Evaluation of a Video Generation Method Linking Dance and Scenes

Owing to the impact of COVID-19, the venues for dancers to perform have shifted from the stage to the media. In this study, we focus on the creation of dance videos that allow audiences to feel a sense of excitement without disturbing their awareness of the dance subject and propose a video generation method that links the dance and the scene by utilizing a sound detection method and an object detection algorithm. The generated video was evaluated using the Semantic Differential method, and it was confirmed that the proposed method could transform the original video into an uplifting video without any sense of discomfort. © 2023 SPIE.

Video-Based Online Interactive System Using CNN Segmentation Method

In recent years, the COVID-19 has made it difficult for people to interact with each other face-to-face, but various kinds of social interactions are still needed. Therefore, we have developed an online interactive system based on the image processing method, that allows people in different places to merge the human region of two images onto the same image in real-time. The system can be used in a variety of situations to extend its interactive applications. The system is mainly based on the task of Human Segmentation in the CNN (convolution Neural Network) method. Then the images from different locations are transmitted to the computing server through the Internet. In our design, the system ensures that the CNN method can run in real-time, allowing both side users can see the integrated image to reach 30 FPS when the network is running smoothly. © 2023 IEEE.

A Survey on Machine Learning Techniques for Multimodal Biomedical Signal Processing

In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented. © 2023 IEEE.

Artificial Intelligence-based Speech Signal for COVID-19 Diagnostics

The speech signal has numerous features that represent the characteristics of a specific language and recognize emotions. It also contains information that can be used to identify the mental, psychological, and physical states of the speaker. Recently, the acoustic analysis of speech signals offers a practical, automated, and scalable method for medical diagnosis and monitoring symptoms of many diseases. In this paper, we explore the deep acoustic features from confirmed positive and negative cases of COVID-19 and compare the performance of the acoustic features and COVID-19 symptoms in terms of their ability to diagnose COVID-19. The proposed methodology consists of the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images to extract deep audio features. In addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 from acoustic features compared to COVID-19 symptoms, achieving an accuracy of 97%. The experimental results show that the proposed method remarkably improves the accuracy of COVID-19 detection over the handcrafted features used in previous studies. © 2022 ACM.

DenseNet201 Model for Robust Detection on Incorrect Use of Mask

In 2022, the COVID-19 pandemic is still occurring. One of the optimal prevention efforts is to wear a mask properly. Several previous studies have classified the use of masks incorrectly. However, the accuracy resulting from the classification process is not optimal. This research aims to use the transfer learning method to achieve optimal accuracy. In this research, we used three classes, namely without a mask, incorrect mask, and with a mask. The use of these three classes is expected to be more detailed in detecting violations of the use of masks on the face. The classification method used in this research uses transfer learning as feature extraction and Global Average Pooling and Dense layers as classification layers. The transfer learning models used in this research are MobileNetV2, InceptionV3, and DenseNet201. We evaluate the three models' accuracy and processing time when using video data. The experimental results show that the DenseNet201 model achieves an accuracy of 93%, but the processing time per video frame is 0.291 s. In contrast to the MobileNetV2 model, which produces an accuracy of 89% and the processing speed of each video frame is 0.106 s. This result is inversely proportional to accuracy and speed. The DenseNet201 model produces high accuracy but slow processing time, while the MobileNetV2 model is less accurate but has faster processing. This research can be applied in the crowd center to monitor health protocols in the use of masks in the hope of inhibiting the transmission of the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Automated ventilator prototype for COVID-19 patient treatment: the design and development of the electronic system

The coronavirus disease 2019 (COVID-19) pandemic has created an urgent global demand for ventilators, respirators and various resuscitation devices. Various research and development organizations, private companies and individual engineers have collaborated and carried out the development of low-cost ventilation prototypes. In turn, doctors and nurses are collapsed due to the exponential increase in COVID-19 cases. This scenario worsens more when the tasks are manual in nature. The article`s objective to describe the electronic system designed, developed and implemented in a functional prototype of an automatic ventilator in order to be evaluated by a team of health professionals to be later used in cases of health emergencies. This system automates the manual ventilation task aided by a few medical resources in a scenario of scarce resources and is a temporary solution when a respirator is not available. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

