Measurement of lifestyle health behaviours from wearable cameras

通过可穿戴相机测量生活方式健康行为

• 批准号: 2636068
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2636068
• 关键词: Measurement; lifestyle; health; behaviours; wearable

The lifestyle choices that people make have repercussions for their health. For instance, reallocating time away from sedentary behaviour has been associated with lower risk of incident cardiovascular disease. Traditionally, identifying these choices has relied on self-reported data, which suffers from information bias, which has meant that the impacts of lifestyle behaviours are sometimes inaccurately estimated. However, advances in wearable sensors have introduced a more objective avenue for measuring lifestyle behaviours. Recent work has shown that wearable accelerometers are accurate at distinguishing between different physical activity behaviours. However, they still have not shown to be able to capture a range of lifestyle behaviours, for instance, recognising people's social interactions. Wearable cameras provide a promising avenue for identifying a wide range of these behaviours, as well as the context in which they occur. The gold standard measurement of lifestyle behaviour is direct observation; reviewing footage from wearable cameras is often as close as we can get to this gold standard. However, in order for these cameras to be included in the next generation of large-scale population health studies, we need to develop methodology for annotating wearable camera data at scale. Although we can take inspiration from the field of video-based human activity recognition, models developed in this field are typically trained on video clips that are very different to the series of images captured by camera loggers, which capture photos only every 20-30 seconds and are worn for a full day of different activities. This project will involve developing novel computer vision methods that can recognise a range of lifestyle activities from the relatively infrequent images captured by camera loggers.During a rotation project with the OxWearables group, I have shown that it is possible to automatically distinguish between coarse descriptions of physical activity from wearable cameras using techniques from computer vision. A DPhil in this direction would aim to rigorously assess the feasibility of incorporating wearable cameras into large-scale population health studies, and would include the following specific aims:1. Develop unsupervised and semi-supervised techniques for activity recognition based on camera logger data, with the goal of developing a tool that improves the annotation of new camera logger data-sets 2. Review the ethical frameworks for working with wearable camera data and revise them to reflect the issues raised by automated classification of lifestyle behaviours, 3. Develop multi-modal approaches to activity recognition that combine wearable cameras and accelerometers, and determine which types of activities are better recognised using different modalities, 4. Assess generalisation of wearable camera based classifiers across different population groups, environments and wearable devices.In the OxWearables group, there is unique access to wearable camera and accelerometer data-sets for physical activity recognition, and there are several new studies in progress which promise to bring in further data, such as the RADAR-AD project comprising 100 EU citizens, a study of 300 adolescents based in Australia, and a study of 150 older adults based in the UK and China. If successful, the methods proposed here promise to objectively measure lifestyle behaviours in future large-scale population health studies, and thus contribute to our understanding of how these behaviours are associated with major disease outcomes.This project falls within the ESPRC healthcare technologies research area. There are no additional companies or collaborators involved in this project.

人们选择的生活方式对他们的健康有影响。例如，重新分配久坐行为的时间与降低心血管疾病的风险有关。传统上，确定这些选择依赖于自我报告的数据，这受到信息偏差的影响，这意味着生活方式行为的影响有时会被不准确地估计。然而，可穿戴传感器的进步为测量生活方式行为引入了更客观的途径。最近的研究表明，可穿戴加速度计在区分不同的身体活动行为方面是准确的。然而，它们仍然没有显示出能够捕捉一系列生活方式行为，例如识别人们的社交互动。可穿戴摄像头为识别各种各样的这些行为以及它们发生的背景提供了一个有希望的途径。衡量生活方式行为的黄金标准是直接观察;查看可穿戴相机的镜头通常是我们能够达到的最接近这一黄金标准的方式。然而，为了将这些相机纳入下一代大规模人群健康研究，我们需要开发大规模注释可穿戴相机数据的方法。虽然我们可以从基于视频的人类活动识别领域获得灵感，但在该领域开发的模型通常是在视频剪辑上进行训练的，这些视频剪辑与摄像头记录仪捕获的一系列图像非常不同，摄像头记录仪每20-30秒才捕获一次照片，并在一整天的不同活动中佩戴。该项目将涉及开发新的计算机视觉方法，可以从相机记录器捕获的相对不频繁的图像中识别一系列生活方式活动。在与OxWearables小组的旋转项目中，我已经证明，使用计算机视觉技术可以自动区分可穿戴相机对身体活动的粗略描述。在这个方向上的哲学博士将旨在严格评估将可穿戴相机纳入大规模人群健康研究的可行性，并将包括以下具体目标：1。开发基于摄像机记录器数据的无监督和半监督活动识别技术，目标是开发一种工具，改进新摄像机记录器数据集的注释2。审查使用可穿戴相机数据的道德框架，并对其进行修订，以反映生活方式行为自动分类所提出的问题。开发多模式方法来识别活动，结合联合收割机可穿戴摄像头和加速度计，并确定使用不同的模式更好地识别哪些类型的活动。评估基于可穿戴摄像头的分类器在不同人群、环境和可穿戴设备中的推广情况。在OxWearables组中，可穿戴摄像头和加速度计数据集具有独特的访问权限，可用于身体活动识别，并且有几项正在进行的新研究有望带来更多数据，例如由100名欧盟公民组成的RADAR-AD项目，一项针对澳大利亚300名青少年的研究，以及一项针对英国和中国150名老年人的研究。如果成功的话，这里提出的方法有望在未来的大规模人群健康研究中客观地测量生活方式行为，从而有助于我们了解这些行为与主要疾病结局的关系。没有其他公司或合作者参与该项目。