207 research outputs found

    Continuous monitoring of health and mobility indicators in patients with cardiovascular disease: a review of recent technologies

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    Cardiovascular diseases kill 18 million people each year. Currently, a patient’s health is assessed only during clinical visits, which are often infrequent and provide little information on the person’s health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring

    Advanced Sensing, Fault Diagnostics, and Structural Health Management

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    Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries. With the advancement of science and technology, modern structural and mechanical systems are becoming more and more complex. Due to the continuous nature of operation and utilization, modern systems are heavily susceptible to faults. Hence, the operational reliability and safety of the systems can be greatly enhanced by using the multifaced strategy of designing novel sensing technologies and advanced intelligent algorithms and constructing modern data acquisition systems and structural health monitoring techniques. As a result, this research domain has been receiving a significant amount of attention from researchers in recent years. Furthermore, the research findings have been successfully applied in a wide range of fields such as aerospace, manufacturing, transportation and processes

    Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II

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    The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

    Bioelectronic Sensor Nodes for Internet of Bodies

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    Energy-efficient sensing with Physically-secure communication for bio-sensors on, around and within the Human Body is a major area of research today for development of low-cost healthcare, enabling continuous monitoring and/or secure, perpetual operation. These devices, when used as a network of nodes form the Internet of Bodies (IoB), which poses certain challenges including stringent resource constraints (power/area/computation/memory), simultaneous sensing and communication, and security vulnerabilities as evidenced by the DHS and FDA advisories. One other major challenge is to find an efficient on-body energy harvesting method to support the sensing, communication, and security sub-modules. Due to the limitations in the harvested amount of energy, we require reduction of energy consumed per unit information, making the use of in-sensor analytics/processing imperative. In this paper, we review the challenges and opportunities in low-power sensing, processing and communication, with possible powering modalities for future bio-sensor nodes. Specifically, we analyze, compare and contrast (a) different sensing mechanisms such as voltage/current domain vs time-domain, (b) low-power, secure communication modalities including wireless techniques and human-body communication, and (c) different powering techniques for both wearable devices and implants.Comment: 30 pages, 5 Figures. This is a pre-print version of the article which has been accepted for Publication in Volume 25 of the Annual Review of Biomedical Engineering (2023). Only Personal Use is Permitte

    Machine Learning for Biosensors

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    Biosensors have become increasingly popular as diagnostic tools due to their ability to detect and quantify biological analytes in a wide range of applications. With the growing demand for faster and more reliable biosensing devices, machine learning has become a valuable tool in enhancing biosensor performance. In this report, we review recent progress in the application of machine learning to biosensors. We discuss the potential benefits of using machine learning in biosensors, including improved sensitivity, selectivity, and accuracy. We also discuss the various machine learning techniques that have been applied to biosensors, including data preprocessing, feature extraction, and classification and data analysis models. The potential benefits of machine learning in biosensors are discussed, including the ability to analyze large and complex data sets, to detect subtle changes in biomolecular interactions, and to provide real-time monitoring of biological processes. The challenges associated with the integration of machine learning and biosensors are also addressed, including data availability, sensor performance, and computational requirements. We further highlight the challenges and opportunities for the integration of machine learning and biosensors, including the development of portable and low-cost biosensors, and the use of machine learning algorithms for efficient data analysis. Finally, we provide an outlook on future trends and emerging technologies in the field, including the use of artificial intelligence and deep learning algorithms for biosensors, and the potential for creating a fully autonomous biosensing system

    Fundamentals

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    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

    Sistema vestible para medida de biomarcadores

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    Este Trabajo de Fin de Grado tiene como objetivo el desarrollo de un dispositivo que ofrezca suficiente flexibilidad y reprogramabilidad como para ser utilizado en diversas aplicaciones enfocadas a la adquisición de bioseñales y biomarcadores, es decir, se pretende crear un único dispositivo que mediante modificaciones de software y las mínimas posibles de hardware, se pueda utilizar en la adquisición de distintos parámetros y señales presentes en el cuerpo humano. En concreto, se va a ejemplificar como dicho dispositivo se puede emplear en la adquisición de la señal cardíaca y en el calculo del porcentaje del nivel de saturación de oxígeno en sangre, haciendo cambios mínimos al apartado físico. Se pretende que según la aplicación, el dispositivo diseñado pueda ser utilizado en su función de dispositivo vestible. Para lograr estos objetivos, el proyecto está basado en la tecnología SoC (System on a chip), la cual permite aglutinar gran cantidad de componentes electrónicos en un mismo empaquetado de reducido tamaño. Con la intencionalidad de simplificar el uso para un usuario final, se desarrolla una aplicación Android, la cual es capaz de comunicarse con el dispositivo creado vıa Bluetooth Low Energy, mostrar los resultados obtenidos para la medida concreta que se esté realizando y almacenar estos para su posible consulta a futuro.This Bachelor’s Thesis aims to develop a device that offers enough flexibility and reprogrammability to be used in various applications focused on the acquisition of biosignals and biomarkers, that is, it is intended to create a single device that through software modifications and the minimum possible modifications of hardware can be used in the acquisition of different parameters and signals present in the human body. Specifically, it is going to be exemplified how this device can be used in the acquisition of the cardiac signal and in the calculation of the percentage of oxygen saturation level in blood, making minimal changes to the physical section. It is intended that depending on the application, the designed device can be used in its function as a wearable device. To achieve these goals, the project is based on SoC (System on a chip) technology, which allows to agglutinate a large number of electronic components in the same small package. With the intention of simplifying the use for an end user, an Android application is developed, which is able to communicate with the device created via Bluetooth Low Energy, display the results obtained for the specific measurement being performed and store them for possible future reference

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
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