System to Recommend the Best Place to Live Based on Wellness State of the User Employing the Heart Rate Variability

Lacuesta, Raquel; García, Laura; García‐Magariño, Iván; Lloret, Jaime

doi:10.1109/access.2017.2702107

Cited by 19 publications

(19 citation statements)

References 34 publications

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“…Higher HRV values are considered more beneficial. Lower HRV values indicate equidistant heart beats and are associated with stress [ 28 ]. Pulse sensors as the one proposed for HR monitoring and ECG sensors can be employed for monitoring this parameter.…”

Section: Methodsmentioning

confidence: 99%

Physical Wellbeing Monitoring Employing Non-Invasive Low-Cost and Low-Energy Sensor Socks

García

Parra

Jiménez

et al. 2018

Sensors

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Determining and improving the wellbeing of people is one of the priorities of the OECD countries. Nowadays many sensors allow monitoring different parameters in regard to the wellbeing of people. These sensors can be deployed in smartphones, clothes or accessories like watches. Many studies have been performed on wearable devices that monitor certain aspects of the health of people, especially for specific diseases. In this paper, we propose a non-invasive low-cost and low-energy physical wellbeing monitoring system that provides a wellness score based on the obtained data. We present the architecture of the system and the disposition of the sensors on the sock. The algorithm of the system is presented as well. The wellness threshold evaluation module allows determining if the monitored parameter is within healthy ranges. The message forwarding module allows decreasing the energy consumption of the system by detecting the presence of alerts or changes in the data. Finally, a simulation was performed in order to determine the energy consumption of the system. Results show that our algorithm allows saving 44.9% of the initial energy in 10,000 min for healthy people.

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Section: Methodsmentioning

confidence: 99%

Physical Wellbeing Monitoring Employing Non-Invasive Low-Cost and Low-Energy Sensor Socks

García

Parra

Jiménez

et al. 2018

Sensors

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“…Variability can be used in different ways in smart real estate. Lacuesta et al [53] introduced a recommender system based on big data generated by heart rate variability in different patients, and they recommended places that allow the person to live with the highest wellness state. Similarly, Lee and Byrne [54] investigated the impact of portfolio size on real estate funds and argued that big data with larger variability can be used to assess the repayment capabilities of larger organizations.…”

Section: Variabilitymentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

116

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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

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“…As a future work, we are planning to develop a QoL assessment system for Smart Cities based on our previous work in [107] and also, employing the knowledge obtained from this paper to choose the best wireless technology in order to establish communication among several devices.…”

Section: Discussionmentioning

confidence: 99%

Wireless Technologies for IoT in Smart Cities

García

Jiménez

Taha

et al. 2018

NPA

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As cities continue to grow, numerous initiatives for Smart Cities are being conducted. The concept of Smart City encompasses several concepts being governance, economy, management, infrastructure, technology and people. This means that a Smart City can have different communication needs. Wireless technologies such as WiFi, ZigBee, Bluetooth, WiMax, 4G or LTE (Long Term Evolution) have presented themselves as solutions to the communication needs of Smart City initiatives. However, as most of them employ unlicensed bands, interference and coexistence problems are increasing. In this paper, the wireless technologies available nowadays for IoT (Internet of Things) in Smart Cities are presented. Our contribution is a review of wireless technologies, their comparison and the problems that difficult coexistence among them. In order to do so, the characteristics and adequacy of wireless technologies to each domain are considered. The problems derived of over-crowded unlicensed spectrum and coexistence difficulties among each technology are discussed as well. Finally, power consumption concerns are addressed. 24 As the initiatives taken to turn a city into a smart city may be varied and have different requirements, employing one unified technology throughout the city may not be efficient. Although wired solutions present a reliable channel to transmit data, the cost of deploying a wired network that connects all devices is extremely high and the result would be considerably inefficient. Wireless technologies have presented themselves as a solution to the rapid growth of devices that are connected to the network as well as the increasing demand of services that allow monitoring cities. IoT is gaining rapid popularity and it is being utilized for transport, health, environment, animal monitoring and smart metering applications [4,5]. Wireless Sensor Networks (WSN) are being employed all over the world as a low-cost and low-energy consuming method to provide a communication mechanism [6]. However, wireless technologies are varied and their utilization should be considered depending on the application. Traffic type, distance, energy consumption or number of nodes are some of the factors that should be considered when deciding how to transmit the gathered data. Moreover, the abuse of wireless technologies may derive in some problems.The majority of the wireless technologies available nowadays employ the 2.4 GHz ISM (Industrial, Scientific and Medical) band. It has not been a great problem until wireless devices started growing at a fast pace. Cable connections between computers and peripherals are being replaced by technologies such as Bluetooth [7]. It is also being employed by home automation systems or telemedicine applications [8]. ZigBee is popular for monitoring systems where a great number of nodes and low energy consumption are required. Services that require interaction with humans are usually utilized with PCs, smartphones or tablets, such as Ambient Assisted Living (AAL) and healthcare systems, and employ WiFi or mo...

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System to Recommend the Best Place to Live Based on Wellness State of the User Employing the Heart Rate Variability

Cited by 19 publications

References 34 publications

Physical Wellbeing Monitoring Employing Non-Invasive Low-Cost and Low-Energy Sensor Socks

Physical Wellbeing Monitoring Employing Non-Invasive Low-Cost and Low-Energy Sensor Socks

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Wireless Technologies for IoT in Smart Cities

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