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

预测表明，到2025年底，全球将有超过271亿台互联网连接设备（事物）。物联网（IoT）的应用领域从家庭到行业，包括但不限于智能医疗系统。这些物联网系统主要由低成本的嵌入式系统组成，具有不同的复杂性和感知和行动能力。在医疗物联网系统中，事物是可穿戴设备的形式（例如，身体传感器、智能手表等），个人设备（例如，智能电话），环境传感器（例如，温度、湿度等），摄像头、物联网助手等等。他们的主要作用是收集信息，以监测并最终支持临床医生诊断和治疗健康状况。当发生显式（主动）和隐式（被动）人与物的交互时，物从环境中收集和管理数据。本研究项目专注于老年人护理，这是物联网医疗保健的主要方面之一。该项目旨在创建一种创新方法，以获取和融合从物联网医疗环境中收集的数据，并探索老年人如何看待物联网技术的使用，以便建立可信赖的交互。信任可以被定义为“相信另一方将按照一套既定的规则行事，从而达到自己的期望”。通过确定老年人的行为来评估和计算信任度，可以让物联网系统识别异常行为。这将有助于护理人员和/或临床医生在发生特定情况时提供所需的帮助和支持，例如，已经发生伤害、特定疾病正在恶化、恶意个体的威胁行为等。将同步收集几种类型的数据，例如个人数据（例如，智能电话），生命体征采集（例如，智能手表），语调（例如，麦克风），运动和面部表情（例如，相机），环境变化（例如，主要的挑战是数据融合和信息提取，以确定我们将使用人工智能进行物理活动的人类行为。运动的急动性，手的握力，平衡测试等，将允许使用从运动和传感器收集的数据进行验证。最后，将在一组老年人中测试所创建的算法，以确定其对物联网场景的适用性。

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.