Using quantified-self for future remote health monitoring

Remote monitoring is an essential part of future mHealth systems for the delivery of personal and pervasive healthcare, especially to allow the collection of personal bio-data outside clinical environments. mHealth involves the use of mobile technologies including sensors and smart phones with Internet connectivity to collect personal bio-data. Yet, by its very nature, it presents considerable challenges: (1) it will be a highly distributed task, (2) requiring collection of bio-data from a myriad of sources, (3) to be gathered at the clinical site, (4) and via secure communication channels. To address these challenges, we propose the use of an online social network (OSN) based on the quantified-self, i.e. the use of wearable sensors to monitor, collect and distribute personal bio-data, as a key component of a near-future remote health monitoring system. Additionally, the use of a social media context allows existing social interactions within the healthcare regime to be modeled within a carer network, working in harmony with, and providing support for, existing relationships and interactions between patients and healthcare professionals. We focus on the use of an online social media platform (OSMP) to enable two primitive functions of quantified-self which we consider essential for mHealth, and on which larger personal healthcare services could be built: remote health monitoring of personal bio-data, and an alert system for asynchronous notifications. We analyse the general requirements in a carer network for these two primitive functions, in terms of four different viewpoints within the carer network: the patient, the doctor in charge, a professional carer, and a family member (or friend) of the patient. We propose that a wellbeing remote monitoring scenario can act as a suitable proxy for mHealth monitoring by the use of an OSN. To allow rapid design, experimentation and evaluation of mHealth systems, we describe our experience of creating an mHealth system based on a wellbeing scenario, exploiting the quantified-self approach of measurement and monitoring. The use of wellbeing data in this manner is particularly valuable to researchers and systems developers, as key development work can be completed within a realistic scenario, but without risk to sensitive patient medical data. We discuss the suitability of using wellbeing monitoring as a proxy for mHealth monitoring with OSMPs in terms of functionality, performance and the key challenge in ensuring appropriate levels of security and privacy. We find that OSMPs based on quantified-self offer great potential for enabling personal and pervasive healthcare in an mHealth scenario

Mobihealth: mobile health services based on body area networks

In this chapter we describe the concept of MobiHealth and the approach developed during the MobiHealth project (MobiHealth, 2002). The concept was to bring together the technologies of Body Area Networks (BANs), wireless broadband communications and wearable medical devices to provide mobile healthcare services for patients and health professionals. These technologies enable remote patient care services such as management of chronic conditions and detection of health emergencies. Because the patient is free to move anywhere whilst wearing the MobiHealth BAN, patient mobility is maximised. The vision is that patients can enjoy enhanced freedom and quality of life through avoidance or reduction of hospital stays. For the health services it means that pressure on overstretched hospital services can be alleviated

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

New intelligent network approach for monitoring physiological parameters : the case of Benin

Benin health system is facing many challenges as: (i) affordable high-quality health care to a growing population providing need, (ii) patients’ hospitalization time reduction, (iii) and presence time of the nursing staff optimization. Such challenges can be solved by remote monitoring of patients. To achieve this, five steps were followed. 1) Identification of the Wireless Body Area Network (WBAN) systems’ characteristics and the patient physiological parameters’ monitoring. 2) The national Integrated Patient Monitoring Network (RIMP) architecture modeling in a cloud of Technocenters. 3) Cross-analysis between the characteristics and the functional requirements identified. 4) Each Technocenter’s functionality simulation through: a) the design approach choice inspired by the life cycle of V systems; b) functional modeling through SysML Language; c) the communication technology and different architectures of sensor networks choice studying. 5) An estimate of the material resources of the national RIMP according to physiological parameters. A National Integrated Network for Patient Monitoring (RNIMP) remotely, ambulatory or not, was designed for Beninese health system. The implementation of the RNIMP will contribute to improve patients’ care in Benin. The proposed network is supported by a repository that can be used for its implementation, monitoring and evaluation. It is a table of 36 characteristic elements each of which must satisfy 5 requirements relating to: medical application, design factors, safety, performance indicators and materiovigilance

Asynchronous Remote Medical Consultation for Ghana

Computer-mediated communication systems can be used to bridge the gap between doctors in underserved regions with local shortages of medical expertise and medical specialists worldwide. To this end, we describe the design of a prototype remote consultation system intended to provide the social, institutional and infrastructural context for sustained, self-organizing growth of a globally-distributed Ghanaian medical community. The design is grounded in an iterative design process that included two rounds of extended design fieldwork throughout Ghana and draws on three key design principles (social networks as a framework on which to build incentives within a self-organizing network; optional and incremental integration with existing referral mechanisms; and a weakly-connected, distributed architecture that allows for a highly interactive, responsive system despite failures in connectivity). We discuss initial experiences from an ongoing trial deployment in southern Ghana.Comment: 10 page

Transnational comparison : A retrospective study on e-health in sparsely populated areas of the northern periphery

Peer reviewedPublisher PD

A (digital) finger on the pulse

Complex Event Processing (CEP) is a computer-based technique used to track, analyse and process data in real-time (as an event happens). It establishes correlations between streams of information and matches to defined behaviour

Mobile Health Care over 3G Networks: the MobiHealth Pilot System and Service

Health care is one of the most prominent areas for the application of wireless technologies. New services and applications are today under research and development targeting different areas of health care, from high risk and chronic patients’ remote monitoring to mobility tools for the medical personnel. In this direction the MobiHealth project developed and trailed a system and a service that is using UMTS for the continuous monitoring and transmission of vital signals, like Pulse Oximeter sensor , temperature, Marker, Respiratory band, motion/activity detector etc., to the hospital. The system, based on the concept of the Body Area Network, is highly customisable, allowing sensors to be seamlessly connected and transmit the monitored vital signal measurements. The system and service was trialed in 4 European countries and it is presently under market validation