# 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
Strengths are indicated as follows [59]:  
Figure 5: OWAS evaluation of arm postures. Figure 6: OWAS evaluation of leg postures. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Action Category** & **Interpretation** & **Required Action** \\ \hline
1 & Normal posture, no action needed & None \\ \hline
2 & Slightly risky body position & Correct in the near future \\ \hline
3 & High-risk body position & Correct as soon as possible \\ \hline
4 & Severely harmful posture & Urgent correction is needed \\ \hline \hline \end{tabular}
\end{table} 
Table 3: OWAS structured scoring. Figure 7: OWAS evaluation of the external load for men. * Applicable in real-time with no specialized equipment;
* Entire body evaluation, unlike RULA or AULA, which are more focused on upper limbs;
* Suitable for physical work in logistics, agriculture, construction, and retail sectors. Limitations can be defined as follows [30]:
* Does not account for the duration or repetition of a posture;
* Limited ability to differentiate between subtle variations in joint angles;
* Less sensitive to dynamic tasks involving frequent movement transitions. Case Studies and Industry Applications
* Automotive Manual Assembly: [PERSON] et al. applied OWAS to evaluate postures during manual seat assembly in the automotive sector [65]. The study showed that workers performing repetitive overhead and forward-bending tasks accumulated high-risk OWAS postures (code 4232, AC4). The use of AR-based training and ergonomic redesign reduced exposure to harmful postures and significantly improved cerebral oxygenation, indicating reduced fatigue. * Retail Intralogistics: [PERSON] et al. (2021) analyzed logistics workers in warehouse picking and packaging using OWAS and CUELA (a digital strain analysis system) [65]. In four standard task configurations, OWAS identified a high frequency of AC3 and AC4 scores, primarily associated with trunk flexion and static leg postures. Recommendations included redesigning shelf heights and rotating tasks more frequently to reduce the lumbar load. * Agriculture Sector: In a comparative study by [PERSON] et al. (2020), OWAS was used alongside RULA and REBA to evaluate 196 farm tasks [63]. While OWAS was less sensitive to the upper-limb load than RULA, it proved effective at identifying risky full-body postures, particularly during squatting and bent-knee tasks such as planting and harvesting. * Manufacturing Simulations: [PERSON] et al. (2020) employed OWAS within a digital human modeling platform (Teconomatix Jack) to simulate ergonomic scenarios using data from a 360\({}^{\circ}\) spherical camera [67]. Postural risk profiles derived through OWAS were used to assess improvements achieved by collaborative workstations. The integration of OWAS in simulated environments validated its applicability in Industry 4.0 contexts. Digital Integration and Future Directions  
Modern OWAS applications increasingly rely on pose estimation and digital human modeling tools. For example, [PERSON] et al. (2022) reported an OWAS classification accuracy of 94% using 3D pose estimations for well-visible postures and 83% for occluded views, supporting its feasibility in vision-based automated systems [59]. Future developments include  
* Combining OWAS with cognitive workload indicators;
* Real-time risk alerts through wearable motion sensors;
* Integration in digital twin environments and augmented reality simulations. In conclusion, OWAS remains a robust and versatile tool for whole-body ergonomic risk assessments and is particularly suitable for physically intensive and static posture-dominant work environments. While it has limitations in detecting fine-grained postural shifts and task dynamics, its integration with Industry 4.0 technologies, such as AR, VR, and 3D motion tracking, enhances its precision and applicability. The system provides actionable insights that support proactive workplace design and long-term health sustainability.