Mobile Computing in Health Care Discussion

Mobile Computing in Health Care Discussion ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Mobile Computing in Health Care Discussion summarized 2 research paper in one presentation and should include some necessary pointe: Mobile Computing in Health Care Discussion – out lines – introduction – background of the study (mobile computing in healthcare) – paper (1) IoT .Research objectives .Problem statement .Contribution (results) – paper (2) Intelligent .Research objectives .Problem statement .Contribution (results) – comparison table between 2 research papers – conclusion intelligent_healthcare_systems_assisted_by_data.pdf internet_of_things_for_healthcare_using_effects_of_mobile.pdf Hindawi Wireless Communications and Mobile Computing Volume 2018, Article ID 3928080, 16 pages https://doi.org/10.1155/2018/3928080 Review Article Intelligent Healthcare Systems Assisted by Data Analytics and Mobile Computing Xiao Ma,1 Zie Wang,1 Sheng Zhou,1 Haoyu Wen,1 and Yin Zhang 1 2 1,2 School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China State Key Laboratory for Novel Software Technology, Nanjing University, China Correspondence should be addressed to Yin Zhang; [email protected] Received 9 January 2018; Accepted 20 May 2018; Published 3 July 2018 Academic Editor: Javier Prieto Copyright © 2018 Xiao Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. It is entering an era of big data, which facilitated great improvement in various sectors. Particularly, assisted by wireless communications and mobile computing, mobile devices have emerged with a great potential to renovate the healthcare industry. Although the advanced techniques will make it possible to understand what is happening in our body more deeply, it is extremely difficult to handle and process the big health data anytime and anywhere. Therefore, data analytics and mobile computing are significant for the healthcare systems to meet many technical challenges and problems that need to be addressed to realize this potential. Mobile Computing in Health Care Discussion Furthermore, the advanced healthcare systems have to be upgraded with new capabilities such as machine learning, data analytics, and cognitive power for providing human with more intelligent and professional healthcare services. To explore recent advances and disseminate state-of-the-art techniques related to data analytics and mobile computing on designing, building, and deploying novel technologies, to enable intelligent healthcare services and applications, this paper presents the detailed design for developing intelligent healthcare systems assisted by data analytics and mobile computing. Moreover, some representative intelligent healthcare applications are discussed to show that data analytics and mobile computing are available to enhance the performance of the healthcare services. 1. Introduction In the past two decades, advanced information technologies, such as mobile communication systems [1], big data [2], Internet of Things (IoT) [3], and wearable computing [4], have been widely used in the sector of healthcare [5]. Particularly, various novel healthcare systems assisted by big data and mobile computing are developed for provide intelligent and professional services [6]. However, the explosion of healthcare data brings the following challenges for data management, storage, and processing: (i) Large Scale. With the improvement of electromedical and wearable devices, the data volume of healthcare systems has been extensively increasing [7]. (ii) High Throughout. Generally, major electromedical and wearable devices can continuously acquit health data, while these data need to be processed rapidly for prompt response to emergencies [8]. (iii) Various Forms. There are various healthcare data generated and stored in healthcare systems, such as medical record, hospitalization records, medical imaging, and surgery data. These multisourced data include text, image, audio, and video [9]. More importantly, the same category of healthcare data collected through different devices may follow the different data standard defined by providers. (iv) Deep Value. Mobile Computing in Health Care Discussion The value of mining single source healthcare data is very limited. Thus, more research attempted to develop data fusion based approach to discover more knowledge from various data to provide more valuable services, such as personalized health guidance and public health warnings [10]. Fortunately, with the assistance of advanced techniques, more intelligent healthcare services are supported by data analytics, while it becomes more convenient for users to access to these novel services [11]. For example, M. Pramanik 2 et al. propose a big data enabled smart healthcare system framework to offer theoretical representations of an intraand interorganizational business model in the healthcare context [12]. M. Rathore et al. developed a Hadoop-based intelligent healthcare system demonstrating IoT-based collaborative contextual big data sharing among all of the devices in a healthcare system [13]. S. Peddia et al. designed a cloudbased mobile e-health calorie system that can classify food