Evaluation of healthcare Internet of Things (IoT) systems to monitor and protect older people via IoT trust

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Forecasts suggest that there will be more than 27.1 billion Internet-connected devices (things) worldwide by the end of 2025. Application domains of the Internet of Things (IoT) span from domestic to industry, including but not limited to intelligent healthcare systems. These IoT systems are mainly composed of low-cost embedded systems with varying complexities and the capability to sense and act. In healthcare IoT systems, things are in the form of wearable devices (e.g., body sensors, smartwatches, etc.), personal devices (e.g., smartphones), environmental sensors (e.g., temperature, humidity, etc.), cameras, IoT assistants, and many others. Their primary role is to gather information to monitor and eventually support clinicians in diagnosing and treating health conditions. Things collect and manage data from the environment when both explicit (active) and implicit (passive) human-to-thing interactions occur.This research project focuses on care for older people, one of the main aspects of IoT healthcare. The project aims to create an innovative method to acquire and fuse data collected from things in an IoT healthcare setting and explore how older individuals perceive the use of IoT technology so that trustworthy interactions can be established. Trust can be defined as "the belief that another party will behave according to a set of well-established rules and will thus meet one's expectations". Evaluating and computing trust by determining older individuals' behaviours can allow the IoT system to recognise abnormal behaviours. This would help carers and/or clinicians to provide the required help and support whenever specific situations occur, e.g., an injury has occurred, a particular disease is getting worse, threatening behaviours from malevolent individuals, etc.Several types of data will be collected synchronously, such as personal data (e.g., smartphone), vital signs acquisition (e.g., smartwatch), tone of voice (e.g., microphone), movements and facial expressions (e.g., cameras), environmental changes (e.g., luminosity), etc. The main challenges are data fusion and information extraction to determine the behaviours of humans performing physical activities for which we will use Artificial Intelligence. The jerkiness of motion, hand grip strength, balance tests, etc., will allow validation using data collected from motion and sensors. Finally, the created algorithm will be tested in a cohort of older individuals to determine its applicability to IoT scenarios.