About this Research Topic
The World Health Organization speculates that by the year 2050, more than 20% of the population in the developing countries will be over 60 years of age. The percentage of ageing population has been growing in the last years and is bringing new challenges of sustainability to the current healthcare systems. Besides the financial feasibility, other challenges include the long delays in servicing the patients with consequent late detection of serious health issues. The fact that the majority of the elderly prefer to age in their homes emphasizes the need to develop technological solutions that autonomously monitor and enhance their well-being.
The growing technological advancement is resulting in various cheap, sensory equipment that can benefit the healthcare of aging population. The uptake of IoT opens new opportunities in how technology can assist people in improving their health and well-being and in improving the cost-effectiveness and quality of health and social services. Such technological innovation contributes to the emerging field of gerontechnology, an interdisciplinary field that focuses on health and well-being in older age. Applications, such as administration of medication, voice command technologies, telemedicine and others based on artificial intelligence are very important tools that contribute to the peace of mind. In particular, machine learning and predictive analysis combined with IoT may play an important role in the early detection of suspicious signs that can lead to mobility, mental and cognitive issues. Other applications may aggregate the high frequency messy and intermittent data and incorporate it in the Electronic Health Record (EHR) using a Blockchain platform that can be shared with healthcare professionals. Issues such as scalability, information and cyber security, and systems interoperability are, however, realities that need to be dealt with urgently in order to achieve sustainable healthcare systems.
The theme of this Research Topic is autonomous health monitoring and assistance systems with IoT, and aims at covering two aspects. First, to discuss the latest advancement with respect to IoT-related technology and information systems mainly in terms of scalability, security and interoperability. Second, to propose artificial intelligent systems, based on machine and deep learning, for applications that assist in independent living. The goal is to bring together researchers and practitioners from both academia and industry across different computer science fields, like artificial intelligence, information systems, ubiquitous computing and distributed systems, and others.
Topics of interest include, but are not limited to:
· Early detection of health issues
· Intelligent systems that support the well-being of individuals based on machine learning, deep learning and artificial intelligence
· Immersive technologies for healthcare
· Edge, Cloud and Blockchain based solutions for managing data and its quality in secure and scalable ways
· IoT system architecture for autonomous heath monitoring systems
· Distributed IoT analytics
· Autonomous Information Systems for health monitoring and assistance – Innovative architectures, concepts, models, realizations and use cases
· Literature surveys
The growing technological advancement is resulting in various cheap, sensory equipment that can benefit the healthcare of aging population. The uptake of IoT opens new opportunities in how technology can assist people in improving their health and well-being and in improving the cost-effectiveness and quality of health and social services. Such technological innovation contributes to the emerging field of gerontechnology, an interdisciplinary field that focuses on health and well-being in older age. Applications, such as administration of medication, voice command technologies, telemedicine and others based on artificial intelligence are very important tools that contribute to the peace of mind. In particular, machine learning and predictive analysis combined with IoT may play an important role in the early detection of suspicious signs that can lead to mobility, mental and cognitive issues. Other applications may aggregate the high frequency messy and intermittent data and incorporate it in the Electronic Health Record (EHR) using a Blockchain platform that can be shared with healthcare professionals. Issues such as scalability, information and cyber security, and systems interoperability are, however, realities that need to be dealt with urgently in order to achieve sustainable healthcare systems.
The theme of this Research Topic is autonomous health monitoring and assistance systems with IoT, and aims at covering two aspects. First, to discuss the latest advancement with respect to IoT-related technology and information systems mainly in terms of scalability, security and interoperability. Second, to propose artificial intelligent systems, based on machine and deep learning, for applications that assist in independent living. The goal is to bring together researchers and practitioners from both academia and industry across different computer science fields, like artificial intelligence, information systems, ubiquitous computing and distributed systems, and others.
Topics of interest include, but are not limited to:
· Early detection of health issues
· Intelligent systems that support the well-being of individuals based on machine learning, deep learning and artificial intelligence
· Immersive technologies for healthcare
· Edge, Cloud and Blockchain based solutions for managing data and its quality in secure and scalable ways
· IoT system architecture for autonomous heath monitoring systems
· Distributed IoT analytics
· Autonomous Information Systems for health monitoring and assistance – Innovative architectures, concepts, models, realizations and use cases
· Literature surveys
Keywords: Anomaly Detection, Intelligent Systems, Artificial Intelligence, Edge Computing, Cloud Computing, Blockchain, Gerontechnology, Cyber Security, Data Visualization, Smart Homes, Telehealth Services, Digital Health, Autonomous Systems, Machine Learning, Deep Learning, Predictive Analysis
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