Browsing by Author "Akanyeti, Otar"

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    Automatic Balance Assessment Using Smartphone and AI
    (The Eurographics Association, 2023) Sganga, MagalĂ­; Rozmiarek, Patrycja; Ravera, Emiliano; Akanyeti, Otar; Povina, Federico Villagra; Vangorp, Peter; Hunter, David
    Postural control assessment is essential for understanding human biomechanics in both static and dynamic situations. The relationship between the center of mass (CoM), center of pressure (CoP), and the base of support (BoS) determines whether a person is capable to maintain the balance. Inertial motion units (IMUs) are portable and cost-effective devices capable of measuring acceleration and angular velocity. The integration of IMUs into smartphones provides an accessible means of evaluating postural control in the general population without the need for expensive and time-consuming laboratory setups. A convolutional neural network (CNN) architecture will be employed to predict the difference between the CoM and CoP behavior during different tasks with data from an optoelectronic motion capture system combined with instrumented treadmill. This study aims to establish the foundation for developing an application that assesses postural control and balance in both healthy and pathological populations.
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    Groupwise Non-rigid Image Alignment With Graph-based Initialisation
    (The Eurographics Association, 2018) Aal-Yhia, Ahmad; Malcolm, Paul; Akanyeti, Otar; Zwiggelaar, Reyer; Tiddeman, Bernard; {Tam, Gary K. L. and Vidal, Franck
    Groupwise image alignment automatically provides non-rigid registration across a set of images. It has found applications in facial image analysis and medical image analysis by automatically generating statistical models of shape and appearance. The main approaches used previously include iterative and graph-based approaches. In iterative approaches, the registration of each image is iteratively updated to minimise an error measure across the set. Various metrics and optimisation strategies have been proposed to achieve this. Graph-based methods perform registration of each pair of images in the set, to form a weighted graph of the ''distance'' between all the images, and then finds the optimal paths between the most central image and every other image. In this paper, we use a graph-based approach to perform initialisation, which is then refined with an iterative approach. Pairwise registration is performed using demons registration, then shortest paths identified in the resulting graph are used to provide an initial warp for each image by concatenating warps along the path. The warps are refined using an iterative Levenberg-Marquardt minimisation to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimisation approach is efficient, has very few free parameters to tune and we show how to tune the few remaining parameters. We compare the combined approach to both the iterative and graph-based approaches used independently. Results demonstrate that the combined method improves the alignment of various datasets, including two face datasets and a difficult medical dataset of prostate MRI images.