Transportation research. part c, emerging technologies

The era of big data and analytics is opening up new possibilities for disaster management (DM). Due to its ability to visualize, analyze and predict disasters, big data is changing the humanitarian operations and crisis management dramatically. Yet, the relevant literature is diverse and fragmented, which calls for its review in order to ascertain its development. A number of publications have dealt with the subject of big data and its applications for minimizing disasters. Based on a systematic literature review, this study examines big data in DM to present main contributions, gaps, challenges and future research agenda. The study presents the findings in terms of yearly distribution, main journals, and most cited papers. The findings also show a classification of publications, an analysis of the trends and the impact of published research in the DM context. Overall the study contributes to a better understanding of the importance of big data in disaster management.

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Big data and disaster management: a systematic review and agenda for future research

Patient Monitoring System Based on Internet of Things

Background: Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care.
Objective: This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application.
Methods: We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms “conversational agents,” “conversational AI,” “chatbots,” and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis.
Results: The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education.
Conclusions: The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence–driven, and smartphone app–delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness.

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Conversational Agents in Health Care: Scoping Review and Conceptual Analysis.

With the increasing demand for automated, remote, intelligent, and real-time healthcare services in smart cities, smart healthcare monitoring is necessary to provide improved and complete care to residents. In this monitoring, health-related media or signals collected from smart-devices/objects are transmitted and processed to cater to the need for quality care. However, it is challenging to create a framework or method to handle media-related healthcare data analytics or signals (e.g., voice/audio, video, or electroglottographic (EGG) signals) to meet the complex on-demand healthcare needs for successful smart city management. To this end, this paper proposes a cloud-oriented smart healthcare monitoring framework that interacts with surrounding smart devices, environments, and smart city stakeholders for affordable and accessible healthcare. As a smart city healthcare monitoring case study, a voice pathology detection (VPD) method is proposed. In the proposed method, two types of input, a voice signal and an EGG signal, are used. The input devices are connected to the Internet and the captured signals are transmitted to the cloud. The signals are then processed and classified as either normal or pathologic with a confidence score. These results are passed to registered doctors that make the final decision and take appropriate action. To process the signals, local features are extracted from the first-order derivative of the voice signal, and shape and cepstral features are extracted from the EGG signal. For classification, a Gaussian mixture model-based approach is used. Experimental results show that the proposed method can achieve VPD that is more than 93% accurate.

Smart healthcare monitoring: a voice pathology detection paradigm for smart cities

With the recent increase in the interest of individuals in health, lifecare, and disease, hospital medical services have been shifting from a treatment focus to prevention and health management. The medical industry is creating additional services for health- and life-promotion programs. This change represents a medical-service paradigm shift due to the prolonged life expectancy, aging, lifestyle changes, and income increases, and consequently, the concept of the smart health service has emerged as a major issue. Due to smart health, the existing health-promotion medical services that typically have been operated by large hospitals have been developing into remote medical-treatment services where personal health records are used in small hospitals; moreover, a further expansion has been occurring in the direction of u-Healthcare in which health conditions are continuously monitored in the everyday lives of the users. However, as the amount of data is increasing and the medical-data complexity is intensifying, the limitations of the previous approaches are increasingly problematic; furthermore, since even the same disease can show different symptoms depending on the personal health conditions, lifestyle, and genome information, universal healthcare is not effective for some patients, and it can even generate severe side effects. Thus, research on the AI-based healthcare that is in the form of mining-based smart health, which is a convergence technology of the 4IR, is actively being carried out. Particularly, the introduction of various smart medical equipment for which healthcare big data and a running machine have been combined and the expansion of the distribution of smartphone wearable devices have led to innovations such as personalized diagnostic and treatment services and chronic-disease management and prevention services. In addition, various already launched applications allow users to check their own health conditions and receive the corresponding feedback in real time. Based on these innovations, the preparation of a way to determine a user’s current health conditions, and to respond properly through contextual feedback in the case of unsound health conditions, is underway. However, since the previously made healthcare-related applications need to be linked to a wearable device, and they provide medical feedback to users based solely on specific biometric data, inaccurate information can be provided. In addition, the user interfaces of some healthcare applications are very complicated, causing user inconvenience regarding the attainment of desired information. Therefore, we propose a chatbot-based healthcare service with a knowledge base for cloud computing. The proposed method is a mobile health service in the form of a chatbot for the provision of fast treatment in response to accidents that may occur in everyday life, and also in response to changes of the conditions of patients with chronic diseases. A chatbot is an intelligent conversation platform that interacts with users via a chatting interface, and since its use can be facilitated by linkages with the major social network service messengers, general users can easily access and receive various health services. The proposed framework enables a smooth human–robot interaction that supports the efficient implementation of the chatbot healthcare service. The design of the framework comprises the following four levels: data level, information level, knowledge level, and service level.

