Video-Based Fall Detection and Medical Image Denoising Methods

医疗技术可以帮助管理日益增加的老年人口对医疗系统造成的日益增长的压力，老年人口容易福尔斯。图像处理算法构成了为跌倒检测而构建的系统的基础，并且可以提高医学图像的质量，以便正确评估伤害的影响。研究人员最近专注于基于视频的跌倒检测解决方案。文献中提到的方法受到速度-准确性权衡的影响，因为准确性的任何改进都会导致更长的执行时间，并且也容易出现误报。该项目旨在研究是否以及如何开发一种更准确的方法，而不会牺牲速度。然后，必须在类似于现实世界条件的环境中验证该方法。跌倒发生后，医生必须使用受影响区域的扫描来评估受伤的严重程度。医学图像会受到噪声和硬件引入的各种伪影的影响。在医学图像中的噪声抑制方法方面已经进行了大量的工作。然而，通常只检查伪影的类型。因此，该项目将调查是否以及如何制定一种适应性办法，以查明和有效地消除多重影响。然后，明智的做法是调查该方法在多大程度上适用于所有类型的医学图像。对于这两个研究部分，在完成文献综述后，将制定算法的初始版本，并收集显示不同程度骨折的跌倒场景和医学扫描数据集。然后将编写机器学习代码来实现和测试算法，以便进一步优化。考虑到数学在信号处理领域的基础作用，拟议的研究项目主要可以归类为工程领域，并与数学科学有一些交叉。博士项目的价值是多方面的。首先，可以将所提出的跌倒检测算法集成到商业监控解决方案中，因为代码将免费提供。因此，护理人员和紧急服务人员将迅速得到有关跌倒事件的通知，以便患者及时接受医疗护理。此外，医学图像增强的工作将通过简化诊断损伤严重程度的过程来帮助医生。第二个研究部分的结果也将对更广泛的研究界有用。了解所提出的方法在多大程度上可以应用于不同类型的医学图像，将使研究人员能够提取与自己的工作相关的结论。总而言之，拟议的博士项目将专注于开发一种强大的跌倒检测算法和一种自适应医学图像增强技术，旨在改善已经开展的工作和患者的结果。

Medical technology can help manage the growing strain imposed on healthcare systems by the rising elderly population, who is prone to falls. Image processing algorithms form the basis of systems built for fall detection and can enhance medical images' quality, so that the impact of injuries is correctly assessed. Researchers have recently focused on video-based solutions for fall detection. The approaches mentioned in the literature suffer from the speed-accuracy trade-off, as any improvements in accuracy lead to longer execution times, and are also prone to false positives. The project seeks to investigate if and how a more accurate method, which does not sacrifice speed, can be developed. The approach must then be validated in an environment resembling real-world conditions. After a fall has occurred, physicians must use scans of the affected region to assess the injury's severity. Medical images are degraded by noise and various artifacts introduced by hardware. Extensive work has been undertaken on noise suppression methods in medical images. However, only type of artifact is typically examined. The project will hence investigate if and how an adaptive approach, which can identify and effectively combat multiple effects, can be formulated. It would then be wise to investigate to what extent the method is applicable to all types of medical images. For both research components, after the completion of the literature review, an initial version of the algorithms will be formulated and datasets of fall scenes and medical scans showing varying degrees of bone fracture will be collected. Machine learning code will then be written to implement and test the algorithms, so that further optimisations can be made. The proposed research project can be classified primarily into the area of engineering with some cross-over with mathematical sciences, considering mathematics' fundamental role in the signal processing field. The PhD project's value is multifold. Firstly, it will be possible to integrate the proposed fall detection algorithm into commercial monitoring solutions, as the code will be freely available. Carers and emergency services will thus be quickly notified about a fall event, so that the patient receives medical attention in a timely manner. In addition, the work on medical image enhancement will assist physicians with their work by simplifying the process of diagnosing an injury's severity. The findings of the second research component will also be of use to the wider research community. Understanding to what extent the proposed approach can be applied to different types of medical images will allow researchers to extract conclusions relevant to their own work. To summarise, the proposed PhD project will focus on the development of a robust fall-detection algorithm and of an adaptive medical image enhancement technique with the aim of improving the work already undertaken and outcomes for patients.