Multiparametric indices offer a more comprehensive approach to voice quality assessment by taking into account multiple acoustic parameters. Artificial intelligence technology can be utilized in healthcare to evaluate data and optimize decision-making processes. Mobile devices provide new opportunities for remote speech monitoring, allowing the use of basic mobile devices as screening tools for the early identification and treatment of voice disorders. However, it is necessary to demonstrate equivalence between mobile device signals and gold standard microphone preamplifiers. Despite the increased use and availability of technology, there is still a lack of understanding of the impact of physiological, speech/language, and cultural factors on voice assessment. Challenges to research include accounting for organic speech-related covariables, such as differences in conversing voice sound pressure level (SPL) and fundamental frequency (f0), recognizing the link between sensory and experimental acoustic outcomes, and obtaining a large dataset to understand regular variation between and within voice-disordered individuals. Our study investigated the use of cellphones to estimate the Acoustic Voice Quality Index (AVQI) in a typical clinical setting using a Pareto-optimized approach in the signal processing path. We found that there was a strong correlation between AVQI results obtained from different smartphones and a studio microphone, with no significant differences in mean AVQI scores between different smartphones. The diagnostic accuracy of different smartphones was comparable to that of a professional microphone, with optimal AVQI cut-off values that can effectively distinguish between normal and pathological voice for each smartphone used in the study. All devices met the proposed 0.8 AUC threshold and demonstrated an acceptable Youden index value.

Monitoring the security of audio biomedical signals communications in wearable IoT healthcare.

The COVID-19 pandemic has imposed new challenges on the healthcare industry as hospital staff are exposed to a massive coronavirus load when registering new patients, taking temperatures, and providing care. The Ebola epidemic of 2014 is another example of a pandemic which a hospital in New York decided to use an audio-based communication system to protect nurses. This idea quickly turned into an Internet of Things (IoT) healthcare solution to help to communicate with patients remotely. However, it has grabbed the attention of criminals who use this medium as a cover for secret communication. The merging of signal processing and machine-learning techniques has led to the development of steganalyzers with very higher efficiencies, but since the statistical properties of normal audio files differ from those of purely speech audio files, the current steganalysis practices are not efficient enough for this type of content. This research considers the Percent of Equal Adjacent Samples (PEAS) feature for speech steganalysis. This feature efficiently discriminates the least significant bit stego speech samples from clean ones with a single analysis dimension. A sensitivity of 99.82% was achieved for the steganalysis of 50% embedded stego instances using a classifier based on the Gaussian membership function.

From Art Image to Video/Comic-Image Learning. The Video Spot Stay at Home

From the Covid-19 health emergency entered our lives, the web continues to alleviate moments of isolation with ironic memes, photos and videos that, despite having been considered an irreverence to the masterpieces of Art and/or one of the many uses of irony to exorcise fear, they have favored the staging of video-graphic products with a strong ‘humor' component. Within these premises, in the context of graphic design, this paper will evaluate aspects as the analysis of fashion environment as expressive language of living indoor during Covid-19 pandemic;the audiovisual languages and compositional criteria for the creation and multimedia communication of a video-graphic spot on Stay at home communication campaign. The video-graphic products were analyzed on the basis of: relationship between ‘humor' message and supporting artwork;integration between image and photo-cinematography;figurative languages generative of graphic signs;duration of audiovisual spot;sound component as key to emotional reading;communication strategies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Study on the risk signal mining related to lopinavir/ritonavir based on the US FDA Adverse Event Reporting System.

Objective: To explore the clinical safety of lopinavir/ritonavir (LPV/r) by mining the risk signals of adverse events (AEs) related to LPV/r for the safe application of the drug in the treatment of novel coronavirus pneumonia (COVID-19). Method(s): The risk signals related to LPV/r in AE reports of US FDA Adverse Event Reporting System (FAERS) from the first quarter of 2010 to the third quarter of 2019 were mined by reporting odds ratio (ROR). An AE with reports more than 3 and 95% confidence interval (CI) lower limit of ROR greater than 1 was defined as a positive signal. AEs were counted and classified using the preferred system organ class (SOC) and preferred term (PT) of Medical Dictionary for Regulatory Activities (MedDRA). The PTs of top 50 adverse event reports and signal strength were selected and analyzed. Result(s): From the first quarter of 2010 to the third quarter of 2019, a total of 13 335 AE reports with LPV/r as the primary suspicious drug were reported in the FAERS database. Four hundred and fifty-five AE risk signals with reports more than 3 and the 95%CI lower limit of ROR greater than 1 were detected, involving 7 718 AE reports. The top 2 system organs involved in AE reports were "injury, poisoning and procedural complications" [13.6% (1 051/7 718)] and "pregnancy, puerperium and perinatal conditions" [11.7% (899/7 718)]. However, 998 (95.0%) of 1051 AE reports involved in "injury, poisoning and procedural complications" were related to drug exposure during pregnancy. The system organ with the highest signals was "congenital, familial and genetic disorders" [16.3% (74/455)]. In addition, 144 AEs caused by drug interactions were detected, which ranked the 7th in the AE reports. Conclusion(s): The risk signals of fetal, neonatal and infant abnormalities related to LPV/r during pregnancy were detected, suggesting that attention should be paid to the risk of using LPV/r in pregnant women and infants. The interaction between LPV/r and other drugs was also worthy of attention.Copyright © 2020 by the Chinese Medical Association.

