Design of a wearable device for respiratory rate monitoring and detection of anomalous values

The main causes of death in the world are cardiovascular diseases, strokes and respiratory diseases, among which the following stand out: chronic obstructive pulmonary disease and respiratory system infections. Regarding pulmonary function measurement technology, spirometry is the reference standard for the diagnosis and evaluation. This test requires specialized equipment that does not allow it to be performed on an outpatient basis or for constant monitoring. For this reason, the doctor must systematically look for the presence of symptoms that may go unnoticed by the patient and that can be attributed to age, sedentary lifestyle, or the fact of smoking. This is why it would be important to be able to constantly monitor breathing in order to identify irregularities in breathing rates that could be indicative of a respiratory condition.The solution proposed in this article is focused on the design of a prototype of a wearable device that allows the monitoring of the respiratory rate. For this prototype feasibility is analyzed using signals from a database. This device will allow this biometric variable to be identified and will notify when it is outside the normal ranges to suggest an airflow test (spirometry) at a possible early stage of a respiratory condition. This instrumentation system will be integrated into the frame of a pair of glasses, specifically positioning the sensor on the nasal platelets.

A Survey on Yogic Posture Recognition

Yoga has been a great form of physical activity and one of the promising applications in personal health care. Several studies prove that yoga is used as one of the physical treatments for cancer, musculoskeletal disorder, depression, Parkinson's disease, and respiratory heart diseases. In yoga, the body should be mechanically aligned with some effort on the muscles, ligaments, and joints for optimal posture. Postural-based yoga increases flexibility, energy, overall brain activity and reduces stress, blood pressure, and back pain. Body Postural Alignment is a very important aspect while performing yogic asanas. Many yogic asanas including uttanasana, kurmasana, ustrasana, and dhanurasana, require bending forward or backward, and if the asanas are performed incorrectly, strain in the joints, ligaments, and backbone can result, which can cause problems with the hip joints. Hence it is vital to monitor the correct yoga poses while performing different asanas. Yoga posture prediction and automatic movement analysis are now possible because of advancements in computer vision algorithms and sensors. This research investigates a thorough analysis of yoga posture identification systems using computer vision, machine learning, and deep learning techniques.

PACMAN: A Framework for Pulse Oximeter Digit Detection and Reading in A Low-Resource Setting

In light of the COVID-19 pandemic, patients were required to manually input their daily oxygen saturation (SpO2) and pulse rate (PR) values into a health monitoring system—unfortunately, such a process trend to be an error in typing. Several studies attempted to detect the physiological value from the captured image using optical character recognition (OCR). However, the technology has limited availability with high cost. Thus, this study aimed to propose a novel framework called PACMAN (Pandemic Accelerated Human-Machine Collaboration) with a low-resource deep learning-based computer vision. We compared state-of-the-art object detection algorithms (scaled YOLOv4, YOLOv5, and YOLOR), including the commercial OCR tools for digit recognition on the captured images from the pulse oximeter display. All images were derived from crowdsourced data collection with varying quality and alignment. YOLOv5 was the best-performing model against the given model comparison across all datasets, notably the correctly orientated image dataset. We further improved the model performance with the digits auto-orientation algorithm and applied a clustering algorithm to extract SpO2 and PR values. The accuracy performance of YOLOv5 with the implementations was approximately 81.0-89.5%, which was enhanced compared to without any additional implementation. Accordingly, this study highlighted the completion of the PACMAN framework to detect and read digits in real-world datasets. The proposed framework has been currently integrated into the patient monitoring system utilized by hospitals nationwide. IEEE

A Secure Wearable Framework for Stress Detection in Patients Affected by Communicable Diseases

The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences on people's physical and mental well-being. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension among the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even an increased frequency of suicides. Stress monitoring is a critical component of any strategy used to intervene in the case of stress. However, constant monitoring during activities of daily living using clinical means is not viable. During the current pandemic, isolation protocols, quarantines, and overloaded hospitals have made it physically challenging for subjects to be monitored in clinical settings. This study presents a proposal for a framework that uses unobtrusive wearable sensors, securely connected to an artificial intelligence (AI)-driven cloud-based server for early detection of hypertension and an intervention facilitation system. More precisely, the proposed framework identifies the types of wearable sensors that can be utilized ubiquitously, the enabling technologies required to achieve energy efficiency and secure communication in wearable sensors, and, finally, the proposed use of a combination of machine-learning (ML) classifiers on a cloud-based server to detect instances of sustained stress and all associated risks during times of a communicable disease epidemic like COVID-19. © 2001-2012 IEEE.

