ORCID Profile
0000-0001-6686-4424
Current Organisation
Federation University
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Publisher: Hindawi Limited
Date: 06-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Springer Science and Business Media LLC
Date: 08-05-2017
Publisher: IGI Global
Date: 2019
DOI: 10.4018/978-1-5225-7277-0.CH009
Abstract: Remote patient monitoring involves the collection of data from wearable sensors that typically requires analysis in real time. The real-time analysis of data streaming continuously to a server challenges data mining algorithms that have mostly been developed for static data residing in central repositories. Remote patient monitoring also generates huge data sets that present storage and management problems. Although virtual records of every health event throughout an in idual's lifespan known as the electronic health record are rapidly emerging, few electronic records accommodate data from continuous remote patient monitoring. These factors combine to make data analytics with continuous patient data very challenging. In this chapter, benefits for data analytics inherent in the use of standards for clinical concepts for remote patient monitoring is presented. The openEHR standard that describes the way in which concepts are used in clinical practice is well suited to be adopted as the standard required to record meta-data about remote monitoring. The claim is advanced that this is likely to facilitate meaningful real time analyses with big remote patient monitoring data. The point is made by drawing on a case study involving the transmission of patient vital sign data collected from wearable sensors in an Indian hospital.
Publisher: IEEE
Date: 11-2014
Publisher: Elsevier BV
Date: 04-2022
Publisher: Wiley
Date: 05-09-2021
DOI: 10.1002/SPE.3023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-07-2021
Publisher: Chapman and Hall/CRC
Date: 13-12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-09-2023
Publisher: IEEE
Date: 12-2010
Publisher: Elsevier BV
Date: 03-2020
Publisher: ACM
Date: 04-10-2004
Publisher: IEEE
Date: 12-2020
Publisher: Elsevier BV
Date: 10-2022
Publisher: MDPI AG
Date: 16-12-2019
DOI: 10.3390/ELECTRONICS8121552
Abstract: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: Springer Science and Business Media LLC
Date: 27-05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 07-2008
Publisher: Wiley
Date: 14-06-2020
DOI: 10.1002/SPE.2849
Abstract: Internet of Things (IoT) is finding application in many areas, particularly in health care where an IoT can be effectively used in the form of an Internet of Medical Things (IoMT) to monitor the patients remotely. The quality of life of the patients and health care outcomes can be improved with the deployment of an IoMT because health care professionals can monitor conditions access the electronic medical records and communicates with each other. This remote monitoring and consultations might reduce the traditional stressful and costly exercise of frequent hospitalization. Also, the rising costs of health care in many developed countries have influenced the introduction of the Healthcare Monitoring Application (HMA) to their existing health care practices. To materialize the HMA concepts for successful deployment for civilian and commercial use with ease, application developers can benefit from a generic, scalable framework that provides significant components for building an HMA. In this chapter, a generic maintainable HMA is advanced by amalgamating the advantages of event‐driven and the layered architecture. The proposed framework is used to establish an HMA with an end‐to‐end Assistive Care Loop Framework (ACLF) to provide a real‐time alarm and assistance to monitor pregnant women.
Publisher: Wiley
Date: 28-07-2021
DOI: 10.1111/EXSY.12772
Abstract: Basketball is a mathematical game with many abstract data interpretations. An average fan ceases to witness the revolution in sports, which is influenced using data science and analytics unless someone brings it to light. Nowadays, teams look at data and tend to make decisions on scouting the player for the team. The decision making for the coaches can be made easier using machine learning algorithms to identify the star potential of players. The paper provides a novel algorithm by building a machine learning model on all players to predict whether the player is a star or not. Besides, an interactive user interface is developed for coaches to input the player's data and to make an informed decision based on the prediction.
Publisher: Elsevier BV
Date: 06-2021
Publisher: IEEE
Date: 23-06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 06-2021
Publisher: Computing in Cardiology
Date: 14-09-2017
Publisher: SAGE Publications
Date: 24-09-2020
Abstract: Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small s les of experts. Results from the evaluation demonstrate the viability of the machine learning technique used.
Publisher: IEEE
Date: 10-2019
Publisher: IEEE
Date: 12-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: IEEE
Date: 04-2014
Publisher: ACM
Date: 04-02-2020
Publisher: IEEE
Date: 08-2011
Publisher: IEEE
Date: 12-2008
Publisher: IGI Global
Date: 2022
DOI: 10.4018/978-1-6684-3662-2.CH050
Abstract: Remote patient monitoring involves the collection of data from wearable sensors that typically requires analysis in real time. The real-time analysis of data streaming continuously to a server challenges data mining algorithms that have mostly been developed for static data residing in central repositories. Remote patient monitoring also generates huge data sets that present storage and management problems. Although virtual records of every health event throughout an in idual's lifespan known as the electronic health record are rapidly emerging, few electronic records accommodate data from continuous remote patient monitoring. These factors combine to make data analytics with continuous patient data very challenging. In this chapter, benefits for data analytics inherent in the use of standards for clinical concepts for remote patient monitoring is presented. The openEHR standard that describes the way in which concepts are used in clinical practice is well suited to be adopted as the standard required to record meta-data about remote monitoring. The claim is advanced that this is likely to facilitate meaningful real time analyses with big remote patient monitoring data. The point is made by drawing on a case study involving the transmission of patient vital sign data collected from wearable sensors in an Indian hospital.
Publisher: IEEE
Date: 11-2019
Publisher: ACM
Date: 14-02-2022
Publisher: MDPI AG
Date: 27-06-2023
DOI: 10.3390/COMPUTERS12070131
Abstract: Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or multiple distributed SDN controllers facing severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (referred to as SDN hereafter) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, a priority scheduling and congestion control algorithm is proposed in the standard SDN, named extended SDN (eSDN), which minimises congestion and performs better than the standard SDN. However, the enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic SDN controller mapping. Often, the same SDN controller gets overloaded, leading to a single point of failure. Our literature review shows that most proposed solutions are based on static SDN controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among the SDN controllers in real-time, eventually increasing the network latency. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static SDN controller to neutralise the on-the-fly traffic burst. Thus, our novel dynamic controller mapping algorithm with multiple-controller placement in the SDN is critical to solving the identified issues. In dSDN, the SDN controllers are mapped dynamically with the load fluctuation. If any SDN controller reaches its maximum threshold, the rest of the traffic will be erted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation.
Publisher: ACM
Date: 14-02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Mark Allen Group
Date: 06-2010
DOI: 10.12968/BJOM.2010.18.6.48312
Abstract: Women have a strong need to be involved in their own maternity care. Pregnancy hand-held records encourage women's participation in their maternity care gives them an increased sense of control and improves communication among care providers. They have been successfully used in the UK and New Zealand for almost 20 years. Despite evidence that supports the use of hand-held records, widespread introduction has not occurred in Australia. The need for an electronic version of pregnancy hand-held records has become apparent, especially after the introduction of the Electronic Medical Record in Australia. A personal digital assistant (PDA) was developed as an interactive antenatal electronic maternity record that health-care providers could use in any setting and women could access using the internet. This article will describe the testing of the antenatal electronic maternity record.
Publisher: IEEE
Date: 02-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11531371_56
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-08-2020
Publisher: IEEE
Date: 02-2019
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 06-2022
Publisher: Elsevier BV
Date: 06-2021
Publisher: IEEE
Date: 2005
DOI: 10.1109/ICMB.2005.16
No related grants have been discovered for Venki Balasubramanian.