## 2 Related Works  
Traditionally, postural analysis in ergonomic studies relied on manual observation, self-reporting, or subjective assessments, such as Ovako Working Posture Analysis System (OWAS) [6], Strain Index [7], Nordic Musculoskeletal Questionnaire [8], etc. However, these methods are subjective, time-consuming, and prone to bias.  
Meanwhile, the field of ergonomics posture categorization has seen significant advancements in recent years, with researchers focusing on developing objective methods to assess and analyze human postures in various settings. Numerous studies have investigated different techniques and approaches for posture detection, aiming to improve ergonomics practices and promote better musculoskeletal health. One common approach in the literature involves the use of wearable sensors such as accelerometers [9; 10] and inertial measurement units [11] (IMUs) to capture and analyze body movements and postures. Motion capture systems can be implemented based on a system of special markers, sensors or trackers located on the torso, hands and legs, combined into a system that can accurately record the movements of the entire body [12; 13]. These sensors provide real-time data on joint angles, acceleration, and orientation, allowing for the quantification and classification of different postures.  
The emergence of AI, particularly computer vision techniques [14], has provided new opportunities to automate and enhance the characterization of postures, including in the field of ergonomics. These methods utilize image and video processing algorithms to extract relevant features from visual data captured by cameras or depth sensors. Techniques such as image segmentation [15], posture estimation [16], and skeletal tracking [17] have been employed to identify and analyze body postures.  
In the meantime, researchers have explored machine learning and deep learning to accurately classify and identify specific postures based on sensor data [18; 19]. Specifically, transfer learning [20], a subfield of deep learning, has obtained attention for its ability to leverage pre-trained models to improve classification performance. Also, researchers have leveraged other deep learning architectures [21] and posture estimation models like OpenPose [22] and PoseNet [23] to achieve high accuracy and robustness in posture detection from image data.  
Among these models, Movenet [24] stands out as a promising option for application in various scenarios, due to its strong ability to detect key joint features using image information. It has been proved that MoveNet can be applied to create a software for monitoring physical activities in the elderly [25], and can be extended to the classification of stroke patients based on videos captured by smartphones [26]. In scenarios where precise and complex measuring instruments are challenging to use for posture detection, MoveNet achieves more accurate results with simple image data alone, showcasing its immense application potential.  
While significant progress has been made in posture detection research, there is still room for improvement. Challenges remain in dealing with variations in environmental conditions, clothing, and individual body characteristics. For instance, human activity classification may be confounded by the background of the image such as in a farm, and the clothes that a person wears, their gender role or ethnicity might be related to their occupation. Moreover, the practical implementation of posture detection systems in real-world agricultural settings requires considerations such as computational efficiency, system usability, and integration with existing agricultural practices.