Year: 2025
Type: conference-paper
Abstract: In paper proposed approach to recognizing postural disorders from visual data based on analysis of anatomical landmarks coordinates of spine, determined using deep learning neural network. The quantitative model for assessing spinal curvature is proposed, which provides an interpreted representation of the spatial characteristics of posture. The calculated angular values allow identifying deviations from the norm in the spine geometry. Visualization of key points and their mutual position form the basis for explaining the model solutions. The method for recognizing postural disorders from visual data is developed for analysis of key anatomical landmarks of the spine, determined using deep neural network. Using the coordinates of key points (cervical, thoracic, lumbar and sacral spine), the method assesses the spine curvature and determines deviations from the norm, which allows identifying pathological changes, such as hyperlordosis, hyperkyphosis or scoliosis. The method integrates geometric analysis with threshold-based classification procedure, which makes it possible to apply it in automated posture control tasks. The research is aimed at resolving the contradiction between the need for accurate posture analysis and the requirements for the availability and ease of use of appropriate technological solutions: traditional hardware provides high accuracy, but limits scalability due to the high cost and operation complexity. The proposed method is based on visual data analysis and does not require specialized equipment. This allows to reduce the barriers to implementation and at the same time provide an interpretable assessment of posture, which demonstrates the possibility of eliminating the specified contradiction by using computer vision and explanatory artificial intelligence models. The general problem of determining the presence of posture disorders is solved by the developed method with Accuracy 0.86, in conditions of different image quality, two-dimensional nature of analysis and fixed body orientation.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7167819829