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

鉴于对保健和社会护理资源的压力，人们越来越有动力探索长期条件下自我管理的方法。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患病的人的生活质量。关于智能数据融合的新兴研究将一系列环境和可穿戴数据传感器结合起来，用于建模和分析随时间收集的生理和行为数据。这可用于为患者本人或其医疗保健专业人员提供早期预警或指导。研究的挑战在于开发针对个人的机器学习模型，这些模型在面对嘈杂的现实世界传感器数据时是可验证的和稳健的，这些数据会随着时间的推移而变化，因为人的情况会发生变化。如何最好地选择和整合多种类型的传感器数据，以保持不同信息流的完整性，同时还提供对人的活动的有意义的表示，在知识方面也存在空白。本研究将解决所提到的挑战，并探索交互系统的设计，该系统可以使用主动学习方法将用户输入用于语义标记和建模。让用户参与进来可以提高参与度，同时在传感器选择和融合技术方面提供改进的推理和信心。本研究将探索多模态用户输入方法，以引出和整合用户输入的语义标签，使用有监督、无监督和自学习技术的组合来解决噪声数据的挑战，并随着时间的推移可靠地跟踪长期条件的变化。这项研究将由跨学科监督团队成员正在进行的临床前工作提供信息，并与之相关，这些工作将探索怀孕、衰老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）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.