## 5 Conclusions  
We designed and developed an ecological paradigm comprising an ad hoc workstation connected with devices for acquiring I/E data from users. Some limitations of the study could be pointed out. First of all, to be able to generalize these results to real-world situations, the temporal length of the experimental sessions should be similar to the duration of real working activities (i.e., at least 1 h-2 h per condition). Thus, there will be the possibility to analyze changes in cognitive and psychophysiological measures over time due to tiredness and drowsiness. Second, insofar as the participants involved were mainly young adults, it is not possible to generalize the results to different workforce categories such as senior workers. Such workers may present different levels of acceptance [49], considering the wearable devices employed (i.e., eye trackers, surface electrodes). We will consider these aspects in subsequent studies. However, the data collected showed that some implicit measures (i.e., eye behavior and heart activity) could be matched with the proposed explicit measure. This combination could be extremely informative about the type of data that can be exploited by a potential symbiotic system. Indeed, the considered measures proved their feasibility in monitoring variations in MWL, ensuring a proper interpretation of users' data. The present findings pointed out that, considering the current experimental tasks, a symbiotic system could exploit the combination of I/E variables (i.e., task accuracy, HR, fixation duration, and blink frequency) to detect situations of high MWL while a participant is accomplishing the ST and DT. Indeed, in the ST, a high MWL (and relatedly, a lower task accuracy) will be reflected by a higher HR, a shorter fixation duration, and a lower blink frequency. In contrast, in the DT, a high MWL (and relatedly, a lower task accuracy) will be reflected by a higher HR, a longer mean fixation duration, and a lower mean blink frequency. By doing so, based on a different mean fixation duration, the system will be also capable of understanding whether the individual is in a condition of high MWL while performing the ST (i.e., shorter mean duration) or the DT (i.e., prolonged mean duration). In this way, the system will be capable of implementing a set of adaptive features to support properly, in both tasks, the individual who is experiencing a condition of high MWL. Moreover, these findings, as previously discussed in regard to data interpretability, underline once again that the type of task, as well as the environmental context, crucially influences the predictions based on these I/E measures. Furthermore, the correspondence between the majority of I/E measures and the subjective ones (i.e., participants' perception of the MWL) suggested that the functioning of cutting-edge systems that will implement adaptive features based on these users' data will be transparent to the operators. The users will understand the changes that the system will operate in consequence of the variations in their internal state (MWL) linked to the working activities. Thus, the human-machine mutual understanding will result in a coordinated and fluent interaction, whereby the technology will support the users whenever needed [50]. In future works, we will implement devices for cognitive and psychophysiological monitoring in an adaptive workstation equipped with a cobot (i.e., that will adjust its operating to I/E measures related to the internal state of users). These experiments will be carried out first in laboratory settings and then in entirely ecological situations (i.e., inside a factory) to establish which design approach would be the most suitable with regards to the different characteristics of the task and the environment. A specific set of measures, in response to which the system will implement adaptive functions, will be grounded on features of both the tasks and environment.