture recognition is one of the most challenging tasks due to the variation in human appearance, changes in the background and illumination, additional noise in the frame, and diverse characteristics and amount of data generated. Aside from these, generating a high configuration for recognition of human body parts, occlusion, nearly identical parts of the body, variations of colors due to clothing, and other various factors make this task one of the hardest in computer vision. Therefore, these studies require high-computing devices and machines that could handle the computational load of this task. This study used a small-scale convolutional neural network and a smartphone built-in camera to recognize proper and improper sitting posture in a work-from-home setup. Aside from the recognition of body points, this study also utilized points' distances and angles to help in recognition. Overall, the study was able to develop two objective datasets capturing the left and right side of the participants with the supervision and guidance of licensed physical therapists. The study shows accuracies of 85.18% and 92.07%, and kappas of 0.691 and 0.838, respectively. The system was developed, implemented, and tested in a work-from-home environment.

Understanding Desire to Touch Using Large-scale Twitter Data

I and research colleagues investigated people's desire to touch by collecting and analyzing a large amount of text data that contain phrases such as "want to touch” on Twitter. We revealed the relationship between the body part that people want to touch and the touch gesture. We also revealed the effects of the COVID-19 pandemic on the desire to touch. Specifically, we observed "skin hunger,” i.e., the strong desire for physical communication, and variation of touch avoidance toward objects such as doorknobs. Our results will be beneficial for understanding human behavior as well as for the further development of haptic technology. © 2023 Nippon Telegraph and Telephone Corp.. All rights reserved.

Amino Acid and Mineral Profiles of Fresh Snakehead (Channa striata) Meat to Potential as an Immune System

During the COVID-19 pandemic, Riau Province experienced an increase in fish consumption as an effort to improve the immune system in preventing the transmission of COVID-19. The increase in fish consumption is in line with the increase in the production of snakehead fish commodity fisheries in Riau Province. However, research on the nutritional content of snakehead fish from Riau waters has not been reported. So, this study aimed to characterize the snakehead fish meat's amino acid and mineral profiles. Snakehead fish was obtained from Bengkuang Lake, Riau Province then analyzed physically (fish freshness detection, morphometrics, and fish body proportions) and chemically (proximate, amino acids, and minerals). The results showed that the snakehead fish meat obtained from Riau waters was in fresh condition with a body length of 30.33 cm and a body weight of 252 g, which was dominated by the proportion of the body part of the meat of 37.38%. Based on chemical characteristics, snakehead fish meat contained 16.99% protein and 1.96% ash. Snakehead fish meat also had dominant amino acids, namely leucine 0.539% and glutamic acid 1.446%. Furthermore, snakehead fish meat minerals were dominated by Fe 6.217% and Zn 2.235%. The presence of amino acids leucine, glutamic acid, and Fe and Zn minerals play an important role in the wound healing process and immune system.

Contrast Enhancement and Noise Reduction of chest X-ray images

X-ray radiography is used to get medical images of body parts such as chest, bones etc. These images help in detection of anomaly in inspected body part, for eg- Chest X-ray are used for detection of many diseases such as Covid-19, Pneumonia and Cancer. However, images obtained from radiography are low in contrast and with high noise level. Enhancement of an image is very crucial for the diagnostic purpose, as currently medical images are very helpful in identifying various disease and problem in human body. With the technical support, the enhancement is considered one of the first-rate methods for the betterment of visualization and raising the standard for understanding and clearing the image details. In our work, we have focused on the contrast enhancement and noise reduction, using Histogram equalization, CLAHE (Contrast Limited Adaptive Histogram Equalization), median filter and DCT filter for chest X-ray images of COVID-19 positive patients. The dataset of 6,334 images are collected from the Kaggle repository. All these methods are combined and as a result, has provided the best output by giving a colored enhanced image, highlighting the major details. This work will be helpful in the diagnosis of various kind of the diseases from radiographic approach. In the future, we will extend the process for the diagnostic part of COVID-19 from the enhanced images dataset, which will help in easy detection and work as a technological support to healthcare system. © 2022 IEEE.

Comparative Analysis of Image Enhancement Techniques for Chest X-ray Images

X-ray radiography plays a crucial part in diagnosis of various diseases in human body like Covid-19, Cancer and Pneumonia. The images obtained through X-ray radiography is interpreted by Surgeons, Pathologists and Radiologists for detecting anomaly in scanned body part. Chest X-ray is one of the cheapest and easily accessible tests of functioning of chest and lungs. However, images obtained through X-ray are not very clear, low in contrast and with lesser variation in gray level. Image enhancement is done for better visualization of images and bringing forward the underlying details of image. The Kaggle repository of total 6334 chest X-ray images were used for experimentation and calculation works. In this paper, we have compared various combinations of contrast enhancement techniques such as CLAHE, Morphological operations (black and white hat transforms) and noise reduction techniques like Median filter, DCT and DWT. The Comparison was done on the basis of image quality assessment parameters such as MSE, PSNR, and AMBE. The results showed that fusion of CLAHE and DWT techniques gave best results with highest PSNR value and lowest AMBE among the various models discussed. The proposed methodology shall be very helpful in diagnosis of diseases from chest X-ray images. © 2022 IEEE.

