Saudi Cultural Missions Theses & Dissertations
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Item Restricted The Role of Physical Activity in Reducing Falls and Fragility-Related Fall Risks in Sedentary Older Adults Aged 50-69(University of Dundee, 2024-03-19) Aljohany, Maysa Awdah; Drew, TimBackground: The risk of falling increases with advancing age and is a major health problem in older adults. As the risk increases so does instability, loss of balance and consequently injury in older populations aged over 50. This requires the introduction of new interventions to prevent further pressure on the NHS and health systems worldwide. Exercise has commonly been noted as a beneficial and cost-effective intervention to support improvements in fall risk in older adult populations. This study carried out a systematic review that thoroughly analysed multiple research studies to uncover optimal fall prevention techniques for individuals aged over 50. The assessment focused on how exercise therapies influence balance improvement and the reduction of fall risks within this aged group. Methods: Search methods: Electronic searches in PubMed, ScienceDirect and Google Scholar. Selection Criteria: Studies were required to focus on older adults or postmenopausal populations aged between 50-70 years who engaged in a physical activity intervention and were compared to either a comparator or control group. Data extraction and analysis: Seventeen studies were eligible for inclusion. Data extraction and risk of bias assessments were independently conducted by a reviewer. Data were collected regarding various components relating to the exercise interventions as well as the participant demographics which supported the collection of information regarding the effect of the interventions.113 0Item Restricted The Immediate Effect of Changing the Footplate Profile of a Rigid Ankle-Foot Orthosis (AFO) on Lower Limb Kinematics and User Feedback(University of Strathclyde, 2022-08-05) Albasri, Renad; Cox, ChrisBackground: Rigid ankle-foot orthoses (AFOs) are widely used within clinical practice to address pathological gait. A literature review search illustrated AFO footplate variations are under investigated, despite this being a key design feature in AFO prescription. There are currently no studies investigating how dorsiflexing the footplate would affect lower limb kinematics and user feedback. Objective: To evaluate the effects of dorsiflexing the footplate on hip, knee and ankle kinematics and user feedback. Study design: Feasibility study. Methods: Healthy participants (n = 10) were tested under four test conditions: shoe- only, rigid AFO with 0° of dorsiflexion at the footplate, rigid AFO with 15° of dorsiflexion at the footplate and rigid AFO with 30° of dorsiflexion at the footplate. Data on hip, knee and ankle kinematics was obtained from midstance to pre-swing. User feedback was established through verbal free text comments and scoring the visual analogue scale. Results: The most significant finding in this study is identifying 100% of the participants preferred a dorsiflexed footplate over a flat footplate. No statistically significant differences were observed; however, trends in hip, knee and ankle kinematics were identified. Conclusion: We recommend orthotists consider dorsiflexing the footplate of rigid AFOs as part of routine clinical practice. Further research is required to comprehend the effects of dorsiflexing the footplate on pathological gait and to determine a suitable prescription criterion.11 0Item Restricted Anatomical, biomechanical and histological evaluation of the glenoid labrum(The University of Edinburgh, 2024-04-26) Almajed, Yousef Abdulaiz M; Alashkham, AbduelmenemThe glenoid labrum is an integral part of the highly mobile glenohumeral joint. Labral injury can be detrimental to the movement and function of the shoulder. Therefore, an in-depth investigation of glenoid labrum microanatomy may improve our understanding of its roles and functions in the glenohumeral joint. This project aimed to explore the (1) composition and attachment, (2) vascularity, and (3) innervation of the glenoid labrum. Labral degenerative changes were also assessed relative to the changes in labral neurovascularity that were observed. Sixteen cadaveric shoulders were obtained under the supervision of a licensed anatomist from the Department of Anatomy, University of Edinburgh and in accordance with the Human Tissue Act (Scotland) of 2006. After dissection, the glenoid, including the labrum and the attachments of its associated structures, was segmented into eight regions and processed for histological analysis. Three histological stains were utilised to evaluate the composition of the labrum and assess its attachments. General labral morphology was first assessed in haematoxylin and eosin-stained sections. This was followed by staining with toluidine blue to assess the total and percentage of the labral area occupied by proteoglycans as an indirect measure of its capacity to accommodate a compressive load. Finally, Verhoeff-Van Gieson stain was used to localise elastin fibres within the labral cross-section. Labral vascularity was assessed immunohistochemically using an anti-alpha-smooth muscle actin antibody and evaluated quantitatively by determining the vascular count, vascular density, and blood vessel distribution within the labrum. Labral innervation was assessed immunohistochemically with an anti-p75 antibody that facilitated the quantitative analysis of nerve count and nerve density. Nerve types were identified following the modified Hagert (2008) classification. Degenerative changes detected in the labrum were assessed following a histopathologic grading system described by Pauli et al. (2011). After microanatomical quantification, differences in proteoglycan content, vascularity, and innervation were assessed across various labral regions, and correlations of labral vascularity and innervation to proteoglycan area and proteoglycan percentage were determined. Finally, labral vascularity and innervation were also evaluated in labral tissues exhibiting various grades of degeneration. The glenoid labrum is connected to the glenoid bone via a fibrocartilaginous attachment that includes a fibrocartilaginous transition zone that links the bone to the fibrous labrum. Elastin fibres exhibited a distinct localisation within the labrum, including the capsular attachment, between large collagen bundles and within the fibrocartilaginous regions. Blood vessels and nerves were identified in all labral regions. Four types of mechanoreceptor nerve endings were identified within the labrum, including Ruffini corpuscles, Pacini corpuscles, Golgi-like nerve endings