# Mind, Machine, and Meaning: Cognitive Ergonomics and Adaptive Interfaces in the Age of Industry 5.0
## 3 Ergonomic Techniques in Human-Machine Interactions
### Methods Used in Ergonomic Evaluations  
#### 3.2.1 Rapid Upper Limb Assessment (RULA): Methodological Framework  
and Applications  
The Rapid Upper Limb Assessment is an observational screening tool developed by [PERSON] and [PERSON] in 1993 to evaluate biomechanical and postural strain affecting the upper limbs, neck, and trunk during work tasks. It is especially useful for identifying factors that contribute to musculoskeletal disorders caused by occupational activities (WMSDs) in environments where workers perform repetitive, static, or awkward postures. The method has been widely adopted in both industrial and healthcare settings due to its efficiency, low cost, and practicality [57]. RULA divides the body into two groups for posture analysis:  
* Group A--upper arm, lower arm, and wrist (Figure 2). Figure 1: Cognitive processing flow in human-machine interactions. The scores are  
- One for 20\({}^{\circ}\) extension to 20\({}^{\circ}\) of flexion;  
- Two for extension greater than 20\({}^{\circ}\) or 20-45\({}^{\circ}\) of flexion;  
- Three for 45-90\({}^{\circ}\) of flexion;  
- Four for 90\({}^{\circ}\) or more of flexion. - Group B--neck, trunk, and legs (Figure 3). The scores are  
- One for 0-10\({}^{\circ}\) flexion;  
- Two for 10-20\({}^{\circ}\) flexion;  
- Three for 2/1\({}^{\circ}\) or more flexion;  
- Four if in extension. Each segment is scored based on the angular deviation from a neutral posture, combined with modifiers for force, repetition, and muscle use. The intermediate scores (A and  
Figure 3: The body posture of Group B (neck, trunk, and legs). Figure 2: The body posture of Group A (upper arm, lower arm, and wrist). B) are then cross-referenced in tables to yield a final Grand Score ranging from one to seven. The Table 2 shows a structured scoring approach that allows practitioners to prioritize interventions based on risk severity [58]. Strengths: RULA's key advantage lies in its simplicity, speed of use, and focus on an upper limb evaluation--an anatomical area commonly affected by WMSDs. It does not require special equipment and is thus practical for on-site assessments in real work environments. Studies have validated its sensitivity and correlation with discomfort reports, EMG activity, and kinematic data [59; 60]. RULA has demonstrated strong reliability and accuracy when used with digital pose estimation technologies, including single-view 3D pose estimation models that track worker postures using RGB video footage. These systems, such as the DyWHSE dataset used by [PERSON] et al., have improved the accuracy of the RULA-based wrist posture analysis in complex manufacturing environments [61]. Limitations: Despite its widespread adoption, RULA presents several drawbacks. It is limited in its capacity to assess lower extremity postures, temporal exposure to risk, or postural variability throughout a task. Furthermore, assessments are typically unilateral, which may overlook asymmetrical loads. Some studies have noted inter-rater reliability challenges due to subjective scoring, though training improves consistency [58]. The method also tends to overestimate risks for temporary or transitional postures and can underestimate dynamic loading, which is more accurately captured by tools such as EMG or biomechanical modeling [62]. Applied Industrial Case Studies  
* Wire Harness Assembly with Cobots: [PERSON] et al. applied RULA to assess ergonomic improvements in a wire harness assembly when a collaborative robot (cobot) was introduced. In the original manual process, high scores of six and seven were recorded, primarily due to upper arm elevation and wrist twisting. After implementation, these scores dropped to three, confirming a significant reduction in risk exposure [62]. * Three-Dimensional Pose Estimation in Manufacturing: [PERSON] et al. developed a computer vision-based RULA evaluation framework using 3D human pose estimation. Their system showed 93% alignment with expert RULA ratings in non-occluded views and 82% accuracy in occluded postures.