## 1 Introduction  
The widespread distribution of cutting-edge software and hardware products in the workplace requires more and more individuals to interact with technological systems. Most of these tools are designed and developed to ease the accomplishment of tasks (e.g., avoiding information pollution). They support workers both at the physical and cognitive level, based on the paradigm of the internet of things (IoT), which empowers the fast and flexible information exchange among different technological devices through their digital connection. The IoT concept, applied to the industrial environment, especially in the context of Industry 4.0, results in an interconnected system of physical entities characterized by communication exchanges, data processing, and/or sensing capabilities [1]. The goal of IoT exploitation is to produce a wide range of work-related improvements, such as increased work safety and security, productivity, and efficiency of resource management. Humans are often part of such systems, and whenever the interaction between humans and machines involves a form of mutual support, it could be defined as symbiotic. [PERSON] first coined the term man-computer symbiosis to describe this partnership characterized by cooperative interaction between the two coupled agents, as happens in nature between two living organisms that draw mutual support [2]. When these conditions are met, human beings are able to receive more customized support in performing tasks, and technological devices improve their functioning due to continuous human-computer interactions (HCI) [3]. In this perspective, the standard human-computer communication is explicit and observable, and users can intentionally communicate and act within the system, for example, through a graphical interface. At present, several forms of implicit interaction are spreading, in terms of non-voluntarily types of communication from the human agent towards the computer, to improve HCI [4; 5]. This type of interaction is characterized by the machine's acquisition of users' data that cannot be controlled or manipulated by them. For instance, bio-signals are usually considered to reflect the users' psychophysiological state [5; 6] and could be acquired by utilizing non-invasive sensors. In symbiotic systems, these users' collected data could be shared among all the computational devices connected through the network. Once it detects the operator's need for support (e.g., an extreme level of stress), the system could tailor its interface and functions by implementing real-time adaptive features [7; 8]. These concepts were modeled by [PERSON] and [PERSON] ([3]; see Figure 1). They postulated that a symbiotic system collects meaningful data from both the environment and the human, employing a series of devices such as biosensors (data acquisition). These data are elaborated and interpreted by the computational tools, exploiting artificial intelligence or machine learning algorithms, resulting in a reliable and flexible user's model (data elaboration and interpretation). Lastly, the machine personalizes its actions and decisions on the actual user's needs and desires as they are programmed (execution of automated actions sequence). This sequence of events occurs iteratively. Thus, the symbiotic systems would be more precise in interpreting the users' intentions and behaviours and supporting them in achieving their goals. As a consequence, the performance of the human agent and overall proficiency would improve. Figure 1: Model of a symbiotic system. Symbiotic systems present two crucial limits: the interpretability of the users' data and the transparency of the system's actions. Regarding the former, it must be considered that a monitored measure (e.g., heart rate; HR) has no univocal interpretation. The behavioral and psychophysiological measures may reflect different internal states related to the specific task that the user is accomplishing.