Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

PhysioDroid: Combining Wearable Health Sensors and Mobile Devices for a Ubiquitous, Continuous, and Personal Monitoring

Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users’ conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices.This work was partially supported by the Spanish CICYT Project SAF2010-20558, Junta de Andalucia Project P09-TIC-175476, and the FPU Spanish Grant AP2009-2244. This work was also supported in part by the INTERREG IV European Project WHM-Wireless Health Monitoring (I-1-02=091) and the European Commission Seventh Framework Programme FP7 Project OPENi-Open-Source, Web-Based, Framework for Integrating Applications with Social Media Services, and Personal Cloudlets under Grant no. 317883

Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security

Extending remote patient monitoring with mobile real time clinical decision support

Large scale implementation of telemedicine services such as telemonitoring and teletreatment will generate huge amounts of clinical data. Even small amounts of data from continuous patient monitoring cannot be scrutinised in real time and round the clock by health professionals. In future huge volumes of such data will have to be routinely screened by intelligent software systems. We investigate how to make m-health systems for ambulatory care more intelligent by applying a Decision Support approach in the analysis and interpretation of biosignal data and to support adherence to evidence-based best practice such as is expressed in treatment protocols and clinical practice guidelines. The resulting Clinical Decision Support Systems must be able to accept and interpret real time streaming biosignals and context data as well as the patient’s (relatively less dynamic) clinical and administrative data. In this position paper we describe the telemonitoring/teletreatment system developed at the University of Twente, based on Body Area Network (BAN) technology, and present our vision of how BAN-based telemedicine services can be enhanced by incorporating mobile real time Clinical Decision Support. We believe that the main innovative aspects of the vision relate to the implementation of decision support on a mobile platform; incorporation of real time input and analysis of streaming\ud biosignals into the inferencing process; implementation of decision support in a distributed system; and the consequent challenges such as maintenance of consistency of knowledge, state and beliefs across a distributed environment

Platform independent web-based telecardiology for connected heart care

Most of the commercial telecardiology systems are platform-dependent and operating system (OS)-dependent. This causes inconvenience to medical officer for retrieving data from database and hence reduce the work efficiency. In this paper, a platformindependent and OS-independent web-based telecardiology system, named VirtualDave System, is proposed based on client-server model and developed in Hypertext Markup Language 5 (HTML5), Active Server Pages (ASP) scripting and C# languages. This system allows users to log on and access the patient medical data from any technology devices that equipped with web browser and internet access. Besides, it also allows targeted users to communicate and get remote medical consultation without long distance traveling and long-time queuing. Verification result shows that this proposed system could be executed in any platform regardless the OS. This web-based telecardiology could significantly help to improve the health care services especially in rural area