Guest editorial: Selected papers from the International Conference on Smart Living and Public Health

Abstract

The International Conference on Smart Living and Public Health (ICOST, www.icost-society.org) provides a premier venue for the presentation and discussion of research in the design, development, deployment, and evaluation of artificial intelligence (AI) for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems. ICOST focuses on analysing the impact of ICTs on public health and the wellbeing of citizens all over the world. For more than a decade and a half, the ICOST conference has succeeded in bringing together a community from different continents and has raised awareness about frail and dependent people's quality of life in our societies.

This special issue presents extended versions of selected papers from the 18th edition of the ICOST conference. The issue contains four papers presented at the conference on Biomedical and Health Informatics, Internet of Things and AI solutions for E-health and Wellbeing Technologies topics.

Khriji et al. in their paper entitled “Automatic heart disease class detection using convolutional neural network architecture-based various optimizers-networks” propose a deep learning architecture for automatic classification of the patient's electrocardiogram (ECG) signal into a specific class according to American National Standards Institute – Association for the Advancement of Medical Instrumentation standards. This enables automatic arrhythmia heart disease detection at an early stage, which is of high interest because it helps to reduce the mortality rate for cardiac disease patients. The proposed approach is validated through different ECG databases. Experimental results show high achievement compared with state-of-the-art models. Implementation on graphical processing units confirms the low computational complexity of the system and its possible use in detecting disease events in real time, which makes it a good candidate for portable health care devices.

Ben Ida et al. in their paper “Adaptative vital signs monitoring system based on the early warning scoring approach in smart hospital context” present an edge-based early warning score (EWS) that respects a risk evaluation approach named NEWS2. The proposed approach allows the prediction of patients' risk level based on collected vital signs data. The paper proposes an adaptative configuration of the vital signs monitoring process depending on variations in the patient’s health status and the medical staff’s decisions. The authors also propose an intelligent notification mechanism that reduces the delay of medical staff intervention in case of risk detection.

Sellami et al. in their paper entitled “A Plug&Play Approach for Modelling and Simulating Applications in the Era of Internet of Social Things” presents an approach to model and simulate Plug&Play social things. Social things engage in collaborative scenarios that expose specific relations connecting these things together. The paper puts forward four stages for social things Plug&Play referred to as connecting to demystify social relations among things, influencing to examine the impact of social relations on things, playing to make things perform while considering influence, and incentivizing to reward things based on their performance. The main goal of the paper is to define when and where social relations are active. These properties would enable resource starvation to be avoided in an environment where millions of things would operate and hence compete for resources. The proposed use would regulate the life cycles of social relations in terms of longevity (short-term versus long term), nature (static versus dynamic), and occurrence (one versus multiple).

Forchuk et al. in their paper “Improving Access and Mental Health for Youth Using Smart Technologies” present a study to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youths aged 14–25 years with symptoms of anxiety or depression. The paper describes the set of tools and methods used and the main outcomes obtained. The study included 115 youths who were accessing outpatient mental health services at one of three hospitals and two community agencies. The adopted technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also enables secure virtual treatment visits in which youths can participate through mobile devices. This longitudinal study uses participatory action research with mixed methods.