Chatbot-based heathcare service with a knowledge base for cloud computing

In order to cope with disaster situations properly, it is very important to identify the disaster scale and provide the accurate information of the site to the appropriate authorities including disaster site and Central Disaster Management Center, on-site command post, etc. and share the information provided. In particular, sharing information on disaster situations should control the disaster quickly to prevent the disaster situation from lasting and expanding. However, in the event of a large-scale disaster, delay is caused in the existing commercial network and therefore, the disaster situation cannot be communicated quickly and accurately. In order to determine the situation exactly in the event of a disaster, safety and connectivity of the network and flow of data are very important. Even if the stability of the network and connection of nodes are resolved in the network of each agency business operator, it is necessary to share the platform between networks for IoT/M2M communication for the smooth flow of data. Recently, the disaster safety net of combining existing disaster standard technology with Ubiquitous Technology and Smart IT such as Tetra of Europe, iDEN of the U.S., etc. has been built for disaster safety communications. In addition, systems useful for demand-centered, site-centered immediate disaster response by using Mobile, SNS, cloud computing, etc. are being built and designed to play an important role in the disaster information system especially through IoT, P2P cloud network, big data, etc. Therefore, in this paper, we proposed the P2P cloud network service for IoT based disaster situations information according to the paradigm of the changing times. The proposed service is to combine IoT/M2M network with P2P cloud service for rapid and smooth response in the event of a disaster and provide the results as social services such as SNS. To this end, the wide area wireless disaster information network system has been built in the local and each local network is connected to each other to provide disaster situations by using the server of the disaster area. At this time, each server was to be interconnected via P2P network and to be connected automatically by software-based network in P2P Cloud System. Also, the cognitive cycle was applied for selecting optimal wireless link and router of P2P Cloud-based Disaster Information Network and the danger situations of the disaster area were to be provided to the user by configuring disaster information component for providing services and building central disaster information platform managing it.

P2P cloud network services for IoT based disaster situations information

Recently, with changing paradigms in health, the focus of healthcare is shifting from treatment after contracting disease to prevention and early diagnosis of disease. Accordingly, the healthcare paradigm is changing from diagnosis and treatment to preventive management, emphasizing prevention of chronic diseases, such as obesity. In particular, obesity in children and adolescents has become a global issue. Lifestyle and health management using BT---IT convergence is needed to improve and manage the health of children and adolescents, and convenience and accessibility must be improved. For that, use of a machine-to-machine (M2M) u-health cluster that allows wireless network connection is increasing, along with wireless networks for measuring biometrics. Expanded to communications between people and objects as well as between objects, M2M refers to the next-generation convergence infra-architecture that offers intelligent services through various media. Because various wireless devices form a cluster when building a service platform using M2M, when the number of users with various M2M devices increases, data traffic increases and causes network overload, deteriorating system performance. To solve this problem, services are increasingly being built by combining a conventional network and Wi-Fi technology. However, in an M2M network, there is a limitation due to low transfer speed, because the network processes biometrics and data through different sensor nodes, and wireless communications based on the system is composed of different wireless sensor nodes. Thus, in this paper, we proposed a knowledge-based health service considering user convenience using a hybrid wireless fidelity (Wi-Fi) peer-to-peer (P2P) architecture. For knowledge-based health services in conventional M2M-based smart health services, hybrid Wi-Fi P2P and wireless devices must be linked. Because there are different ways to link hybrid Wi-Fi P2P devices, depending on the network environment, in this study, a dynamic configuration mechanism is applied to Wi-Fi P2P linkage of wireless devices in an M2M environment. The proposed service provides a high-quality health service (whenever patients use the knowledge-based health service) by building a network using a dispersed cross-layer optimization algorithm that optimizes variables of the transmission control protocol/internet protocol stack in order to improve the energy efficiency of the u-health sensor network and system reliability.

