## 5 Discussion  
We attempted to use CNNs and MoveNet for trunk posture classification within the domain ergonomics in agricultural field settings. Although not proposing a new CNN architecture, our study contributed to the understanding of the suitability of standard CNNs, and developed key features suitable for posture classification and pre-trained models like MoveNet for detecting and classifying postures in a unique environment. The application of these techniques offers insights and advantages in analyzing and evaluating trunk postures from images. CNNs have been widely utilized for image classification and have demonstrated impressive performance in various domains. However, when applied to trunk posture classification in this study, i.e., the ergonomics context, CNNs encountered certain limitations. One of the main challenges was their susceptibility to variations in background image composition, lighting conditions, and occlusions in the outdoor settings. Apart from that, CNNs rely on large amounts of diverse and labeled training data to learn and generalize effectively; meanwhile the images obtained from our study were neither many nor diverse enough. This was due to the nature of ergonomic field study in a single location; that is, there was not much variety of tasks as compared to human daily activities. The complex nature of trunk postures, along with potential variations in clothing, body sizes, and environmental factors, could also lead to difficulties in accurately classifying different trunk posture categories using CNNs. Moreover, CNNs are generally computationally intensive, requiring significant computational resources and time for training and inference, which may limit their real-time application in agriculture field settings. To address the limitations of CNNs, we adopted MoveNet for extracting original anatomical position features for trunk posture classification. MoveNet offered several  
Figure 5: Confusion matrix of posture classification using the new slope and distance features and ANN model (the best model). advantages in our research context. Firstly, MoveNet was specifically designed for posture estimation and could detect some key anatomical points, such as hips and shoulders. This made it well-suited for capturing detailed information about body joint positions and angles relevant to trunk postures. By leveraging MoveNet's capabilities, we could accurately identify and classify different trunk posture categories in images, providing valuable insights into ergonomic risk factors in the agricultural field. Secondly, MoveNet, known for its lightweight architecture, made our image classification more computationally efficient. The model could process static images with fast inference times, enabling real-time trunk posture analysis even on resource-constrained devices commonly found in agricultural environments. This efficiency allows for practical deployment and usefulness of the system in real-world scenarios. Additionally, MoveNet offered our study flexibility and scalability in the context of trunk posture detection. Meaning that, the model could be trained and fine-tuned on diverse datasets, encompassing a wide range of trunk postures in various image backgrounds including agricultural settings. Due to the aforementioned advantages, MoveNet has proven to be versatile and effective in various scenarios. However, as a general-purpose model, it gives all significant key points on the human body of which are redundant features that can interfere with the model's performance when applied. Therefore, to overcome these challenges, we further computed key features for posture classification based on the features extracted from MoveNet. For instance, we calculated planar angles between the nose and hips from the slope of the line between two points, among other features. We then employed feature selection methods to filter in only these crucial features for subsequent model training, resulting in improved classification outcomes.