Measurement of lifestyle health behaviours from wearable cameras

人们选择的生活方式会对他们的健康产生影响。例如，重新分配远离久坐行为的时间与发生心血管疾病的风险较低有关。传统上，识别这些选择依赖于自我报告的数据，这存在信息偏差，这意味着有时不准确地估计生活方式行为的影响。然而，可穿戴传感器的进步为测量生活方式行为提供了一条更客观的途径。最近的研究表明，可穿戴式加速度计能够准确区分不同的身体活动行为。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。可穿戴式摄像头为识别一系列此类行为以及它们发生的背景提供了一条很有希望的途径。衡量生活方式行为的黄金标准是直接观察；回顾可穿戴相机拍摄的镜头通常是我们尽可能接近这一黄金标准的标准。然而，为了将这些相机纳入下一代大规模人口健康研究，我们需要开发出按比例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，但该领域开发的模型通常是在视频剪辑上进行培训的，这些视频剪辑与相机记录器捕获的一系列图像非常不同，相机记录器每隔20-30秒才捕获一张照片，并在不同的活动中佩戴一整天。这个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OxWearables小组的一个旋转项目中，我已经展示了使用计算机视觉技术自动区分可穿戴相机对身体活动的粗略描述是可能的。在这方面，DPhil的目标是严格评估将可穿戴相机纳入大规模人口健康研究的可行性，并将包括以下具体目标：1.开发基于相机记录器数据的非监督和半监督活动识别技术，目标是开发一种工具，改进新相机记录器数据集的注释2.审查使用可穿戴相机数据的伦理框架，并对其进行修订，以反映生活方式行为自动分类带来的问题，3.开发结合可穿戴式相机和加速计的多模式活动识别方法，并确定使用不同模式更好地识别哪些类型的活动，4.评估基于可穿戴相机的分类器在不同人群、环境和可穿戴设备中的普适性。在OxWearables组，可以独特地访问可穿戴相机和加速度计数据集来识别身体活动，还有几项新研究正在进行中，有望带来更多数据，例如由100名欧盟公民参与的雷达-AD项目，对300名澳大利亚青少年的研究，以及对英国和中国的150名老年人的研究。如果成功，这里提出的方法有望在未来的大规模人口健康研究中客观地衡量生活方式行为，从而有助于我们理解这些行为与重大疾病结果的关系。该项目属于ESPRC医疗保健技术研究领域。没有其他公司或合作者参与此项目。

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.