# Deep Learning-Based Postural Asymmetry Detection Through Pressure [PERSON]
## 5 Discussion  
This paper presents a set of deep learning algorithms designed to predict postural asymmetries, deformity development, and developmental disturbances from data collected with a commercial pressure mat. Pressure mats offer a simple, portable, and continuous method for capturing pressure distribution data, which make them well suited for detecting subtle postural asymmetries that could indicate emerging healthcare problems. The algorithms, which have been developed in collaboration with healthcare specialists, were trained for continuous patient monitoring and early detection of postural asymmetries. The first results show their potential to facilitate the continuous management of posture-related health problems, improving patient care. To our best knowledge, this study is the first to use DL with pressure mat data specifically aimed at posture anomaly detection and analysis of the consequences of these in relation to the development of deformities and functional repercussions. Previous research has largely focused on specific applications, such as bed posture classification for monitoring patients during sleep or preventing pressure ulcers [25; 27; 28]. Our approach extends the scope of practical postural monitoring, allowing greater access and faster analysis of posture, providing greater accessibility and practical utility. For comparison, other state-of-the-art studies, such as those employing joint-node plots for postural control [68] or logistic regression for physical condition detection through postural balance [26; 69], demonstrate strong performance in limited domains but rely on traditional machine learning methods. Similarly, advanced DL models like temporal convolutional networks and BiLSTM networks have shown high accuracy in reducing pressure peaks in critical neonates [70], but these focus on specific use cases. Our DL framework, by combining CNNs, YOLOv8, and LLaMa3, provides an integrated solution for detecting postural anomalies and assessing their clinical impact in dynamic environments, addressing a broader range of postural issues and paving the way for scalable, personalized care systems. \begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Report Section** & **Description** \\ \hline General description of the performed test & \\ \hline Patient information & Patient data (name, age, gender, weight, height, family history, etc.). \\ Asymmetries & Postural asymmetries of the patient during the assessment and their corresponding medical description. \\ \hline Pressure data analysis using LLM & \\ \hline Mean pressure & The evolution of the average pressure throughout the video collected with the pressure platform is shown and analyzed. \\ Hemibodies & The results obtained by the CNN that calculates six or seven points are analyzed and compared with other patients’ results by the LLM. \\ Temporal evolution of the pressures of different parts of the body & Additionally, the results obtained by the object detection model, which recognizes different body parts, are analyzed and compared with other patients’ results by the LLM. \\ Asymmetry of area and pressure & In this last section of the report, the area and pressure axes of the population with normotypical unhealthy development are compared with the data collected from the population with alteration of the motor-healthy sphere. \\ \hline Conclusions & \\ \hline Conclusions & A text describing and concluding the generated report is added. In this text, the most important results found are discussed, as well as the therapeutic recommendations. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Generated report structure. However, there are some limitations. The current algorithms are still in the early stages of development and require additional validation by healthcare specialists to guarantee their reliability in different patients.