EFFICIENT COVID-19 DISEASE DIAGNOSIS BASED ON COUGH SIGNAL PROCESSING AND SUPERVISED MACHINE LEARNING

The spread of the coronavirus has claimed the lives of millions worldwide, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of the coronavirus to limit its spread. In this work, we propose an automated system to detect COVID-19 based on the cough as one of the most important infection indicators. Several studies have shown that coughing accounts for 65% of the total symptoms of infection. The proposed system is mainly based on three main steps: first, cough signal detection and segmentation;second, cough signal extraction;and third, three techniques of supervised machine learning-based classification: Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Decision Tree (DT). Our proposed system showed high performance through good accuracy values, where the best accuracy for classifying female coughs was 99.6% using KNN and 88% for males using SVM. © 2022 by the Authors.

A Genomic Signal Processing-Based Coronavirus Classification Model Using Deep Learning with Web-Based Console

Various strains of Coronavirus have led to numerous deaths worldwide with CoViD-19 being the most recent. Hence, the need for various research studies to determine and develop technologies that would reduce the spread of this virus as well as aid in the early diagnosis of the disease. The Severe Acute Respiratory Syndrome CoV (SARS-CoV), which emerged in 2003, Middle East Respiratory Syndrome CoV (MERS-CoV) in 2012 and Severe Acute Respiratory Syndrome CoV 2 (SARS-CoV-2) which is generally regarded as CoViD-19, in 2019 have very similar symptoms and genetics. Without proper diagnosis of these strains, they may be mistaken for one another. Therefore, there is a need to distinguish CoViD-19 from the other two Coronaviruses to enhance prompt and specific treatment. In this study, we developed a deep learning model with a web console for the classification of genomic sequences of the three Coronavirus strains using genomic signal processing. The DNA sequences harvested from the Virus Pathogen Database and Analysis Resource (ViPR) was used as dataset and these sequences were transformed to RGB images using Voss and Z-curve encodings. A convolutional neural network (CNN) model was consequently used for classification and incorporated in a web application platform developed with the Django framework. The results of the transformation of the images highlights the similarities of the three coronaviruses in terms of visual and genetic characteristics with the CNN model distinctly classifying SARS-CoV-2, SARS-CoV and MERS-CoV with a training and validation accuracies of 95.58% and 85% respectively which compares favourably with other results in the literature. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Research on fatigue identification methods based on low-load wearable ECG monitoring devices

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments. © 2023 Author(s).

Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform

The work in this paper helps study cardiac rhythms and the electrical activity of the heart for two of the most critical cardiac arrhythmias. Various consumer devices exist, but implementation of an appropriate device at a certain position on the body at a certain pressure point containing an enormous number of blood vessels and developing filtering techniques for the most accurate signal extraction from the heart is a challenging task. In this paper, we provide evidence of prediction and analysis of Atrial Fibrillation (AF) and Ventricular Fibrillation (VF). Long-term monitoring of diseases such as AF and VF occurrences is very important, as these will lead to occurrence of ischemic stroke, cardiac arrest and complete heart failure. The AF and VF signal classification accuracy are much higher when processed on a Graphics Processor Unit (GPU) than Central Processing Unit (CPU) or traditional Holter machines. The classifier COMMA-Z filter is applied to the highly-sensitive industry certified Bio PPG sensor placed at the earlobe and computed on GPU.

Object detection and activity recognition in video surveillance using neural networks

Purpose: This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy. Design/methodology/approach: The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods. Findings: The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus. Originality/value: This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection. © 2023, Emerald Publishing Limited.

