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

鉴于卫生和社会保健资源的压力，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患病的人们的生活质量。智能数据融合的研究正在兴起，联合收割机结合了一系列环境和可穿戴数据传感器，用于建模和分析随着时间的推移收集的生理和行为数据。这可以用于为患者本身或其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，这些模型在面对随着时间的推移而变化的嘈杂的真实世界传感器数据时是可验证的和鲁棒的。在如何最好地选择和整合多种类型的传感器数据方面也存在知识差距，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。这项研究将解决所指出的挑战，并探讨互动系统的设计，可以将用户输入的语义标签和建模，使用主动学习的方法。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中提供改进的推理和信心。这项研究将探索多模态用户输入方法，用于引发和整合语义标签的用户输入，使用监督，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，探索对怀孕，衰老过程和年龄相关疾病（如中风，糖尿病和心血管功能障碍）的反应的行为和生理变化。

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.