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

智能环境中的智能传感和数据融合，用于人类活动识别，支持长期状况的自我管理

• 批准号: 2888131
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2888131
• 关键词: Intelligent; sensing; data; fusion; smart

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.

鉴于对健康和社会护理资源的压力，越来越多的动力来探索长期条件的自我管理方法。通过一系列环境集成传感器和服务机器人技术实现的智能环境可以长期条件改善其生活质量。有关于智能数据融合的新兴研究，以结合一系列环境和可穿戴的数据传感器，以建模和分析随时间收集的生理和行为数据。这可用于为患者本身或其医疗保健专业人员提供预警或指导。研究挑战在于开发特定于人的机器学习模型，面对嘈杂的现实世界传感器数据，这些模型随着人的状况而变化，它们会随着时间的流逝而变化。关于如何最好地选择和集成多种类型的传感器数据的知识也存在差距，以保留不同信息流的完整性，同时还提供了对人活动的有意义的表示。这项研究将解决所提到的挑战，并探索可以使用主动学习方法来结合用户输入的交互式系统的设计。使用户保持循环可以改善参与度，同时提高了对传感器选择和融合技术的推理和信心。这项研究将探索多模式的用户输入方法，以使用监督，无监督和自学学习技术的组合来引起和整合用户输入以进行语义标签，以应对嘈杂数据的挑战并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的成员进行的临床前工作和与正在进行的临床前工作有关，并探讨了对怀孕，衰老过程以及与年龄相关的疾病（如中风，糖尿病和心血管功能障碍）的行为和生理变化。