Knowledge-based health service considering user convenience using hybrid Wi-Fi P2P

As an interest in health and disease has increased, medical service has changed to prevention of disease and health care from treatment oriented service. Medical service industry is creating various services and added value for promotion of health. Aging, extension of life expectancy, increase in lifestyle and income growth have brought about a change in paradigm of medical service which led smart health to become an important issue. Smart health caused medical service for promotion of health to change into remote medical treatment that uses personal health record from medical service which has been provided by mainly large hospitals. Medical service for promotion of health has developed into u-Healthcare which monitors condition of health in everyday life. This enabled problems of time and space constraints that occur in medical service for promotion of health that requires a medical doctor to examine bio-signal related information of a patient while facing a patient to be solved. It is difficult for a remote medical treatment to care for chronic patients who require a care of lifestyle because it focuses on treating specific diseases. As a remote medical treatment does not provide innovative medical service and it only delivers general bio information on a patient to a medical doctor remotely, remote medical open platform is needed. Thus, in this paper, we proposed a PHR open platform based smart health services using the distributed object group framework. A PHR open platform based smart health system is distributed object group framework based smart health service for managing chronic diseases. When Medical WBAN sensor uses multi-channel in transmitting data, emergency data is very important in patient's life, smart health environment is built using distributed network considering importance according to data. As WBAN sensor is very different from other networks in terms of application, architecture and density of development, it is important for WBAN sensor to be combined with external network. High quality of service of integrated network as well as link connectivity should be maintained. Since automatic diagnosis function should be reinforced in order for remote diagnosis service to be provided, integration of each small unit system and model design are important. Therefore, smart health network environment that makes the most of performance of distributed network based on automation technique and distributed agent for optimum design of system is built.

PHR open platform based smart health service using distributed object group framework

Home networks currently dominated by human---object or human---human information production, exchange, processing, and paradigms are transitioning to machine to machine (M2M) due to the sudden introduction of embedded devices. Recently, due to the spread of IT equipment, more M2M-related devices are being used, and M2M-based projects are underway in various fields such as M2M-based u-city, u-port, u-work, u-traffic, etc. M2M has been applied in various fields, and u-healthcare is attracting attention in the M2M medical field. U-healthcare refers to technology in which ordinary patients can receive prescription services from experts by continuously monitoring changes in their health status during daily life at home based on wired and wireless communications infrastructures. In this paper, we propose an M2M-based smart health service for human UI/UX using motion recognition. Non-IP protocol, not TCP/IP protocol, has been used in sensor networks applied to M2M-based u-healthcare. However, sensors should be connected to the Internet in order to expand the use of services and facilitate management of the M2M-based sensor network. Therefore, we designed an M2M-based smart health service considering network mobility since data measured by the sensors should be transferred over the Internet. Unlike existing healthcare platforms, M2M-based smart health services have been developed for motion recognition as well as bio-information. Smart health services for motion recognition can sense four kinds of emotions, anger, sadness, neutrality, and joy, as well as stress using sensors. Further, they can measure the state of the individual by recognizing a user's respiratory and heart rates using an ECG sensor. In the existing medical environment, most medical information systems managing patient data use a centralized server structure. Using a fixed network, it is easy to collect and process limited data, but there are limits to processing a large amount of data collected from M2M devices in real-time. Generally, M2M communication used in u-healthcare consists of many networked devices and gateways. An M2M network may use standardized wireless technology based on the requirements of a particular device. Network mobility occurs when the connecting point changes according to the movement of any network, and the terminal can be connected without changing its address. If the terminal within the network communicates with any corresponding node, communication between the terminal and corresponding node should be continuously serviced without discontinuation. The method proposed in this paper can easily respond to dynamic changes in the wireless environment and conduct systematic management based on user's motion recognition using technology to support mobility among sensor nodes in M2M.

Roy C. Park

Papers

Chatbot-based heathcare service with a knowledge base for cloud computing

P2P cloud network services for IoT based disaster situations information

Knowledge-based health service considering user convenience using hybrid Wi-Fi P2P

PHR open platform based smart health service using distributed object group framework

M2M-based smart health service for human UI/UX using motion recognition