# 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
This method proves valuable in automating large-scale ergonomic assessments, especially in settings with numerous workers or real-time monitoring needs [59]. * Inertial Sensor-Based RULA Automation: [PERSON] et al. proposed a wearable inertial sensor system that automates RULA and REBA scoring. Their results achieved an over 88% classification accuracy compared to expert assessments. This method is suitable for high-risk, dynamic tasks where visual scoring is impractical, such as in heavy industry or confined workspaces [58]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Grand Score** & **Action Level** & **Interpretation** \\ \hline
1–2 & Level 1 & Acceptable posture \\ \hline
3–4 & Level 2 & Further investigation needed \\ \hline
5–6 & Level 3 & Investigation and changes required soon \\ \hline
7 & Level 4 & Immediate investigation and changes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RULA structured scoring. Agriculture Sector: [PERSON] et al. conducted a bibliometric review showing that agriculture, healthcare, and manufacturing are the top domains for RULA use. In agricultural contexts, such as fruit harvesting or rubber tapping, high RULA scores have led to equipment redesign and optimized workflows. The authors noted RULA's consistent tendency to detect high risk in repetitive, bent-forward upper-limb postures [59; 60]. Effectiveness and Reliability  
Validation Accuracy: In recent experimental setups using digital sensors or pose estimation, RULA scores matched expert evaluations with over 90% accuracy [59; 60]. Statistical Reliability: Intraclass correlation coefficients (ICCs) often exceed 0.80 in well-trained evaluators, though variability remains for complex tasks [58]. Comparative Sensitivity: RULA was shown to yield higher risk ratings than OWAS and REBA in static or upper-limb focused tasks, but lower sensitivity in dynamic whole-body postures [63]. In conclusion, the RULA remains an indispensable tool in ergonomic analysis, especially when assessing upper-body static or repetitive work. Its application has been enhanced by emerging technologies such as collaborative robots, computer vision systems, and wearable sensors, which improve scoring accuracy and allow for large-scale deployment. However, for full-body assessments or highly dynamic work scenarios, RULA should be complemented with other tools such as REBA (Rapid Entire Body Assessment) or biomechanical simulation models. 2.2. Ovako Working Posture Analysis System (OWAS): Ergonomic Assessment Methodology and Applications  
The Ovako Working Posture Analysis System is a method developed in the 1970s by the steel surface treatment company Ovako Oy for the evaluation of workers' postures and the identification of musculoskeletal disorder (MSD) risk factors in industrial environments. The system was originally designed for tasks involving heavy manual handling, particularly in steel manufacturing, but has since been widely adopted across various sectors such as agriculture, logistics, healthcare, and manufacturing [64; 65]. OWAS assesses postural load by classifying body positions and providing action categories to determine the urgency of corrective ergonomic measures. It is widely used due to its simplicity, rapid application, and ability to cover the entire body's postural configuration during work tasks [63]. Method Description and Scoring Principles  
OWAS evaluates the worker's posture based on four components [66]:  
Back posture (four categories) (Figure 4);  
Arm postures (three categories) (Figure 5);  
Figure 4: OWAS evaluation of back posture. Each body part is assigned a code based on its position during the task. The combination of these codes results in a unique posture code (e.g., 2121) which is then linked to one of four Action Categories (ACs) (Table 3). The method is typically applied through a visual observation or video analysis of work tasks. The frequency and duration of each posture code are documented by analysts to evaluate the overall risk associated with the task [64; 67].