and unclassified corpuscles. The labral sections ranged from normal to moderately degenerated; no severely degenerated labra were identified. The analysis revealed significant variation across all labral regions and a significant inverse correlation between vascularity and innervation with proteoglycan content. However, there were no significant differences in vascularity and innervation across the degeneration spectrum. In conclusion, the glenoid labrum was vascularised and innervated throughout its circumference, but exhibited significant microanatomical variations that were linked to compressive load accommodation properties. The different types of nerve endings found in the labrum suggest that the labrum contribute to the sensorimotor function of the glenohumeral joint. Whilst the degree of degeneration had no apparent influence on labral vascularity or innervation, a larger sample size will be needed to substantiate these findings. The results of this study will lead to improvements in our understanding of the microanatomy of the glenoid labrum and have important implications for shoulder function and stability. The findings may also provide critical insights into methods that may be used for injury management and new rehabilitation strategies.34 0Item Restricted Deep Learning-Based Digital Human Modeling And Applications(Saudi Digital Library, 2023-12-14) Ali, Ayman; Wang, PuRecent advancements in the domain of deep learning models have engendered remarkable progress across numerous computer vision tasks. Notably, there has been a burgeoning interest in the field of recovering three-dimensional (3D) human models from monocular images in recent years. This heightened interest can be attributed to the extensive practical applications that necessitate the utilization of 3D human models, including but not limited to gaming, human-computer interaction, virtual systems, and digital twin. The focus of this dissertation is to conceptualize and develop a suite of deep learning-based models with the primary objective of enabling the expeditious and high-fidelity digitalization of human subjects. This endeavor further aims to facilitate a multitude of downstream applications that leverage digital 3D human models. The endeavor to estimate a three-dimensional (3D) human mesh from a monocular image necessitates the application of intricate deep-learning models for enhanced feature extraction, albeit at the expense of heightened computational requirements. As an alternative approach, researchers have explored the utilization of a skeleton-based modality, which represents a lightweight abstraction of human pose, aimed at mitigating the computational intensity. However, this approach entails the omission of significant visual cues, particularly shape information, which cannot be entirely derived from the 3D skeletal representation alone. To harness the advantages of both paradigms, a hybrid methodology that integrates the benefits of 3D human mesh and skeletal information offers a promising avenue. Over the past decade, substantial strides have been made in the estimation of two-dimensional (2D) joint coordinates derived from monocular images. Simultaneously, the application of Convolutional Neural Networks (CNNs) for the extraction of intricate visual features from images has demonstrated its prowess in feature extraction. This progress serves as a compelling impetus for our investigation into a hybrid architectural framework that combines CNNs with a lightweight graph transformer-based approach. This innovative architecture is designed to elevate the 2D joint pose to a comprehensive 3D representation and recover essential visual cues essential for the precise estimation of pose and shape parameters. While SOTA results in 3D Human Pose Estimation (HPE) are important, they do not guarantee the accuracy and plausibility required for biomechanical analysis. Our innovative two-stage deep learning model is designed to efficiently estimate 3D human poses and associated kinematic attributes from monocular videos, with a primary focus on mobile device deployment. The paramount significance of this contribution lies in its ability to provide not only accurate 3D pose estimations but also biomechanically plausible results. This plausibility is essential for achieving accurate biomechanical analyses, thereby advancing various applications, including motion tracking, gesture recognition, and ergonomic assessments. Our work significantly contributes to enhancing our understanding of human movement and its interaction with the environment, ultimately impacting a wide range of biomechanics-related studies and applications. In the realm of human movement analysis, one prominent downstream task is the recognition of human actions based on skeletal data, known as Skeleton-based Human Action Recognition (HAR). This domain has garnered substantial attention within the computer vision community, primarily due to its distinctive attributes, such as computational efficiency, the innate representational power of features, and robustness to variations in illumination. In this context, our research demonstrates that, by representing 3D pose sequences as RGB images, conventional Convolutional Neural Network (CNN) architectures, exemplified by ResNet-50, when complemented by as tute training strategies and diverse augmentation techniques, can attain State-of-the-Art (SOTA) accuracy levels, surpassing the widely adopted Graph Neural Network models. The domain of radar-based sensing, rooted in the transmission and reception of radio waves, offers a non-intrusive and versatile means to monitor human movements, gestures, and vital signs. However, despite its vast potential, the lack of comprehensive radar datasets has hindered the broader implementation of deep learning in radar-based human sensing. In response, the application of synthetic data in deep learning training emerges as a crucial advantage. Synthetic datasets provide an expansive and practically limitless resource, enabling models to adapt and generalize proficiently by exposing them to diverse scenarios, transcending the limitations of real-world data. As part of this research’s trajectory, a novel computational framework known as "virtual radar" is introduced, leveraging 3D pose-driven physics-informed principles. This paradigm allows for the generation of high-fidelity synthetic radar data by merging 3D human models and the principles of Physical Optics (PO) approximation for radar cross-section modeling. The introduction of virtual radar marks a groundbreaking path towards establishing foundational models focused on the nuanced understanding of human behavior through privacy-preserving radar-based methodologies.16 0