Master-Slave IoT for Active Healthy Life Style

The main objective of this project is to design and develop a collaborative framework which facilitates real-time tracking of a target person even when GPS signal is not available, while collecting motion data to infer his or her lifestyle and health status. The framework orchestrates a wide range of technologies such as localization technologies, machine learning and AI, sensor data analytics and cloud computing. The overall framework design also takes into consideration the culture, lifestyles, behaviours and infrastructures of ASEAN countries. On location tracking, a mobile and cloud-based Indoor Location Platform (ILP) which incorporates multimodal localization means and assisted by other sensor fusion techniques is developed. In this platform, GPS and non-GPS positioning systems such as Wi-Fi/BLE fingerprinting, IR-UWB positioning, sensor-based and a hybrid of these localization techniques are adopted to provide continuous tracking of the subject of interest in both indoor and outdoor environments. Extensive trials have been carried out in not only laboratory testbeds, but also in factories and other commercial premises. On health or lifestyle monitoring, harvesting of motion data and context reasoning, using the IntelliHealth Solutions were carried out to assess, monitor and to provide feedback on a person’s lifestyle. An intelligent knowledge base is formed and this enables the development of various transient wearable health OS solutions. In this project, wearable motion interfacing and reasoning devices for general public are developed to support trials and data collections involving people from public. 

本项目的核心目标是设计并开发一套协同框架，即便在GPS信号缺失的场景下，仍能实现对目标人员的实时追踪；同时通过采集运动数据，推断其生活方式与健康状态。该框架整合了定位技术、机器学习与人工智能、传感器数据分析及云计算等多元技术体系。框架的整体设计亦充分考量了东盟（ASEAN）国家的文化背景、生活方式、行为模式及基础设施条件。在定位追踪层面，本项目开发了一套基于移动终端与云端的室内定位平台（Indoor Location Platform，ILP），该平台融合了多模态定位手段，并辅以其他传感器融合技术。该平台采用了GPS及非GPS定位系统（如Wi-Fi/BLE指纹定位、红外超宽带（IR-UWB）定位、基于传感器的定位技术及上述技术的混合方案），以实现对目标对象在室内外环境下的连续追踪。本项目已开展了广泛的测试验证，不仅覆盖实验室测试环境，还包括工厂及其他商业场所。在健康与生活方式监测方面，项目通过IntelliHealth Solutions采集运动数据并进行情境推理，以实现对个人生活方式的评估、监测及反馈。项目构建了智能知识库，为各类瞬时可穿戴健康操作系统（OS）解决方案的开发提供支撑。此外，项目还开发了面向普通公众的可穿戴运动交互与推理设备，以支持涉及公众参与的测试与数据采集工作。