# Mind, Machine, and Meaning: Cognitive Ergonomics and Adaptive Interfaces in the Age of Industry 5.0
## 3 Ergonomic Techniques in Human-Machine Interactions
### Methodological Frameworks in HRA  
HRA encompasses a set of qualitative and quantitative techniques designed to evaluate the likelihood and impact of human errors. Common methodologies include  
* HEART (Human Error Assessment and Reduction Technique)--assigns error-producing conditions to specific tasks to estimate human error probability (HEP) [70];
* SHERPA (Systematic Human Error Reduction and Prediction Approach)--developed by [PERSON], this method is regarded as a highly effective approach for examining human reliability in task execution, with a focus on the cognitive factors behind human error [71];
* CREAM (Cognitive Reliability and Error Analysis Method)--it was introduced by [PERSON] in 1998 and it builds upon a cognitive process framework, the Contextual Control Model (COCOM), which describes the relationships between cognitive processes occurring in the human brain [72]. These methods differ in complexity and application domains, with HEART and SHERPA often used in manufacturing contexts and CREAM more prevalent in safety-critical industries. HRA in Manufacturing: Types of Human Error  
[PERSON] et al. (2021) applied SHERPA and HEART to a complex manual assembly process and grouped thirty-six action errors, nine related to selection, eight involving information retrieval, and six checking errors [73]. The study revealed that geometry-sensitive components (like cushioned loop clamps) had the most significant risk of human error. Perceptually enhanced instructions and feedback loops were found to be effective at mitigating such errors. In another context, [PERSON] et al. (2020) discussed reliability differences between human-operated and robot-assisted machining lines [74]. Simulation models in FlexSim demonstrated that human operators are more likely to introduce errors during fatigue or when handling non-repetitive tasks. The use of HRA allowed the identification of systemic weaknesses where training or automation could reduce human-induced failures. Embedding wearable sensors into the system, digital twins, and machine learning has allowed for real-time human reliability monitoring. For example, [PERSON] et al. (2024) utilized Tecnomatix JACK software and motion-capture data to model maintenance operations for aircraft brakes and wheels [27]. Their digital ergonomic model accounted for anthropometric variation and predicted high-risk postures contributing to reliability issues. [PERSON] et al. (2023) developed a CNN-based posture recognition model using inertial sensors to estimate wrist flexion/extension [69]. Their system achieved 65% agreementwith the gold-standard optoelectronic systems and demonstrated the feasibility of using AI for HRA in tasks with repetitive hand movements. These digital tools allow the proactive identification of failure points before errors manifest, extending HRA from a diagnostic tool to a predictive one. HRA Applications: Industrial Case Studies  
- Aircraft Maintenance ([PERSON] et al., 2025): A biomechanical model incorporating motion capture data was proposed for aircraft maintenance tasks [68]. By integrating inverse trigonometric algorithms, the model quantifies joint stress and task difficulty, directly supporting HRA metrics. The model proved capable of real-time feedback, enabling ergonomic and reliability-based scheduling. - Assembly Line Guidance ([PERSON] et al., 2021): In a manual bracket installation process, the HEART method predicted the highest HEP values where operators had to choose among geometrically similar components [73]. Visual guidance systems and color-coded interfaces reduced the probability of action errors significantly. - Human-Robot Collaboration ([PERSON] et al., 2020): In simulation scenarios comparing human vs. robot operator reliability, HRA-informed scenarios demonstrated that mixed-mode (human and robot) setups outperformed human-only cells in stability and quality under high-volume settings [74]. The insights allowed for process redesign where robots handled repetitive tasks and humans were assigned tasks requiring judgment.