This work was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number P50NS108676 and Moore Foundation

The increasingly remote workforce resulting from the global coronavirus pandemic has caused unprecedented cybersecurity concerns to organizations. Considerable evidence has shown that one-pass authentication fails to meet security needs when the workforce work from home. The recent advent of continuous authentication (CA) has shown the potential to solve this predicament. In this paper, we propose NF-Heart, a physiological-based CA system utilizing a ballistocardiogram (BCG). The key insight is that the BCG measures the body's micro-movements produced by the recoil force of the body in reaction to the cardiac ejection of blood, and we can infer cardiac biometrics from BCG signals. To measure BCG, we deploy a lightweight accelerometer on an office chair, turning the common chair into a smart continuous identity "scanner". We design multiple stages of signal processing to decompose and transform the distorted BCG signals so that the effects of motion artifacts and dynamic variations are eliminated. User-specific fiducial features are then extracted from the processed BCG signals for authentication. We conduct comprehensive experiments on 105 subjects in terms of verification accuracy, security, robustness, and long-term availability. The results demonstrate that NF-Heart achieves a mean balanced accuracy of 96.45% and a median equal error rate of 3.83% for CA. The proposed signal processing pipeline is effective in addressing various practical disturbances. © 2023 ACM.

Medical Applications of Nonadditive Entropies.

The Boltzmann-Gibbs additive entropy SBG=-k∑ipilnpi and associated statistical mechanics were generalized in 1988 into nonadditive entropy Sq=k1-∑ipiqq-1 and nonextensive statistical mechanics, respectively. Since then, a plethora of medical applications have emerged. In the present review, we illustrate them by briefly presenting image and signal processings, tissue radiation responses, and modeling of disease kinetics, such as for the COVID-19 pandemic.

A Fast Initial Response Approach to Sequential Financial Surveillance

Consider the problem of financial surveillance of a heavy-tailed time series modeled as a geometric random walk with log-Student's t increments assuming a constant volatility. Our proposed sequential testing method is based on applying the recently developed taut string (TS) univariate process monitoring scheme to the gaussianized log-differenced process data. With the signal process given by a properly scaled total variation norm of the nonparametric taut string estimator applied to the gaussianized log-differences, the change point detection procedure is constructed to have a desired in-control (IC) average run length (ARL) assuming no change in the process drift. If a change in the process drift is imminent, the proposed approach offers an effective fast initial response (FIR) instrument for rapid yet reliable change point detection. This framework may be particularly advantageous for protection against imminent upsets in financial time series in a turbulent socioeconomic and/or political environment. We illustrate how the proposed approach can be applied to sequential surveillance of real-world financial data originating from Meta Platforms, Inc. (FB) stock prices and compare the performance of the TS chart to that of the more prominent CUSUM and CUSUM FIR charts at flagging the COVID-19 related crash of February 2020. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Smart and Portable IoT Drug Dispensing System for Elderly and Disabled Person

The necessity of modern intensive care units (ICU) based on IoT is becoming obvious as a result of the population boom and, most notably, coronavirus disease (COVID-19). The continual monitoring of patients' vital indicators (Blood Pressure, ECG, Heart Rate, Blood Saturation, Body Temperature) is one of the most important aspects of an ICU. Existing improvements in informatics, signal processing, or engineering, which potentially reduce the pressure on ICUs, have yet to be implemented. It's possible due to a lack of user participation in research and development. This manuscript focuses on the improvement of a completely integrated system where the doctors can directly connect to patients through the Smart Portable ICU, and physicians can access the patients. Thus, the crucial boundaries of a patient to the concerned specialist at a far-off position have been resolved simply and helpfully. Thus, the specialist can attend to the patient remotely and infuse lifesaving drugs from the distant area if necessary. © 2022 IEEE.

Breathing detection using thermal facial landmark with DICOM file

To limit the spread of COVID-19, thermal screening cameras were installed everywhere. These cameras observe many thermal faces. These thermal face data are generally used to monitor strange temperatures for COVID-19 screening or to maintain social distancing. Big data of Thermal face generated everywhere should be used in the more practical functions. We proposed a method to measure non-contact breathing signals using thermal face data. In addition, breathing signals data estimated from thermal face data was converted to DICOM waveform Information Object Definitions (IODs) for interoperability management of medical data. The proposed method was tested on a golden reference (chest belt) with a mean accuracy of 93.52 %. a proposed method that can extract breathing signals using thermal screening cameras that are widely available around the world and manage data as healthcare interoperability information can show important potential in the public, telemedicine field in the future. © 2022 IEEE.