Video-Based Fall Detection and Medical Image Denoising Methods

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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.