# Deep Learning-Based Postural Asymmetry Detection Through Pressure [PERSON]
## 4 Results
### Body Parts Recognition  
Another use of DL in this investigation is one of the key aspects that specialists consider necessary when performing clinical analysis with a pressure platform: the ability to attribute pressures to different areas of the body. To achieve this, a DL-based object detection algorithm has been trained, which is able to recognize up to six areas of the body in the pressure images: head, trunk, right arm, left arm, left leg, and right leg. As described in Section 3.2, deep learning-based object detection is an advanced technique that uses CNNs to identify and localize objects within an image. This approach has revolutionized the field of computer vision, enabling unprecedented accuracy and efficiency in image recognition and classification tasks. The choice of a YOLO-based object detection algorithm for object recognition in this study is based on the specific characteristics of the objects to be detected and the nature of the pressure image data. These body parts are represented as regions of varying shapes and sizes within the pressure maps, which often overlap or blend at the boundaries due to the continuous nature of the pressure data. On the one hand, the multiscale detection capability of the network is crucial for identifying body parts of different sizes. On the other hand, YOLO's deep feature extraction layers provide robustness against noisy and low-resolution data. Consequently, YOLOv8 was selected for its strong performance in the challenges of body part recognition in pressure images, outperforming other algorithms that may struggle with the noise, overlap, and scale diversity inherent in this task. The object detection process begins with the training of a neural network on a labelled dataset, which contains images of body areas and their respective labels. During training, the network learns to identify patterns and distinctive features of each body zone. It then adjusts itself to minimize the error in predicting the correct labels. Once trained, the neural network can analyze new pressure images and assign the detected pressures to different areas of the body with high accuracy. This is crucial in the clinical context, as it allows specialists to obtain detailed information about the distribution of pressure in different parts of the body and relate it to functional, orthopedic, postural, and evolutionary data, which are crucial for more accurate diagnosis, prevention, and treatment. Some examples of body part detection are shown in Figure 13.  
Version 8 of the YOLO algorithm has been used for this work due to its good results and its simplicity of implementation. YOLOv8 was released in 2023 by Ultralytics and introduced new features and improvements for enhanced performance, flexibility, and efficiency, supporting a full range of vision AI tasks [53]. Furthermore, it allows us to choose different models from the same version depending on the characteristics of the application being developed (accuracy and inference time). Among the different model options, the lightest version (YOLOv8n) has been chosen, because despite being the model  
Figure 12: Hemibody pressure distribution examples charts: (**a**) patient that moves a lot during the test and (**b**) patient that barely moves during the test. with the lowest number of trainable parameters, it still offers incredible performance (see Table 2). The results in Table 2 show that all recognized classes are detected with high accuracy and recall, with the best-performing class being the trunk. Furthermore, the percentage of how much pressure is applied by each part of the identified body zones is shown in Figure 14. As in the example of the hemibody division, two patients are shown. While the first is a patient with little movement, in which all parts of the body are resting on the sensing surface, the second is a patient with a lot of movement, in which, for example, the left leg is hardly resting at all.