objects in the plate and further compute the overall calorie of each food object with high accuracy [14]. Although the great innovation is happening in the healthcare field, there are several issues need to be addressed, especially the heterogeneous data fusion, mobile data transmission and analysis, etc. [15–17]. In [18], it discussed clear motivations and advantages of multisensor data fusion and particularly focuses on physical activity recognition, aiming at providing a systematic categorization and common comparison framework of the literature, by identifying distinctive properties and parameters affecting data fusion design choices at different levels (data, feature, and decision). Mobile Computing in Health Care Discussion In [19], it presented the electronic health record big data analytics for precision medicine, including data preprocessing, mining, and modeling. Nowadays, a huge number of researches focus on data analysis or data mining for healthcare data [10, 20] on technical details in deploying and implementing mobile computing [21, 22], but one of the greatest challenges is how to develop a comprehensive healthcare system for effectively manage multisource heterogeneous healthcare data with particular technical features. Thus, this paper presents a detailed design of intelligent healthcare systems assisted by data analytics and mobile computing, and it make the following contributions: (1) It proposes a unified data collection layer for integrating the healthcare data from public sources and personal devices. (2) It establishes a cloud-enabled and data-driven platform for multisource heterogeneous healthcare data storage and analysis. (3) It designs a healthcare application service layer to provide unified application programming interface (API) for developers and unified interface for users. 2. Related Technologies 2.1. Mobile Computing. Mobile computing is an emerging technology related to multiple disciplines and is involved in many areas; it is also a hot issue in computer technology research. Mobile computing concerns how to provide quality information services (information storage, query, calculation, etc.) to mobile users (including users on laptops, mobile phones, and pagers) distributed across different locations. Mobile computing is a new type of technology that enables computers and other information devices to transmit data without being connected to a fixed physically connected device [23]. With the increase in mobile device usage, mobile computing is booming and has begun to be applied to different fields. Mobile Computing in Health Care Discussion Related work has been done in the education field [24]. In the medical field, mobile computing not only plays an important role but also is a very meaningful application direction as a supporting technology for mobile healthcare. Wireless Communications and Mobile Computing Medical professionals are using mobile devices to change clinical practice. Currently, many medical software applications can help people perform nursing tasks, from information and time management to clinical decision making [25]. The use of mobile devices, healthcare professionals (HCPS), by healthcare workers has changed many aspects of clinical practice, and it has become commonplace in healthcare environments, leading to the rapid development of medical applications. Many applications can now assist healthcare professionals in many important tasks, including information and time management, health record maintenance and access, communication and counseling, information reference and gathering, patient management and monitoring, clinical decision making, medical education, and training. Current mobile devices and applications provide many benefits to HCPs, where the most significant benefit is that people can be better cared for since they support clinical decision making and help patients recover. In [25], the following benefits of mobile devices in healthcare are summarized: convenience, better clinical decision making, and improved accuracy. Mobile computing can not only improve the accuracy of identifying relevant information and the accuracy of metrics but also increase efficiency and productivity. An effective mobile computing platform must be able to effectively use semantic-rich and medically plausible inferences about physiology, physiology, and psychology by sensing sensor information and behavioral status and linking these inferences with environmental, social, and other factors [26]. Mobile Computing in Health Care Discussion The challenge facing mobile computing is the issue of energy consumption, especially in the medical field, which often requires long monitoring durations and lengthy testing of patient-related physiological indicators. In [27], Kao et al. studied the energy consumption and performance of mobile computing, therein developing a task assignment problem and providing a dynamic programming algorithm. Hermes is a solution to the optimal strategy problem for balancing the issues of improving latency and energy consumption of mobile devices. With the increasingly in-depth research being performed, mobile computing will have a better future in the medical field. 