Non-Invasive Assessment of Lung Water Content Using Chest Patch RF sensors: A Computer Study Using NIH Patients CT Scan Database and AI Classification Algorithms

Chronic heart failure, pulmonary hypertension, acute respiratory distress syndrome (ARDS), coronavirus disease (COVID), and kidney failure are leading causes of death in the U.S. and across the globe. The cornerstone for managing these diseases is assessing patients’volume fluid status in lungs. Available methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. With the recent COVID-19 epidemic, the development of a non-invasive, affordable, and accurate method for assessing lung water content in patients became utmost priority for controlling these widespread respiratory related diseases. In this paper, we propose a novel approach for non-invasive assessment of lung water content in patients. The assessment includes quantitative baseline assessment of fluid accumulation in lungs (normal, moderate edema, edema), as well as continuous monitoring of changes in lung water content. The proposed method is based on using a pair of chest patch radio frequency (RF) sensors and measuring the scattering parameters (S-parameters) of a 915-MHz signal transmitted into the body. To conduct an extensive computational study and validate our results, we utilize a National Institute of Health (NIH) database of computerized tomography (CT) scans of lungs in a diverse population of patients. An automatic workflow is proposed to convert CT scan images to three-dimensional lung objects in High-Frequency Simulation Software and obtain the S-parameters of the lungs at different water levels. Then a personalized machine learning model is developed to assess lung water status based on patient attributes and S-parameter measurements. Decision trees are chosen as our models for the superior accuracy and interpretability. Important patient attributes are identified for lung water assessment. A “cluster-then-predict”approach is adopted, where we cluster the patients based on their ages and fat thickness and train a decision tree for each cluster, resulting in simpler and more interpretable decision trees with improved accuracy. The developed machine learning models achieve areas under the receiver operating characteristic curve of 0.719 and 0.756 for 115 male and 119 female patients, respectively. These results suggest that the proposed “Chest Patch”RF sensors and machine learning models present a promising approach for non-invasive monitoring of patients with respiratory diseases. Author

Smart Homecare Research Translation into Broader Practice: Enablers, Barriers and Directions

Smart homecare utilises advanced technologies to support, improve and promote remote healthcare in homes and communities through collecting and analysing health data and sharing this knowledge with carers and clinicians. With the continuous growth in the world’s older population, smart homecare becomes increasingly crucial in providing in-home care for older adults, allowing the vital healthcare dollars to go further into other critical care needs. In addition, with the rise in the development and utilisation of innovative technologies in healthcare settings, it is vital to ensure that these technologies are guided and approved by the corresponding regulatory bodies such as FDA (Foods and Drug Administration) in the USA and TGA (Therapeutic Good Administration) in Australia. With this premise, this paper identifies four dimensions for researchers to consider when developing smart homecare solutions for in-home remote care: Technology, Data, People, and Operational Environment. The essential interplays amongst these four dimensions are discussed to identify the various enablers and barriers in the successful delivery of smart homecare solutions. As the primary output of this paper, it proposes a conceptual framework to achieve practical in-home care for the older population living independently with the support of technology, while addressing the challenges such as security and privacy of patient data. Secondly, a comprehensive and practical guide featuring seven phases is presented to support and direct researchers in implementing smart homecare solutions for remote care. The proposed framework and the guide aim to make smart homecare research practical and truly translational into broader practice. Author

The Past, Present and Future of the Healthcare Delivery System through Digitalization

The prevalence of chronic diseases and the recent global spread of deadly communicable diseases such as COVID-19 has resulted in changing global health needs which require new and adaptable approaches towards delivering healthcare. Healthcare digitization has aided in dealing with old and new healthcare issues and there is still enormous untapped power. Enough power to transform healthcare delivery systems when safe and accurate aggregation of individual health data is achieved. We explore a typical patient's healthcare pathway for two major chronic conditions, namely cardiovascular and mental diseases. The aim is to reveal healthcare delivery approach changes as used in the past, present, with a look to the future to manage these diseases. Further, we provide a holistic overview of the technologies behind the digital healthcare transformation. The study also offers a roadmap which depicts the evolution in the healthcare delivery system enabled by these technological health advancements and concludes with a critical evaluation of such systems. IEEE

