## 4 Results
### Neurophysiological Characterization of Mental Workload in Industry 5.0  
Mental load has been monitored in most studies carried out in an operational context [10; 11; 13; 16; 23], so there are several neurophysiological metrics describing it in the literature. [PERSON] and colleagues [13] employed eye-tracking and heart rate measurement to analyze the mental load of senior workers during assembly tasks with collaborative robots (cobots) within ergonomically advanced workstations. The metrics extracted from eye-tracking (duration and frequency of fixations) permitted the cognitive engagement and mental load of the participants to be evaluated. The duration and frequency of blinks were employed as indicators of fatigue and mental effort. When a dual task (comprising the main task and a secondary mathematical task) was performed, a notable increase in blink frequency was observed, indicating an elevated cognitive load. Moreover, these metrics were capable of predicting the number of errors made, suggesting that an augmented mental load has a detrimental impact on performance. Cardiac activity was employed as an indicator of the level of physical and mental effort. Heart rate confirmed a general engagement during the tasks but was not sensitive enough to detect more subtle variations in cognitive load, i.e., between the single-task and dual-task conditions. Ultimately, eye-tracking proved to be a more effective method for detecting changes in cognitive load during complex tasks, providing an in-depth under-standing of workers' mental load. Similar eye-tracking metrics were used by [PERSON] and colleagues [10] in a study aimed at the simultaneous determination of mental load and learning during collaborative assembly processes. A different parameter of cardiac activity, namely the heart rate variability (HRV) was employed for the purpose of monitoring continuous, non-intrusive physiological responses, with the objective of assessing participants' cognitive states, particularly in relation to mental fatigue and workload [16]. The participants were required to complete a task of polishing metal surfaces in collaboration with a joystick-controlled UR10 robot, with the assistance provided varying between low and high levels. The task required the participant to follow a predefined 'S'-shaped trajectory, delineated on the metal surface, comprising three lateral movements and two 'U'-shaped curves. The HRV and indices HF, LF, and HF/LF ratio, indicative of sympathetic and parasympathetic activity, were employed as a mean of classifying the participants' levels of mental fatigue and workload. It was observed that an increase in mental fatigue resulted in a predominant reduction in para-sympathetic activity or an increase in the LF/HF ratio. Moreover, the HRV data were correlated with a number of experimental variables, including the level of robotic assistance, the gender of the operator and the fatigue status of the participants. Concerning mental fatigue, during robot-assisted sessions, increased parasympathetic activity was detected in the final blocks, suggesting potential recovery from fatigue levels. Concerning sex effects, women showed significant differences in LF/HF parameters compared to men, suggesting different sensitivity to fatigue states. Another widely used neurophysiological method for characterizing mental load is the EEG [55]. Spectral analysis was found to be more frequent. In a recent study, [PERSON] and colleagues [23] employed electroencephalography (EEG) to validate a neuroergonomic workstation for a human-centred, collaborative robot-supported manual assembly process. A BrainWatch module, comprising a commercial EEG device (Smarting MOBI) and a custom-developed algorithm for the calculation of workload indices in real time, was employed to measure three indices related to the Cz channel: theta-alpha ratio (TAR), theta-beta ratio (TBR), and engagement index (EI).