2.2. Big Data. Huge amounts of data are generated in the medical field, therein increasing rapidly every day, especially in mobile healthcare. Normal mobile devices and wearable devices usually need a long time to detect the related physiological indices of the human body, but mobile medicine makes obtaining a patient’s physiological data more convenient and accurate, and it has greatly facilitated the development of medical big data. Big data also provide additional data support for medical treatment, such as in medical imaging and processing, electronic health records, epidemiology, and other higher level analyses of healthcare data and can play an auxiliary role in medical diagnosis [28]. In [29], Viceconti et al. proposed that big data analysis can be successfully combined with VPH technologies to create new electronic medical solutions that are both effective and robust. Wireless Communications and Mobile Computing In mobile healthcare, mobile computing technologies can utilize big data technologies to perform relevant analysis and processing to obtain related data in a timely and convenient manner to provide better medical care. Lv et al. [30] introduced two mobile healthcare applications that can serve as health services based on big data.Mobile Computing in Health Care Discussion On the one hand, big data can play a role in electronic medical record collection terminals; on the other hand, big data provide doctors with solutions for developing rehabilitation tools. In [31], the effects and benefits of big data analytics for healthcare were illustrated, therein suggesting that big data analytics may change the way healthcare professionals gain insights from their clinical and other data repositories and make informed decisions using cutting-edge technology. However, big data technologies also face many challenges in the healthcare industry. Belle et al. [32] discussed three important up-and-coming and important areas of medical research: image-, signal-, and genomic-based analysis. In the medical field, the use of a large amount of medical data and related analytical techniques can provide a positive impact. 2.3. Cloud Computing. Cloud computing is the addition, use, and delivery of Internet-based services and often provides dynamically scalable and virtualized resources through the Internet [33]. The development of cloud computing is not limited to PCs; with the vigorous development of the mobile Internet and the emergence of various mobile terminal devices, mobile cloud computing services have emerged. Cloud computing and big data technology are inseparable and are often used in combination [34]. In [35], a networked physical system based on patient-centered healthcare applications and services was proposed and called Health CPS. It builds on cloud and big data analytics. The results of their research show that cloud and big data technologies can be used to improve the performance of medical systems and allow people to enjoy a variety of smart medical applications and services. By combining the advantages of these two technologies, mobile cloud computing can be better applied to mobile healthcare. If certain limitations, such as limited memory, CPU power, and battery life, can be overcome, mobile cloud computing could greatly improve the capabilities and effects of mobile and cloud computing. Loai A. et al. [36] proposed the design of networked healthcare systems using big data and mobile cloud computing technologies. Wan et al. [37] studied a cloud-enabled WBAN architecture and its applications in pervasive healthcare systems. Mobile Computing in Health Care Discussion Using energy-efficient routing, cloud resource allocation, semantic interaction, and data security mechanisms, the system transmits critical sign data to the cloud, and it provides tremendous opportunities for healthcare systems. Certain benefits brought by cloud computing, such as the scalability offered by “potential” users (i.e., pay-as-yougo users) and software and virtual hardware services being delivered online (for example, collaborative planning, virtual servers, and virtual storage devices), will ensure that organizations do not have to maintain and update their software and hardware facilities by themselves. The flexibility of this 3 emerging computing service can create many possibilities for organizations that currently do not exist [38]. 2.4. Wearable Computing. In recent years, various mobile devices have emerged, including healthcare and medical devices (such as sports wristbands, watches, and smartphones). These wearable devices have computing abilities to record or detect relevant data for the user. Wearable computing technology has greatly facilitated mobile healthcare, as many wearable devices with disease monitoring and body perception abilities have been proposed [39, 40]. The development of wearable computing involves more aspects, and Chen et al. [41] proposed a new architecture for wearable computing based on emotional interaction and cloud technology design mechanisms; they then explored its current problems to demonstrate potential research in new directions. With the development of wearable devices, the wearable computing power needs to be further improved to better assist in medical and healthcare. Relevant studies on, e.g., computing power, connection methods, and power consumption, have been conducted [42–44]. Mobile cloud computing, big data, and other technologies are also promoting the development of wearable computing toward providing better technical support to mobile healthcare. 