CycleGAN Clinical Image Augmentation Based on Mask Self-Attention Mechanism

With the development of society and the advancement of science and technology, artificial intelligence has also emerged as the times require. In computer vision, deep learning based on convolutional neural networks(CNN) achieves state-of-the-art performance. However, the massive data requirements of deep learning have long been a pain point in the field, especially in the medical field, where it is often difficult (and sometimes impossible) to obtain enough training data for some specific tasks. To overcome insufficient and unbalanced data, in this paper, we focus on the generation and balance of data on radiation-induced pneumonia, an extremely rare disease with a low incidence. As a result, datasets on this disease are extremely sparse and unevenly distributed. To address the above problems, the predecessors' method is often to use generative models to generate data as a complement of the fewer samples to achieve a balanced distribution of data samples. Among various generative models, CycleGAN is widely used in medical image generation due to its cycle consistency to achieve style migration without changing the basic content. However, the original CycleGAN method has many shortcomings, especially in Few-shot and the data unevenly distributed, its performance will be greatly reduced. To make the generated data samples retain the original structure and have finer and clearer details, this paper proposes a mask-based self-attention CycleGAN data augmentation method. A self-attention branch is added to the generator and two different loss functions named Self-Attention Loss and Mask Loss are designed. To stabilize the training process, spectral normalization is introduced to improve the discriminator and MS-SSIM and L1 joint loss are used to improve the original identity loss. The ResNet18 is used to complete classification experiments on the radiation-induced pneumonia dataset and the COVID-19 dataset respectively. Four classification performance indicators: the area under the ROC curve (AUC), Accuracy (ACC), Sensitivity (SEN), and Specificity (SPE) are calculated to verify the effectiveness and generalization of our method. Compared with the original CycleGAN and traditional data augmentation, the classifier trained by data augmentation using our method has outstanding performance in multiple classification indicators and has better classification performance. Experimental results show that our method solves the problem of insufficient samples and data imbalance in the pneumonia dataset by generating high-quality pneumonia images. Code is available at https://github.com/ngfufdrdh/CycleGAN-lung.

Monitoring COVID-19 Patients Using Cardio-Pulmonary Stethoscope RF Technology: Computer Simulation Study Using CT Scans of Patients

In 2020, the COVID-19 pandemic claimed 3 million lives worldwide in span of a year;the death toll is still on rise as of writing of this article. Hospitals around the globe overwhelmed with COVID-19 patients faced medical resource shortages preventing them from providing services to even severe cases, leaving patients to selfcare. The identified COVID-19 patients had to observe the symptoms escalation or take imaging tests such as CT scans to determine the disease progression. While these imaging methods provide detailed accounts of damage inflicted to lungs by COVID-19, they have their own limitations and risks. In this article, we use computer simulations to examine the possibility of using the Cardio-Pulmonary Stethoscope (CPS) to continually monitor the COVID-19 afflicted lungs. Using a CT scan of a real COVID-19 patient, an infection was introduced in the lungs of an anatomically correct digital human model to be studied using simulation method. The preliminary results of simulations showed that the least detectable size of infection was an ellipsoid of 0.9 cubic cm, and the CPS was most sensitive while detecting infection in the lungs without preexisting conditions like edema. Based on the results and resolution, signal sensitivity of the CPS to COVID-19 infection is established and it can be argued that CPS could be an alternative method for continuous monitoring of COVID-19 disease.

An Enhanced Binary Multi-Objective Hybrid Filter-Wrapper Chimp Optimization based Feature Selection Method for COVID-19 Patient Health Prediction

This work aims to discover the relevant factors to predict the health condition of COVID-19 patients by employing a fresh and enhanced binary multi-objective hybrid filter-wrapper chimp optimization (EBMOChOA-FW) based feature selection (FS) approach. FS is a preprocessing approach that has been highly fruitful in medical applications, as it not only reduces dimensionality but also allows us to understand the origins of an illness. Wrappers are computationally expensive but have excellent classification performance, whereas filters are recognized as quick techniques, although they are less accurate. This study presents an advanced binary multi-objective chimp optimization method based on the hybridization of filter and wrapper for the FS task using two archives. In exceptional instances, the initial ChOA version becomes stuck at the local optima. As a result, a novel ChOA termed EBMOChOA is developed here by integrating the Harris Hawk Optimization (HHO) into the original ChOA to improve the optimizer’s search capabilities and broaden the usage sectors. The location change step in the ChOA optimizer is separated into three parts: modifying the population using HHO to produce an HHO-based population;creating hybrid entities according to HHO-based and ChOA-based individuals;and altering the search agent in the light of greedy technique and ChOA’s tools. The effectiveness of the EBMOChOA-FW is proven by comparing it to five other well-known algorithms on nine different benchmark datasets. Then its strengths are applied to three real-world COVID-19 datasets to predict the health condition of COVID-19 patients. Author

Fetus Heart Rate Monitoring: A Preliminary Research Study With Remote Sensing

Currently, hospitals and health care sectors employ low-cost Internet of Things based remote health monitoring systems and labs in order to collect a subject's or patients' real-time data. Such a process can be helpful for the early detection of a healthy newborn life and of critical importance for the survival of these lives. In this article, a preliminary implementation of a system monitoring the fetus heart rate (FHR) has been designed and implemented as a mobile wearable measuring system with remote sensing. The proposed implementation turns out to be an efficient combination of simplicity and cost effectiveness and is accompanied with preliminary accurate measurements of the FHR. The proposed system uses a transceiver module and is capable of efficient data transmission to a remote server station using a IEEE 802.11 b/g/n based wireless network. The patients' data can further be monitored using a smart or satellite phone, or even any well-known internet browser connected to the specific network, thus complying with the health safety distance measures required due to various situations, including that of the COVID-19 pandemic.

