Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

Smart sensing systems for in-home health status and emotional well-being monitoring during COVID-19

The COVID-19 pandemic has restricted the mobility of the population. The experts propose several solutions in order to decrease the number of patients infected with this new virus by treating and monitoring them within the comfort of their own home. A new direction for the research has been identified including healthcare smart sensing systems which can provide medical diagnoses, surveillance, and treatment partially or totally remotely. The field of wearable, smart sensing solutions is becoming nowadays a widely accepted solution characterized also by the increased level of acceptance with regard to home health status monitoring. Pervasive computing and wearable solutions are frequently a topic included in current projects and are expected in new future developments, particularly in the pandemic context which forces people to remain mostly at home. As part of wearable devices the design of textiles, computer science, and smart materials are the three major development directions. The latest developments associated with the monitoring of health status and emotional well-being are presented and discussed in this chapter.info:eu-repo/semantics/submittedVersio

Smart home technology for aging

The majority of the growing population, in the US and the rest of the world requires some degree of formal and or informal care either due to the loss of function or failing health as a result of aging and most of them suffer from chronic disorders. The cost and burden of caring for elders is steadily increasing. This thesis focuses on providing the analysis of the technologies with which a Smart Home is built to improve the quality of life of the elderly. A great deal of emphasis is given to the sensor technologies that are the back bone of these Smart Homes. In addition to the Analysis of these technologies a survey of commercial sensor products and products in research that are concerned with monitoring the health of the occupants of the Smart Home is presented. A brief analysis on the communication technologies which form the communication infrastructure for the Smart Home is also illustrated. Finally, System Architecture for the Smart Home is proposed describing the functionality and users of the system. The feasibility of the system is also discussed. A scenario measuring the blood glucose level of the occupant in a Smart Home is presented as to support the system architecture presented

e-CoVig: a novel mHealth system for remote monitoring of symptoms in COVID-19

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).In 2019, a new virus, SARS-CoV-2, responsible for the COVID-19 disease, was discovered. Asymptomatic and mildly symptomatic patients were forced to quarantine and closely monitor their symptoms and vital signs, most of the time at home. This paper describes e-CoVig, a novel mHealth application, developed as an alternative to the current monitoring paradigm, where the patients are followed up by direct phone contact. The e-CoVig provides a set of functionalities for remote reporting of symptoms, vital signs, and other clinical information to the health services taking care of these patients. The application is designed to register and transmit the heart rate, blood oxygen saturation (SpO2), body temperature, respiration, and cough. The system features a mobile application, a web/cloud platform, and a low-cost specific device to acquire the temperature and SpO2. The architecture of the system is flexible and can be configured for different operation conditions. Current commercial devices, such as oximeters and thermometers, can also be used and read using the optical character recognition (OCR) functionality of the system. The data acquired at the mobile application are sent automatically to the web/cloud application and made available in real-time to the medical staff, enabling the follow-up of several users simultaneously without the need for time consuming phone call interactions. The system was already tested for its feasibility and a preliminary deployment was performed on a nursing home showing promising results.This work was funded by Fundação para a Ciência e Tecnologia (FCT) under the grants e-CoVig—Project 255_596880547, and LARSyS—Project UIDB/50009/2020, by FCT/MCTES through national funds and, when applicable, co-funded EU funds under the grant NICE-HOME—Project UIDB/50008/2020, and by the IT—Instituto de Telecomunicações under grant BI/No. 13—19 May 2020 “AIMHealth”, which is gratefully acknowledged.info:eu-repo/semantics/publishedVersio

Tools for landscape-scale automated acoustic monitoring to characterize wildlife occurrence dynamics

In a world confronting climate change and rapidly shifting land uses, effective methods for monitoring natural resources are critical to support scientifically-informed management decisions. By taking audio recordings of the environment, scientists can acquire presence-absence data to characterize populations of sound-producing wildlife over time and across vast spatial scales. Remote acoustic monitoring presents new challenges, however: monitoring programs are often constrained in the total time they can record, automated detection algorithms typically produce a prohibitive number of detection mistakes, and there is no streamlined framework for moving from raw acoustic data to models of wildlife occurrence dynamics. In partnership with a proof-of-concept field study in the U.S Bureau of Land Management’s Riverside East Solar Energy Zone in southern California, this dissertation introduces a new R software package, AMMonitor, alongside a novel body of work: 1) temporally-adaptive acoustic sampling to maximize the detection probabilities of target species despite recording constraints, 2) values-driven statistical learning tools for template-based automated detection of target species, and 3) methods supporting the construction of dynamic species occurrence models from automated acoustic detection data. Unifying these methods with streamlined data management, the AMMonitor software package supports the tracking of species occurrence, colonization, and extinction patterns through time, introducing the potential to perform adaptive management at landscape scales

Time Synchronization and Its Applications in Wireless Sensor Networks

Time synchronization is an essential component of wireless sensor networks (WSNs) that play a key role in the thriving Internet of Things (IoT), supporting IoT applications from large-scale monitoring & event detection to collaborative interactions. The large-scale applications based on resource-constrained sensor nodes promote the development of WSN time synchronization towards the three major aspects of lower energy consumption, lower computational complexity, and higher multi-hop time synchronization accuracy. It is these three aspects that we focus on in our contributions to the development of WSN time synchronization, which are presented in this thesis together with their applications to optimal bundling and node identification

Adaptive waveform design for cognitive radar

Advances in technology, especially in sensing, robotics, wireless communications, hardware capabilities and the constant need to confront not only the existing but also new and advanced threats are pushing for the need of advanced radar techniques. In this context, Cognitive Radar (CR) is visualized as the next generation multifunctional, smart and adaptive radar that extends its capabilities and responsibilities far beyond the traditional radar. CR incorporates knowledge gained by the interaction with the environment into its operation therefore forming a closed-loop system aiming to enhance the system performance. A very important element of the CR operation is the ability to adaptively design the transmitted waveforms based on the radar objective and the changes in the environment. In this thesis, we present the different aspects involved in the Cognitive Radar concept with deeper focus on the adaptive waveform design of the system aiming to improve the tracking performance. A method of adaptive waveform design within the sensor management problem ensuring that the total transmitted power is reduced compared to the transmission of a fixed waveform is proposed and finally a promising direction towards the multi-sensor resource allocation and waveform design is presented

Right place. Right time. Right tool: guidance for using target analysis to increase the likelihood of invasive species detection

In response to the National Invasive Species Council’s 2016–2018 Management Plan, this paper provides guidance on applying target analysis as part of a comprehensive framework for the early detection of and rapid response to invasive species (EDRR). Target analysis is a strategic approach for detecting one or more invasive species at a specific locality and time, using a particular method and/or technology(ies). Target analyses, which are employed across a wide range of disciplines, are intended to increase the likelihood of detection of a known target in order to maximize survey effectiveness and cost-efficiency. Although target analyses are not yet a standard approach to invasive species management, some federal agencies are employing target analyses in principle and/or in part to improve EDRR capacities. These initiatives can provide a foundation for a more standardized and comprehensive approach to target analyses. Guidance is provided for improving computational information. Federal agencies and their partners would benefit from a concerted effort to collect the information necessary to perform rigorous target analyses and make it available through open access platforms