2.5. Internet of Things. Both wearable devices and mobile terminal devices are inseparable from the Internet of Things. Mobile Computing in Health Care Discussion The Internet of Things technology is one of the key technologies for these devices for transmitting and retrieving data and information. Its smart sensing technology provides important support for mobile medical data transmission and acquisition [45]. Catarinucci et al. [46] proposed a smart hospital system (SHS) that relies on different but complementary technologies, particularly wireless sensor networks, RFID, smartphones, and interoperability. SHS can collect real-time environmental conditions and patient physiological parameters. Internet of Things technology, big data technology, and cloud computing are popular and widely used technologies and are often interdependent. Advanced terminal technologies (e.g., smart apparel) and advanced cloud computing technologies (e.g., big data analytics and cloud cognitive computing) are expected to provide people with more reliable and intelligent services. In [4], Chen et al. presented a wearable medical 2.0 system to enhance QoE and QoS for the next generation of healthcare systems. In this system, washable smart clothing, including sensors, electrodes, and leads, is the key component of cloud analytics that provide users with their physiological data and receive the health and emotional state of machinebased, intelligent users. In emergency services, IoT can collect, integrate, and interoperate IoT data to flexibly support emergency medical services. The results of [47] show that the resource-based IoT data access method is effective in distributed and heterogeneous data environments and can access data across cloud and mobile computing platforms in a timely manner. Internet of Things technology is also commonly used at home and is often connected with home healthcare [48]. The 4 Wireless Communications and Mobile Computing seamless convergence of IoT devices in various platforms (such as sensors and wearable smart medicine kits) improves the user experience and service efficiency of home healthcare services (such as telemedicine). 2.6. Cyber Physical Systems.Mobile Computing in Health Care Discussion As a unity of computing processes and physical processes, cyber physical systems (CPSs) represent the next generation of intelligent systems, therein integrating computing, communications, and control [49]. The information physical system interacts with physical processes through a human-computer interaction interface and enables the networked space to manipulate a physical entity in a remote, reliable, real-time, secure, and collaborative manner. The information physical system includes future ubiquitous environmental awareness, embedded computing, network communication, network control, and other types of system engineering; it also provides the functions of computing, communication, precise control, remote collaboration, and autonomy. In addition, such a system focuses on the close combination and coordination of computing resources and physical resources. It is mainly used in certain intelligent systems such as equipment interconnection, IoT sensors, smart homes, robotics, and intelligent navigation systems. As a prominent subcategory of networked physical systems, to generate a convenient and cost-effective platform and promote complex and ubiquitous mobile sensing applications between humans and the surrounding physical world, mobile devices, such as smartphones, are widely used in mobile networked physical systems [50]. In [51], Costanzo et al. presented a flexible and reliable monitoring system based on embedded systems and wearable devices that allows doctors and family members to monitor the patient’s distance using a cell phone. In emergency situations, proper communication with the emergency center is required to rescue patients promptly. Thus, the system can effectively monitor the health of elderly individuals. With the rapid development of IT systems, embedded software has replaced the monitoring and diagnosis of patients. However, these systems have limitations in a wide range of device interoperability and data aggregation aspects. To solve these problems, medical network physical systems (MCP systems) have gradually become a new paradigm for medical systems. The paradigm introduced by mobile networked physical systems (MCPs) and Time Interventions (TI) transcends traditional medical environments in terms of the monitoring, diagnosing, treating, and management of patient health. These paradigms can provide medical care to patients at any place and at any time. Among them, MCP provides the necessary technical support system that facilitates collective work in an autonomous manner. 3. Intelligent Healthcare System Arc … Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10