# Deep Learning-Based Postural Asymmetry Detection Through Pressure [PERSON]
## 1 Introduction
In the same way, when a neuro-musculoskeletal pathology or condition occurs that conditions posture and mobility, it is possible, with the use of a pressure platform that extracts this data, to: objectively know the repercussion of this pathology on the decubitus posture; detect the risks referred to the appearance or evolution of deformities based on the asymmetrical distribution of weight and supports (scoliosis and hip dysplasia/luxation can be considered the biggest problems in children with motor disabilities); and know the modifications that the different actions carried out generate in these postural patterns and, therefore, in their evolution. As [PERSON] et al. [26] comment, in this context there is a clear relationship between contractures and deformities of the spine and hips with the presence of pain and asymmetries in seated and supine (face up) positions in children with cerebral palsy and, therefore, in those pathologies or health conditions that involve alterations in posture, muscle tone, and movement. Accordingly, the fusion of DL with pressure mats is a very promising approach to advance non-invasive health monitoring. Using DL, pressure data can be analyzed in real time to interpret patient behavior and physiological patterns [25]. By leveraging DL, pressure sensing technology can transform human care, enabling smarter, proactive, and data-driven decision-making in healthcare. Some of the applications of pressure mats in healthcare include pressure ulcer prevention [27; 28; 29], sleep research and bed posture monitoring [29; 30; 31; 32; 33], posture and movement assessment and rehabilitation [27; 34; 35; 36], detecting false alarms in neonatal patients [37], and assessing and working on balance in standing position [38; 39]. On the other hand, generative AI [40; 41] has emerged as a fundamental tool for healthcare, with immense potential to transform clinical decision-making and improve health services. Unlike traditional AI, generative AI can create new data, such as text and images, that can be used to support multiple healthcare functions [42]. As a result,generative AI could transform diagnosis, improve patient care, and expand treatment options by generating innovative simulations, synthetic data, and diagnostic insights that can complement clinical expertise and improve patient outcomes. As concluded by [PERSON] and [PERSON] [43], generative AI will play an increasingly important role in medicine and healthcare in the next years. Furthermore, with the large and growing amount of data that healthcare professionals need to analyze, generative AI provides an automated and efficient approach to report generation. For example, generative AI algorithms can quickly summarize patient medical history, integrate recent test results, and suggest potential treatment options, allowing clinicians to provide a more efficient and complete view of a patient's condition [44]. This level of automation can save healthcare professionals significant time, reduce administrative burdens, and minimize the possibility of human error. Therefore, this paper will discuss the use of different deep learning algorithms with data collected with a pressure platform. For this purpose, data from patients with and without postural asymmetries has been gathered, and it will be used for several interesting tasks: patient classification, hemibody division, recognition of different body parts, and automatic report generation associated with these data. The objective of this research has been to investigate the application of AI technologies for the detection and assessment of postural disorders and motor disabilities, whether or not they are related to motor area diagnoses. The idea is to help health professionals with the prevention, early diagnosis, assessment, and monitoring of patients, especially children and young people. The rest of the paper is organized as follows. Section 2 presents the generated dataset. Section 3 describes the deep learning techniques used. The training results are discussed in Section 4.