# Deep Learning-Based Postural Asymmetry Detection Through Pressure [PERSON]
## 1 Introduction  
Deep learning (DL) [1], a subfield of artificial intelligence (AI), has become a powerful tool in healthcare: its ability to analyze large and complex datasets can help to improve the diagnosis, treatment, and management of diseases [2]. Compared to traditional statistical learning methods, which often use feature engineering to create robust characteristics before models are developed, this multilayered neural network-based method represents a significant advance [3]. According to [PERSON] et al. [4], deep learning's extensive learning capabilities reduce the need for manual feature engineering, making it more suitable for complex data scenarios where extensive domain knowledge is not available. In addition, the healthcare sector is expanding rapidly, attracting researchers, clinicians and technical experts who want to use the most advanced technologies to improve patient care. This rapid growth offers opportunities to transform healthcare services by increasingaccess to advanced diagnostic tools, cutting-edge treatment options, and minimally invasive procedures. As described by [PERSON] et al. [5], the idea is to achieve faster recovery times and less patient discomfort. Technological advances have also released the potential of big data in healthcare, enabling a data-driven transformation that provides healthcare professionals with more accurate and actionable information [6]. Machine learning (ML) techniques have been crucial to this evolution; however, DL offers even greater potential for handling the huge and intricate data typical of medical environments. As DL models require larger volumes of data than traditional ML approaches, they are well suited for large data environments such as healthcare. This evolution towards the use of DL technology is reshaping medicine by enabling more robust, accurate, and efficient systems [7]. The integration of DL models into healthcare improves the quality of patient care, optimizes resource allocation, streamlines workflows, and reduces operating costs, enabling healthcare professionals to provide personalized and timely interventions [8]. Some common applications of DL in healthcare are medical imaging and diagnostics [5; 7; 8; 9; 10; 11; 12], virtual patient care [13; 14; 15; 16], medical research and drug development [13; 17; 18], and rehabilitation [13; 19; 20; 21; 22]. Additionally, in healthcare, the potential of deep learning goes beyond conventional applications and includes innovative approaches to monitor patient well-being [23]. One emerging application is the integration of DL with pressure platforms for human care. These are also known as pressure mats, which are composed of sensor arrays that detect applied pressure, provide a unique way of interacting with patients, and provide information on body movements, posture, and balance without the need for invasive methods [24]. This technology is especially beneficial for applications that require continuous monitoring in a non-disruptive manner, such as elderly care, early childhood monitoring, and fitness applications [25]. In this context, pressure mats provide critical insights into pressure distribution patterns, which can reveal postural asymmetries linked to musculoskeletalal issues like scoliosis or muscular contractures. With advancements in data analytics and deep learning, these tools are now integral to non-invasive, real-time posture monitoring and personalized healthcare solutions.