## 1 Introduction  
The advent of robotic technologies has catalyzed a paradigm shift in industrial operations worldwide. With their deployment, tasks once deemed dangerous or laborious for humans are now executed with robotic precision, freeing human operators to engage in more cognitively demanding activities that necessitate creative and analytical thinking. This symbiotic relationship between humans and robots has given rise to new production processes that prioritize human-robot interaction (HRI), recognizing its impact on both efficiency and safety in automated systems [1, 2, 3]. The integration of human factors in HRI is of paramount importance for understanding the cognitive and physical interplay between humans and robots. As task complexity increases and collaborative robotic environments become more diverse, a comprehensive approach is required to ensure both efficiency and operator well-being. While significant progress has been made in regulating the physical safety of human-robot collaboration (HRC), the cognitive and emotional dimensions of these interactions remain underexplored. Addressing these aspects is crucial, particularly in dynamic industrial settings, where operators are exposed to mental workload, fatigue, and stress, which can significantly impact their performance and overall safety [4, 5]. To regulate physical safety in HRI, international standards such as ISO 10218-2 [6] and ISO/TS 15066 [7] have been developed. These standards establish limitations on force, speed, and proximity, ensuring that robotic systems operate within safe parameters when interacting with human workers. Additionally, ISO/TS 15066 introduces biomechanical limits to mitigate the risk of injury, defining acceptable impact forces for direct human-robot contact. While these regulations represent a significant advancement in physical safety, they do not address cognitive ergonomics' risks, leaving a gap in the understanding of mental workload and its impact on HRC [4, 5, 8]. Mental workload is the cognitive effort required by an individual to achieve a certain level of task performance. This multifaceted construct is influenced by a variety of factors, including task complexity, robot capabilities, and environmental conditions [9]. However, the human factor itself is highly intricate, involving numerous interdependent variables, which makes it challenging to establish a universal and standardized definition of mental workload [10]. On this topic, three states of mental workload can be considered: mental overload, mental underload, and optimal mental workload [11]. Ideally, all employees would have an optimal mental workload level, that is, an appropriate amount and type of work. However, in multitasking environments such as industry scenarios, the mental workload can approach or exceed the upper limit of the worker's mental capacity, and it is known that high levels of mental workload can lead to reduced attention, decision-making errors, and slower response times, ultimately affecting both operator well-being and system efficiency [12]. On the opposite side of the spectrum is mental underload, characterized by a reduced or very easy amount of work [13], which is often associated with monotony and disengagement. Thus, assessing and managing the mental workload of workers is closely related to the ability to achieve multiple optimal performance goals. An accurate measurement and management of mental workload is crucial for maintaining optimal performance and preventing accidents in HRC operations. Nevertheless, it is imperative to highlight the importance of assessing this, as consistent monitoring can identify critical thresholds beyond which operator efficiency declines and the risk of errors increases, thus ensuring both human and robotic components of the system can interact safely and effectively [14]. The ISO standard 10075-3 categorizes mental workload assessment methods into four distinct groups: physiology, subjective, performance, and job and task analysis [14].