Computer Vision Systems to Analyse Face and Body Movements, to Automated the Assessment of Physical Health, Mobility, and Safety in Natural Settings and Over Time

我的研究领域是开发用于智能健康监测和康复技术的计算机视觉系统和算法。在接下来的五年里，我将把工作重点放在这一领域的两个重要主题上：1）使用和推进机器学习的最新发展，以改善时间和纵向信息如何纳入计算机视觉健康评估; 2）计算机视觉系统的公平性和偏见，特别是在老年和残疾方面。 1)纵向健康数据通常包含大量缺失信息，并且几乎总是不规则地采样。对缺失数据和不规则采样进行建模的原则性方法包括高斯过程和最近的神经常微分方程。我的研究将增强和推进这些方法，以利用额外的信息，包括缺失信息的模式和样本的时间分布。我还将研究如何将Transformer Networks应用于处理医疗图像/视频数据的长期趋势，以及与其他健康参数或相关信息的关系。 2)我的工作已经确定了与最先进的计算机视觉面部分析模型在有身体或认知障碍的老年人脸上的表现相关的公平和偏见问题，例如中风或痴呆。这是在医疗保健解决方案中采用计算机系统的重要限制因素。用于在标准基准测试中获得最佳性能结果的深度学习模型通常在数十万到数百万个训练示例上进行训练。从各种临床人群中收集这种规模的代表性示例以包括在训练数据中是不切实际的。患者数据和可识别的健康记录（例如面部图像）高度敏感，访问通常受到限制。一个更现实的解决方案是收集有针对性的代表性训练集，并通过迁移学习将在标准基准数据集上训练的最先进模型适应目标人群。到目前为止，在我的工作中，我已经研究了常用的转移技术，并为此开发和评估了新的转移技术。使用这些方法，面部分析模型对目标人群的性能得到改善，但临床人群和健康人群之间的性能差距差距仍然存在。在接下来的几年里，我计划扩大我在这一领域的工作，特别是探索使用反事实推理和不确定性量化来提高迁移学习技术的性能，特别是与面部和身体运动分析中使用的计算机视觉系统相关。 该研究计划将培训七名HQP，并将产生创新的解决方案，以减少偏差，并更好地将长期时间信息纳入计算机视觉系统。

My area of research is the development of computer vision systems and algorithms for intelligent health monitoring and rehabilitation technologies. Over the next five years I will focus my work on two important topics in this area: 1) using and advancing recent developments in machine learning to improve how temporal and longitudinal information is incorporated in computer vision health assessment, and 2) fairness and bias in computer vision systems, particularly with respect to old age and disability. 1) Longitudinal health data often contains large amounts of missing information and is almost always irregularly sampled. Principled approaches to model missing data and irregular sampling include Gaussian Processes and, more recently, Neural Ordinary Differential Equations. My research will augment and advance these approaches to take advantage of additional information, including the patterns of missing information and the temporal distribution of samples. I will also investigate how Transformer Networks could be applied to process long-term trends in healthcare image/video data and in relation to other parameters of health or relevant information. 2) My work has identified fairness and bias issues related to the performance of state-of-the-art computer vision facial analysis models on faces of older adults with a physical or cognitive disability, e.g. stroke or dementia. This is an important limiting factor in employing computer systems in healthcare solutions. Deep learning models used to obtain best performing results on standard benchmarks are typically trained on hundreds of thousands to millions of training examples. It is not practical to collect representative examples of this size from various clinical populations to include in the training data. Patient data and identifiable health records (e.g. face images) are highly sensitive and access is often restricted. A more realistic solution is to collect targeted represented training sets and to adapt state-of-the-art models trained on standard benchmarks datasets to the target population via transfer learning. In my work so far, I have examined commonly used transfer techniques, and have also developed and evaluated novel transfer techniques for this purpose. Using these methods, the performance of facial analysis models on target populations improves, but the gap in performance between clinical and healthy populations persists. Over the next few years, I plan to expand my work in this area and, specifically, explore the use of counterfactual inference and uncertainty quantification to improve the performance of transfer learning techniques, particularly as related to computer vision systems used in facial and body movement analysis. This program of research will train seven HQPs and will result in innovative solutions to reduce bias and to better incorporate long-term temporal information in computer vision systems.