Intelligent sensing and data fusion in a smart environment for human activity recognition to support self-management of long-term conditions

考虑到卫生和社会护理资源的压力，人们越来越有动力探索长期自我管理的方法。通过一系列环境集成传感器和服务机器人实现的智能环境，可以改善长期疾病患者的生活质量。出现了智能数据融合的研究，将一系列环境和可穿戴的数据传感器结合在一起，对随着时间推移收集的生理和行为数据进行建模和分析。这可以用来为患者本身或他们的医疗保健专业人员提供早期警告或指导。研究的挑战在于开发特定于个人的机器学习模型，该模型在面对噪声的真实世界传感器数据时是可验证的和健壮的，这些数据会随着人的条件的变化而随着时间的推移而变化。在如何最好地选择和集成多种类型的传感器数据的知识方面也存在差距，这种方式既能保持不同信息流的完整性，又能提供对人的活动的有意义的表示。这项研究将解决所指出的挑战，并探索使用主动学习方法设计能够纳入用户输入以进行语义标记和建模的交互系统。让用户保持在循环中可以提高参与度，同时在传感器选择和融合技术方面提供更好的推理和信心。这项研究将探索多模式用户输入方法，以诱导和整合用户输入以进行语义标记，使用监督、非监督和自我学习技术的组合来应对噪声数据的挑战，并可靠地跟踪长期条件随时间的变化。这项研究将由跨学科监督小组成员正在进行的临床前工作提供信息，并与之相关，探索因应怀孕、老龄化过程和与年龄相关的疾病(如中风、糖尿病和心血管功能障碍)而发生的行为和生理变化。

Given the pressure on health and social care resources, there is a growing incentive to explore methods for self-management for long-term conditions. Smart environments, realised through a range of ambient integrated sensors and service robotics, could people with long-term conditions improve their quality of life. There is emerging research on intelligent data fusion to combine a range of ambient and wearable data sensors for modelling and analysing physiological and behavioural data collected over time. This can be used to provide early warning or guidance for the patient themselves, or their healthcare professionals. The research challenges lie in developing person-specific machine learning models, which are verifiable and robust in the face of noisy real-world sensor data that will change over time, as the person's condition changes. There is also a gap in knowledge on how best to select and integrate multiple types of sensor data, in a way that preserves the integrity of the different streams of information, while also providing a meaningful representation of the person's activity. This research will address the challenges noted, and also explore the design of interactive systems that can incorporate user input for semantic labelling and modelling, using an active learning approach. Keeping the user in the loop can improve engagement, while offering improved reasoning and confidence in sensor selection and fusion techniques. This research will explore multi-modal user-input approaches for eliciting and integrating user input for semantic labelling, using a combination of supervised, un-supervised and self-learning techniques to address the challenges of noisy data and reliably tracking changes in long-term conditions over time. This research will be informed by, and related to, ongoing preclinical work being conducted by members of the interdisciplinary supervisory team, exploring behavioural and physiological changes in response to pregnancy, the ageing process and age-related diseases such as stroke, diabetes and cardiovascular dysfunction.