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Around the world, a large scale implementation of smart meters is underway. The UK alone will install and configure over 50 million smart meters by the end of 2020. These smart devices enable consumer electricity usage monitoring with a high degree of accuracy. Each device records consumer data at regular intervals, which can be used to identify an...
Citations
... In another paper, the same author discusses the monitoring of consumer electricity usage with high accuracy. Each device records consumer data at regular intervals, allowing for the identification and profiling of routines and habits [8]. Sudden abnormal changes in behavior can be detected based on the analysis of regular activities performed by consumers throughout the day. ...
The utilization of smart meter data has opened new frontiers in the healthcare sector by enabling the detection of health status and daily life activities. A new methodology has been proposed to differentiate between normal and abnormal energy consumption, which can be leveraged to determine the health status of consumers by analyzing load profiling. By visualizing human activities based on electricity usage, there is a growing interest in using smart meter data for detecting health status and Activities of Daily Living (ADLs). The chapter discussed, this innovative approach utilizes the K-Means algorithm to classify the data and identify patterns in the energy usage of individuals. By analyzing this data, potential health issues can be indicated, thereby assisting the healthcare sector in addressing the needs of the elderly population more effectively. This technology has the potential to revolutionize healthcare by providing a non-invasive and cost-effective way to monitor the health of individuals and enable early intervention in case of any health issues.
... Depression is mentioned in [31] as a condition that their outlined energy use monitoring architecture may be able to detect in its early stages. Wider infrastructural architectures required to integrate smart meters with home area networks, consumer access devices (CAD) and cloud computing facilities were reported in Reference [32]. Reference [33] reported that appliances that have been focused on include kettles. ...
Smart health is an essential domain within smart cities. Internet-assisted communication with medical practitioners is now widely used. This is important for elderly or disabled patients, who may not be able to travel but have inexpensive and simple internet access they can take advantage of with video-call consultations. However, smart health based on the Internet of Things (IoT) and smart meters is not without its challenges. This paper reviews the benefits and challenges of innovations in healthcare, with emphasis on IoT and smart meters. A top-level smart health with smart meter system design flowchart is given, and the component and module diagram is proposed.
... Due to the emerging technology in the smart health monitoring nowadays, the energy consumption and usage can be optimized. A recent proposed system called Advanced Metering Infrastructure (AMI) presents new application for monitoring e-health power consumption [19]. ...
Global demographic trends clearly point out that the world population is aging due to a combination of dropping mortality rates and increasing life expectancy. The global community is seeking solutions to address the pressing societal challenge of providing effective and efficient healthcare to the elderly. It is difficult to achieve satisfactory results merely by relying on scaling up conventional healthcare infrastructures. These techniques will not be sufficient to independently assist the elderly to live alone in a house mainly if they are suffering from chronic diseases, thus require continuous health monitoring. It is imperative to exploit the advances in emerging technologies such as biosensors, mobile devices, and communication networks to provide remote health monitoring services along with the physical infrastructural facilities. Remote and continuous monitoring of patients with chronic diseases is being considered as an efficient and cost-effective solution, which will reduce the burden on the elderly and his/her families, as well as on the health government’s expenses. While considerable research and development is being undertaken in this field, most of the current state of the art reflects a lack of a concerted and cohesive approach to develop an integrated remote health monitoring system. This chapter surveys existing pervasive healthcare systems and classifies them as academia based or industrial based, and then it develops a set of criteria to compare these solutions. It discusses some drawbacks of existing solutions and proposes future directions in pervasive healthcare, which are predicted to shape future pervasive healthcare systems. Finally, it proposes a novel healthcare monitoring framework based on an integrated and scalable architecture, which provides flexibility and enables interoperability between myriads of healthcare monitoring devices. The proposed framework relies on the analytics of both evidenced data collected from sensors as well as the massive data collected from social networks. A prototype of the framework has been developed to evaluate the applicability and the efficiency of monitoring and analytics practices.
