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Book ChapterDOI

Mining and monitoring human activity patterns in smart environment-based healthcare systems

TL;DR: The proposed model detects appliance usage in particular with reliable accuracy and mining is done purely based on absolute prediction along with clustering models.
Abstract: Rapid evolution of sensing technology and increasing power computation has resulted in the emergence of smart environments with smart health services. Smart environments can generate hundreds of thousands of transactions per day and storage over the long term is a major issue. Therefore smart environment big data is utilized, which stores large volumes of datasets, both structured and unstructured. Healthcare services are recent and challenging aspects in analytics and sensor technology. People migrating from rural areas to urban areas affect healthcare services to a large extent. Due to migration and developing technology, cities around the world are investing in digital transformation, which aims to provide people with a healthier environment. New healthcare applications are based on activity recognition of people and can include wearable or ambient sensors to gather information related to health and human activity. Consumption of energy is analyzed along with the activity pattern of humans by determining the level of appliance usage that relates to human activity. The pattern determined is used to monitor elderly people living alone. The activity monitored can be learned just from appliances and their time usage. The proposed model detects appliance usage in particular with reliable accuracy. Mining is done purely based on absolute prediction along with clustering models.
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Journal ArticleDOI
TL;DR: The managerial implication of this article is that organizations can use the findings of the critical analysis to reinforce their strategic arrangement of smart systems and big data in the healthcare context, and hence better leverage them for sustainable organizational invention.
Abstract: Organized evaluation of various big data and smart system technology in healthcare context.Proposed a conceptual model on Big data enabled Smart Healthcare System Framework (BSHSF).We extract some depth information (some relevant examples) about advanced healthcare system.In depth study about state-of-the-art big data and smart healthcare system in parallel. In the era of big data, recent developments in the area of information and communication technologies (ICT) are facilitating organizations to innovate and grow. These technological developments and wide adaptation of ubiquitous computing enable numerous opportunities for government and companies to reconsider healthcare prospects. Therefore, big data and smart healthcare systems are independently attracting extensive attention from both academia and industry. The combination of both big data and smart systems can expedite the prospects of the healthcare industry. However, a thorough study of big data and smart systems together in the healthcare context is still absent from the existing literature. The key contributions of this article include an organized evaluation of various big data and smart system technologies and a critical analysis of the state-of-the-art advanced healthcare systems. We describe the three-dimensional structure of a paradigm shift. We also extract three broad technical branches (3T) contributing to the promotion of healthcare systems. More specifically, we propose a big data enabled smart healthcare system framework (BSHSF) that offers theoretical representations of an intra and inter organizational business model in the healthcare context. We also mention some examples reported in the literature, and then we contribute to pinpointing the potential opportunities and challenges of applying BSHSF to healthcare business environments. We also make five recommendations for effectively applying `BSHSF to the healthcare industry. To the best of our knowledge, this is the first in-depth study about state-of-the-art big data and smart healthcare systems in parallel. The managerial implication of this article is that organizations can use the findings of our critical analysis to reinforce their strategic arrangement of smart systems and big data in the healthcare context, and hence better leverage them for sustainable organizational invention.

233 citations

Journal ArticleDOI
TL;DR: This paper proposes a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications and proposes the use of frequent pattern mining, cluster analysis, and prediction to measure and analyze energy usage changes sparked by occupants’ behavior.
Abstract: Nowadays, there is an ever-increasing migration of people to urban areas. Health care service is one of the most challenging aspects that is greatly affected by the vast influx of people to city centers. Consequently, cities around the world are investing heavily in digital transformation in an effort to provide healthier ecosystems for people. In such a transformation, millions of homes are being equipped with smart devices (e.g., smart meters, sensors, and so on), which generate massive volumes of fine-grained and indexical data that can be analyzed to support smart city services. In this paper, we propose a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications. We propose the use of frequent pattern mining, cluster analysis, and prediction to measure and analyze energy usage changes sparked by occupants’ behavior. Since people’s habits are mostly identified by everyday routines, discovering these routines allows us to recognize anomalous activities that may indicate people’s difficulties in taking care for themselves, such as not preparing food or not using a shower/bath. This paper addresses the need to analyze temporal energy consumption patterns at the appliance level, which is directly related to human activities. For the evaluation of the proposed mechanism, this paper uses the U.K. Domestic Appliance Level Electricity data set—time series data of power consumption collected from 2012 to 2015 with the time resolution of 6 s for five houses with 109 appliances from Southern England. The data from smart meters are recursively mined in the quantum/data slice of 24 h, and the results are maintained across successive mining exercises. The results of identifying human activity patterns from appliance usage are presented in detail in this paper along with the accuracy of short- and long-term predictions.

153 citations

Journal ArticleDOI
TL;DR: The potential of the applicability of this kind of techniques to provide profitable services of smart cities, such as the management of the energy consumption and comfort in smart buildings, and the detection of travel profiles in smart transport are shown.
Abstract: This paper presents the main foundations of big data applied to smart cities. A general Internet of Things based architecture is proposed to be applied to different smart cities applications. We describe two scenarios of big data analysis. One of them illustrates some services implemented in the smart campus of the University of Murcia. The second one is focused on a tram service scenario, where thousands of transit-card transactions should be processed. Results obtained from both scenarios show the potential of the applicability of this kind of techniques to provide profitable services of smart cities, such as the management of the energy consumption and comfort in smart buildings, and the detection of travel profiles in smart transport.

142 citations

Journal ArticleDOI
TL;DR: The modular approach for IoT applications—the context engine—to smart health problems is applied, enabling the ability to grow with available data, use general-purpose machine learning, and reduce compute redundancy and complexity.
Abstract: The Internet of Things (IoT) envisions to create a smart, connected city that is composed of ubiquitous environmental and user sensing along with distributed, low-capacity computing. This provides ample information regarding the citizens in various smart environments. We can leverage this people-centric information, provided by the smart city infrastructure, to improve “smart health” applications: user data from connected wearable devices can be accompanied with ubiquitous environmental sensing and versatile actuation. The state-of-the-art in smart health applications is black-box, end-to-end implementations which are neither intended for use with heterogeneous data nor adaptable to a changing set of sensing and actuation. In this paper, we apply our modular approach for IoT applications—the context engine—to smart health problems, enabling the ability to grow with available data, use general-purpose machine learning, and reduce compute redundancy and complexity. For smart health, this improves response times for critical situations, more efficient identification of health-related conditions and subsequent actuation in a smart city environment. We demonstrate the potential with three sets of interconnected context-aware applications, extracting health-related people-centric context, such as user presence, user activity, air quality, and location from IoT sensors.

64 citations

Journal ArticleDOI
TL;DR: A smart multimedia-enabled middleware assistant is proposed that enables an elderly person to observe different energy-efficient processes, control smart home appliances through gestures, receive notifications regarding appliance statuses, and share multimedia messages with a community of interest.

62 citations