# Deep Learning-Based Postural Asymmetry Detection Through Pressure [PERSON]
## 4 Results
### Hemibody Division  
Another aspect that specialists find very interesting is the description of which hemibody bears more weight than the other (with percentages and time). However, dividing the patient's body into two hemibodies from the data collected by a pressure platform is not easy for several reasons:  
* The distribution of pressures can vary significantly depending on the patient's posture, weight, body distribution, and other individual factors. * Each patient has a different morphology. Weight distribution and body shape vary considerably from one individual to another. This means that there is no standard way of dividing the body into hemibodies that works for all patients. * Patients may move and change posture while on the pressure mat. These movements can alter the pressure distribution significantly and rapidly, which further complicates the task of dividing the body into hemibodies. Figure 8: Model training and validation metrics: (**a**) accuracy and (**b**) loss. Figure 7: Distribution of postural asymmetries in training and validation sets. * The resolution and accuracy of pressure mats may not be sufficient to clearly identify the boundaries between the two halves of the body. Pressure diffuses, and it may be difficult to discern the exact points of demarcation. Therefore, to address the above complexities, DL is proposed for the following reasons. On the one hand, DL-based algorithms can learn to recognize complex patterns in pressure data from large training datasets. This allows the system to adapt to different patient morphologies and conditions. On the other hand, AI algorithms can detect complex patterns in pressure distribution that would not be evident by traditional data analysis methods. Additionally, they can handle variability in pressure data due to patient movements, changes in posture, and noise, providing more reliable and consistent results. For this purpose, a regression CNN has been developed that calculates \(N\) points in the pressure image. The idea is that these \(N\) points can be used to divide the hemibody perfectly so that the pressure can be associated with each of the two hemibodies. For this purpose, the models have been tested to predict six or seven \(x\) and \(y\) points, which is equivalent to predicting 12 or 14 values by the convolutional neural network. An \(x\) and \(y\) point represents the position on the vertical and horizontal axes of each of the different points selected for the division of the hemibody in the pressure image. The points have been distributed as shown in Figure 9. In the six-point figure, these are distributed as follows: top and bottom of the head, under the neck (correspondence with verteb C7), body (midpoint of the thorax-abdomen unit), croch (pubis), and between the feet (midpoint between both feet). In the figure of seven points, an extra one is added to the body (replacing the trunk point with two others: the xiphoid process of the sternum and navel), as recommended by specialists, to try to divide the hemibody better in case of postural asymmetries. The initial number of manually labeled images to train this model was 590 (442 for training and 148 for validation). However, data augmentation (horizontal flipping of the images) has been applied to the training images to improve the accuracy of the model. Also, both input images and output values have been scaled between 0 and 1 to improve model performance. In contrast to the CNN used for the classification task, this CNN has a single pressure image as input, as the aim is to be able to obtain six or seven points for each time instant and compare them over the test. The results for a CNN of 176,396 parameters are a mean squared error (MSE) of 6.32 for the six-point prediction and an MSE of 8.44 for the seven-point model. These results demonstrate strong performance for the task.