It is foreseen that the trends for the next decade in healthcare will include more patients requiring care, increased use of technology, need for greater information storage capacity, development of new healthcare delivery models, error reduction, more emphasis on preventative healthcare, faster disease diagnosis, and innovation driven by competition. It is not only important to improve patient care processes, but to also decrease costs while maintaining quality. With the advancement of technology, it is possible to develop reliable and cost‐effective methods to promote the sharing of electronic medical records, obtaining rapid referrals and appointments, and improve practice guidelines and standardization. It is anticipated that patients will become more proactive in their own healthcare by utilizing information available on the internet and with home‐use healthcare technology becoming increasingly available. Patients are beginning to benefit from advanced telemedicine, such as tele‐consultations, telediagnosis, and tele‐monitoring. This enables faster collection of test results and improved follow‐up on diagnosis. Internet‐assisted communication with medical practitioners is now widely used, and can help minimize time‐consuming visits to a practitioner's office. This is important for elderly or disabled patients, who may not be able to travel but have inexpensive and simple internet access which they can take advantage of with video‐call consultations. However, smart health based on internet of things (IoT) and smart devices is not without its challenges. IoT‐based devices capture huge amounts of data, including sensitive information, giving rise to data security and privacy concerns. This chapter reviews the benefits and challenges of innovations in healthcare, with emphasis on IoT and smart devices such as smart meters.
One of the most important issues for people healthcare is how to serve significantly developed facilities to an increasing ratio of needy persons using their financial resource and less available healthcare facilities. Pervasive healthcare service has chosen a positive healthcare solution to many prevailing issues, and it is showing an optimistic conceivable future of the modern healthcare services. Pervasive healthcare may be defined as healthcare service to any needy at anytime and probably anywhere by almost ignoring time and some other constraints including proper coverage and very high-quality real-time services. This chapter discusses an integrated healthcare information system that is designed to use healthcare care and intelligent real-time emergency system, and many applications of healthcare systems, including pathological clinical proof methods as well as the future of pervasive healthcare systems, are also presented and discussed including various aspects.
Pervasive computing allows evolving the notion of autonomous systems that could probably incorporate varied technologies and electronic devices in view of the environmental setting to accomplish a set of rule-based actions and message exchanging. The area of the use of pervasive computing is very big. Evolving pervasive computing expertise is suitable in various aspects of lifetime such as healthcare system, pervasive learning and sport. In simple terms, pervasive healthcare can be precise as healthcare to everyone, every time as well as everywhere by eliminating locality, time and additional limitations although growing mutually its area coverage and overall quality. The wide-ranging definition consists of healthcare maintenance, prevention and check-ups and small-term observing, long-time observing, individual healthcare observing, incidence recognition and supervision as well as emergency mediation and conveyance and proper treatment. This chapter discusses a number of pervasive healthcare uses, relevant challenges as well as related solutions. An incorporated wireless structural design that is proposed to use the abilities of existing and progressing wireless as well as mobile networks for locality supervision, patient monitoring, intellectual emergency system and moveable telemedicine applications is presented and discussed.
A structural health-monitoring system needed to come out from the problem associated due to the rapidly growing population of elderly and the health care demand. The paper discussed the consumer’s electricity usage data, from the smart meter, how to support the healthcare sector by load profiling the normal or abnormal energy consumption. For this work, the measured dataset is taken from 12 households and collected by the smart meter with an interval of an hour for one month. The dataset is grouped according to the features pattern, reduced by matrix-based analysis and classified with K-Means algorithm data mining clustering method. We showed how the clustering result of the Sum Square Error (SSE) has connection trend to indicate normal or abnormal behavior of electricity usage and leads to determine the assumption of the consumer’s health status.
With the revolution in smart infrastructure in the recent past, the smart healthcare system has been paid more considerable attention. The continuous upgradation of electricity meters to smart electricity devices has probed into a new market of intelligent data analysis services, providing aid to the health care systems. This paper presents a unified framework for extracting user behaviour patterns from home-based smart electricity meter data. The structure allows exploration and integration of frequent pattern growth algorithm for pattern mining and application of a variety of machine learning algorithms for categorizing the activities into manually labelled classes along with the implementation of Local Outlier Factor method for detection of an abnormal pattern of the inhabitant of smart homes. To evaluate the proposed framework, the work is implemented on the smart electricity dataset from the United Kingdom by separating the data into four distinct data files meant for the morning, afternoon, evening, and night energy utilization records. The results show a remarkable performance of Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers with kappa statics greater than 0.95 for all time slots data. The resultant frequent device utilization patterns with anomaly score more than the threshold value, reflecting abnormal activity patterns, are found more in evening time data in comparison to other time slots, requiring the immediate attention of concerned healthcare authorities.
