## 3 The Proposed Method for Operators' Workload Assessment
In particular, physical effort is usually assessed by ergonomic indexes, such as _RULA_ or REBA. The general score of a posture obtained by the use of these indexes indicates the potential risk of development of musculoskeletal disorders, according to different scales. Usually the higher the score, the higher the degree of risk. For the assessment of the cognitive conditions, scientific studies have proved that HR increases as MWL increases, and HRV decreases with increasing mental demand [52]. These cardiovascular parameters may change if the measurements are taken after, and not during, the stressful or mentally demanding event. They could also vary consistently with different levels of stress [53]. For this reason, it is essential to monitor the physiological parameters for the entire duration of the activity to be analyzed. With regard to the eye-related metrics, variations due to a high level of MWL are present in pupil diameter (increment), fixation frequency (decrement), and saccadic frequency (decrement). Opposing results about fixation duration, blink duration and frequency are available in the literature [54; 55; 56]. For this reason, in this work, the focus is on the pupil diameter. Furthermore, some studies have highlighted the relationship between the electrodermal activity (_EDA_) and mental states such as stress, anxiety, fatigue, emotional involvement, mental load, and the level of the excitement caused by the perceived emotion [57]. However, electrodermal activity is considered \"one of the most sensitive psychophysiological indicators of stress\" [50]. Generally the eye-related parameters are used to evaluate the mental workload, while the cardiovascular parameters, along with the electrodermal activity being monitored, are exploited to assess the human stress [58]. Moreover, the performance analysis should be considered in the workload assessment. Indeed, a lowered and/or irregular performance may indicate that the user is reaching unacceptable levels of MWL. Through the secondary task method, it is possible to calculate the mental load associated with the primary one [59]. Figure 1 shows the proposed technological set-up and the related collected data to provide the assessment of workers' physical effort, mental workload, and stress. The proposed assessment method is based on a previous study [60] focused on the definition of a unique UX index to be used during virtual task execution to support the design of assembly human tasks. The previous method has been refined in order to be more robust and general-purpose, and properly extended to better discriminate the cognitive assessment between mental workload and stress. The physical assessment is calculated using the motion capture system. Using the position of body segment trackers, the instant _RULA_ score of operators performing the task for each body side (right and left) can be extracted. The _RULA_ subtask score is calculated as the maximum value reached during the sub-task execution between right and left side, as follows:  
\[RULA_{subtask}=RULA_{max}\left(RULA_{right}\text{, }RULA_{left}\text{ }\right) \tag{1}\]  
From physiological data analysis, a set of parameters are defined as follows. Heart activity parameter (_HA_) is calculated as:  
\[HA=\frac{HR\ mean-HR\ baseline}{HR\ max-HR\ baseline} \tag{2}\]  
where _HR mean_ is the mean value of the specific user's _HR_ as collected during the task execution, _HR baseline_ is the mean _HR_ value as recorded during the user's baseline phase, and _HR max_ is the maximum _HR_ value reached during the task execution for each user. Similarly, _RR_ variability (_RR_) is calculated as follows:  
\[RR=\frac{RR\ mean-RR\ baseline}{RR\ max-RR\ baseline} \tag{3}\]  
where _RR mean_ and _RR max_ are calculated in the same manner as the previous parameter during the task execution, while _RR baseline_ is the mean _RR_ value as recorded during the user's baseline phase.