Identification and Classification of Human Body Parts for Contactless Screening Systems: An Edge-AI Approach

Continuous monitoring of vital signs like body temperature and cardio-pulmonary rates can be critical in the early prediction and diagnosis of illnesses. Optical-based methods, i.e., RGB cameras and thermal imaging systems, have been used with relative success for performing contactless vital signs monitoring, which is of great value for pandemic scenarios, such as COVID-19. However, to increase the performance of such systems, the precise identification and classification of the human body parts under screening can help to increase accuracy, based on the prior identification of the Regions of Interest (RoIs) of the human body. Recently, in the field of Artificial Intelligence, Machine Learning and Deep Learning techniques have also gained popularity due to the power of Convolutional Neural Networks (CNNs) for object recognition and classification. The main focus of this work is to detect human body parts, in a specific position that is lying on a bed, through RGB and Thermal images. The proposed methodology focuses on the identification and classification of human body parts (head, torso, and arms) from both RGB and Thermal images using a CNN based on an open-source implementation. The method uses a supervised learning model that can run in edge devices, e.g. Raspberry Pi 4, and results have shown that, under normal operating conditions, an accuracy in the detection of the head of 98.97% (98.4% confidence) was achieved for RGB images and 96.70% (95.18% confidence) for thermal images. Moreover, the overall performance of the thermal model was lower when compared with the RGB model. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

TherMobile: Measuring Body Temperature Using a Mobile Device

The spread of COVID-19 issues high demand on measuring body temperature, which necessitates thermometers. To alleviate a burden to equip/carry thermometers, this paper develops a framework “TherMobile”that measures body temperature using a commercial-off-the-shelf smartphone that most people carry everywhere. Considering that most (if not all) smartphones have a temperature sensor on its battery, we utilize heat transfer from a body part that makes contact with the smartphone, to the smartphone battery. To this end, we collect a time series of the smartphone battery temperature for different pairs of the initial temperature of the smartphone battery and the temperature of a body part, and then classify them. To enable the data collection and classification to infer the temperature of the body part, we address important practical issues, including how to gather data for different target temperatures of a body part (although human body temperature is not controllable), and how to minimize a burden for individual users to gather all necessary data. Our experiments demonstrate that “TherMobile”achieves 90.0% accuracy of measuring body temperature with 1.0°C granularity, enabling a commercial-off-the-shelf smartphone to substitute for a thermometer without any additional hardware. IEEE

Novel Drone Design Using an Optimization Software with 3D Model, Simulation, and Fabrication in Drone Systems Research

This paper presents the design of a small size Unmanned Aerial Vehicle (UAV) using the 3DEXPERIENCE software. The process of designing the frame parts involves many methods to ensure the parts can meet the requirements while conforming to safety and industry standards. The design steps start with the selection of materials that can be used for the drone, which are polylactic acid (PLA), acrylonitrile styrene acrylate (ASA), and acrylonitrile butadiene styrene (ABS). The drone frame consists of four main parts, which are the center top cover (50 g), the side top cover (10 g), the middle cover (30 g), and the drone’s arm (80 g). A simulation was carried out to determine the stress, displacement, and weight of the drone’s parts. Additionally, a trade-off study was conducted to finalize the shapes of the parts and the various inputs based on their priorities. The outcome of this new design can be represented in design concepts, which involve the use of the snap hook function to assemble two body parts together, namely the middle cover and the center top cover, without the need of an additional fastener.

Digital thermometer innovation as a body temperature measuring tool for FKIP Mataram university employees during the covid-19 pandemic

The impact of the Covid-19 pandemic requires that every employee in a university maintains his health. One of the most important body parts to maintain during this pandemic is body temperature. The purpose of this study was to develop a non-contact digital thermometer to measure the body temperature of FKIP employees, Mataram University. The digital thermometer development method starts from literature studies, tool specifications, hardware design, software design and thermometer testing to measure employee body temperature in the morning and afternoon. The results of the digital thermometer test showed that the employee's body temperature in the morning with an average of 33.36 °C and in the afternoon with an average of 33.96 °C. So it can be concluded that the Mataram University FKIP employees who came to work did not experience Covid-19 symptoms. © 2022 Published under licence by IOP Publishing Ltd.

The relationship between smartphone use and musculoskeletal symptoms among university students under the influence of COVID-19

With the widespread use of smartphones, more and more applications are offered for different purposes such as academic use and entertainment. However, the increased use time of smartphones will have an impact on users' health. Especially under the influence of COVID-19 this year, people spend more time on electronic products than ever before as everyone stays at home to maintain social distancing. For university students, smartphones also played more functions on studying and socializing. This study aims to examine the correlations between smartphone use and musculoskeletal symptoms among university students during COVID-19 pandemic conditions. A survey was sent to university students through Qualtrics. This survey consists of one demographic questionnaire, one standard Nordic Musculoskeletal Questionnaire, and one post-questionnaire. The demographic questionnaire was designed to collect personal data and smartphone use related information such as the dominance of hand using the smartphone and time spent on the smartphone. The standard Nordic Musculoskeletal Questionnaire was applied to assess musculoskeletal symptoms. Participants were asked whether they have suffered any pain or discomfort in any of nine body parts in the past 12 months. The post-questionnaire was designed to assess the impact on cellphone use frequency under this COVID-19 situation. The completed questionnaires were then entered and analyzed using Statistical Package for the Social Sciences (SPSS). The findings revealed students' musculoskeletal symptoms are a cause for concern and indicated the necessity to improve the ways that smartphones are used. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.