A framework of multi-point infrared temperature screening system for COVID-19 pandemic

This paper describes a framework for COVID-19 pandemic screening that includes a multi-infrared temperature sensor. Due to the high risk of transmission of the COVID-19 epidemic in closed areas, it is important to secure these areas in terms of epidemics. Symptoms of COVID-19 disease include fever in patients. Thermal cameras or infrared temperature sensors are used to detect this anomaly in real-time. In this study, a study was carried out on which multiple uses of infrared sensors increase the measurement performance. Additionally, the general concept of an intelligent long-range temperature measurement system with facial recognition support is presented, which may be simply integrated with this approach. © 2021 IEEE.

CoCross: An ICT Platform Enabling Monitoring Recording and Fusion of Clinical Information Chest Sounds and Imaging of COVID-19 ICU Patients.

Monitoring and treatment of severely ill COVID-19 patients in the ICU poses many challenges. The effort to understand the pathophysiology and progress of the disease requires high-quality annotated multi-parameter databases. We present CoCross, a platform that enables the monitoring and fusion of clinical information from in-ICU COVID-19 patients into an annotated database. CoCross consists of three components: (1) The CoCross4Pros native android application, a modular application, managing the interaction with portable medical devices, (2) the cloud-based data management services built-upon HL7 FHIR and ontologies, (3) the web-based application for intensivists, providing real-time review and analytics of the acquired measurements and auscultations. The platform has been successfully deployed since June 2020 in two ICUs in Greece resulting in a dynamic unified annotated database integrating clinical information with chest sounds and diagnostic imaging. Until today multisource data from 176 ICU patients were acquired and imported in the CoCross database, corresponding to a five-day average monitoring period including a dataset with 3477 distinct auscultations. The platform is well accepted and positively rated by the users regarding the overall experience.

MER-WearNet: Medical-Emergency Response Wearable Networking Powered by UAV-Assisted Computing Offloading and WPT

The global outbreak of the 2019-nCoV pneumonia has led to illness and loss of life for a large number of people. Many countries built medical-emergency facilities in remote areas to isolate infected people in an attempt to contain the spread of the virus. Various wearable devices based on smart new fabrics can collect life-relevant data from patients on a continuous basis. However, the computing capacity and battery energy of wearable devices are limited. Prolonging the life cycle of the wearable medical-emergency system for as long as possible, while guaranteeing the effectiveness of the monitoring tasks for the users, is a great challenge. Therefore, Medical-Emergency Response Wearable Networking Powered by UAV-assisted (unmanned aerial vehicle) computing offloading and wireless power transfer (WPT), known as MER-WearNet, is presented in this paper. Due to the ultra-low delay demand in the medical emergency scenario, the proposed scheme uses UAV to charge the wearable devices wirelessly, so that the wearable devices can obtain more energy and ensure the efficient completion of the computing offloading in the shortest possible time. The successive convex optimization (SCP) is used to solve the joint optimization model. Finally, simulation experiments verify the effectiveness of the proposed scheme.

Novel Coronavirus Cough Database: NoCoCoDa.

The current pandemic associated with the novel coronavirus (COVID-19) presents a new area of research with its own set of challenges. Creating unobtrusive remote monitoring tools for medical professionals that may aid in diagnosis, monitoring and contact tracing could lead to more efficient and accurate treatments, especially in this time of physical distancing. Audio based sensing methods can address this by measuring the frequency, severity and characteristics of the COVID-19 cough. However, the feasibility of accumulating coughs directly from patients is low in the short term. This article introduces a novel database (NoCoCoDa), which contains COVID-19 cough events obtained through public media interviews with COVID-19 patients, as an interim solution. After manual segmentation of the interviews, a total of 73 individual cough events were extracted and cough phase annotation was performed. Furthermore, the COVID-19 cough is typically dry but can present as a more productive cough in severe cases. Therefore, an investigation of cough sub-type (productive vs. dry) of the NoCoCoDa was performed using methods previously published by our research group. Most of the NoCoCoDa cough events were recorded either during or after a severe period of the disease, which is supported by the fact that 77% of the COVID-19 coughs were classified as productive based on our previous work. The NoCoCoDa is designed to be used for rapid exploration and algorithm development, which can then be applied to more extensive datasets and potentially real time applications. The NoCoCoDa is available for free to the research community upon request.