Development of a Multimodal Lifelogging Platform to Support Self-Reflection & Monitor Inflammation Associated With the Experience of Negative Emotions

该项目将开发一个移动生活记录平台，该平台将在日常情况下（如开车）提供心血管疾病的测量。驾驶是一种常见的日常活动，其中愤怒的经历和表达对健康和安全有影响。因此，这种活动可能与高水平的负面情绪有关，对长期健康产生累积影响。生活日志是一种持续记录和记录我们生活的行为，从我们做的事情，到我们去过的地方，甚至是我们的感受。可穿戴摄像头和身体传感器使我们能够从多个数据源中获取关于我们自己的丰富信息。随着传感器变得越来越普遍，在我们的环境中，可用数据的范围正在增加。这使得生活日志的信息变得更加丰富，并且它们在各种应用领域（如数字健康）中的使用正在增加。该项目将探索如何处理和实时整合多种生理和上下文数据流，以检测用户的状态。心率、脉搏波速度（PWV）、车速、位置和第一人称拍摄的环境照片等指标将被综合起来，以确定愤怒和炎症的情况。一系列的信号处理方法将应用于这些数据项（例如，心跳间隔将进行快速傅立叶变换），伪影将被识别并实时删除或合并。目前，记录行为的公开方面很简单，比如照片、位置和运动。然而，这个项目将把这些标记与心血管生理学的隐蔽变化结合起来，这些变化不会被用户直接感知。因此，该项目扩展了人们对自己身体的意识，他们的行为和对情况的反应是如何直接影响他们的身体的，以及这些行为的触发因素，例如路口的交通拥堵可能会提高我们的心率，而用户却没有意识到这种生理变化。每天重复这种压力行为，持续一段时间，可能会导致心血管疾病的发展。回顾动脉炎症发生的时刻，了解这种行为的背景，可以增强对日常事件如何影响健康的认识。这可以给人的生活方式带来积极的改变，比如避免出现问题的交叉点，以帮助预防引发心血管疾病的诱因。该系统将提供一种监测和影响行为的新方法，使我们能够加强生命记录领域，并使其与数字卫生的进步保持一致。这是通过使用与临床相关的生命记录技术和开发实时处理多模态信号的技术来实现的。据作者所知，在任何其他发展中，还没有解决将这种测量生理变化以预防疾病发生的生物医学标记物整合在一起的问题。总的来说，该项目试图通过一个先进的移动生活记录平台来减少一个重大的现实问题。该平台将在真实场景中进行评估，以评估其在人工环境之外的能力。这将使我们能够衡量其作为记录和量化行为的真实和实用解决方案的鲁棒性。通过这种方式，收集到的数据将用于识别动脉炎症的时刻和这些时间的背景，以促进自我反思和行为改变的实施。

The project will develop a mobile lifelogging platform that will deliver measures of cardiovascular disease in an everyday situation, such as driving a car. Driving represents a common daily activity, where experiences and expressions of anger have implications for health and safety. As such, this activity can be associated with high levels of negative emotions that have a cumulative impact on long-term health.Lifelogging is the continuous act of recording and documenting our lives, from the things we do, to the places we visit and even our feelings. Wearable cameras and body sensors allow us to capture rich information from multiple data sources about ourselves. As sensors become more prevalent, within our environment, the range of available data is increasing. This has enabled lifelogs to become richer with information and their use in various application domains, such as digital health, is increasing. The project will explore how multiple streams of physiological and contextual data can be processed and integrated in real-time to detect the user's state. Measures such as heart rate, pulse wave velocity (PWV), speed of the vehicle, location, and first-person photographs of the environment will be brought together to identify instances of anger and inflammation. A range of signal processing approaches will be applied to these data items (e.g. inter-beat interval from the heart rate will be subjected to Fast Fourier Transform) and artefacts will be identified and either removed or incorporated in real-time. Currently, it is straightforward to log overt aspects of behaviour, such as photographs, location and movement. However, this project will combine those markers with covert changes in cardiovascular physiology, which aren't perceived directly by the user. Hence, the project is extending a person's awareness of their bodies, how their behaviour and reactions to situations are directly impacting their bodies and the triggers for such behaviour, e.g. traffic congestion at a junction may raise our heart rate, without the user being consciously aware of this physiological change. Repeating this stressful behaviour daily, over a sustained period, could contribute to the development of cardiovascular disease. Reviewing moments when arterial inflammation occurs and understanding the context of this behaviour leads to an enhanced perception of how daily events affect health. This can lead to positive changes to the person's lifestyle, such as avoiding the junction in question to help prevent triggers leading to the onset of cardiovascular disease.The system will provide a new method to monitor and influence behaviour, which enables us to enhance and bring the field of lifelogging into alignment with advances in digital health. This is achieved using markers that are clinically relevant in the context of lifelogging technologies and developing techniques to process multi-modal signals in real-time. To the best of the author's knowledge, the integration of such biomedical markers that measures physiological changes in context to prevent the onset of disease has not been addressed in any other developments. Overall, the project attempts to reduce a significant real-world problem with an advanced mobile lifelogging platform. The platform will be evaluated in a real-world scenario to assess its capabilities outside of an artificial environment. This will enable us to gauge its robustness as a real and practical solution to log and quantify behaviour. In this way, the data collected will be used to identify moments of arterial inflammation and the context of those times to promote self-reflection and the implementation of behavioural changes.