Abstract
SummaryAs now well established, the world population is aging rapidly and, according to World Health Organization (WHO), the amount of people aged 60 years and older is expected to total 2 billion in 2050. For this reason, an emerging important issue is the definition of a new generation of healthcare platforms capable of monitoring people's quality of life. In this article, we propose a new methodology that supports the entire requirement elicitation process starting from the initial phase of gathering the requirements, both clinical, technological, and end‐user, up to the choice of the most suitable solution. Our proposal provides a new new iterative model in the smart healthcare field research area. Furthermore we apply our proposal in a real scenario and we report the end‐to‐end implementation of the proposed methodology.
Highlights
Mobile, wearable, and IoT technologies are linking our everyday activities to the digital world
We propose the matching smart health requirements (MSHR) methodology to design a smart health solution consistently taking into consideration requirements from different areas
Starting from these main objectives, our proposal will be applied during the entire definition and modeling process of SMART BEAR project and we will show an end-to-end experiment starting from the requirements elicitation concluding with the modeling and device selection phase
Summary
Wearable, and IoT technologies are linking our everyday activities to the digital world. Their sensing, logging, and tracking capacity can be exploited to improve awareness of people’s health and wellness. The self-monitoring and sharing of health and wellness data are becoming increasingly popular creating an entirely new market of apps and services, often referred as mobile Health - mHealth. The older population represents an excellent target for developing new smart health platforms. According to the World Health Organization (WHO), the aging population is witnessing a new disease pattern comprised of multiple comorbidities of age-related chronic diseases that appear to significantly impact on the mental, cognitive, and physical wellbeing with a consequent accentuated vulnerability which predisposes every older subject to a high risk of geriatric syndromes, hospitalization, and disability. Some of the determining factors are a reduction in costs for health care providers, increased frequency of controls at the biomedical parameters of patients, high data portability and integration, positive perception of patients appreciating increased levels of care services, better control on patients’ history by clinicians
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