## 3 State of the Art
### Additional Human State Measuring Techniques for Collaborative Robotics  
In this subsection, several techniques related to measuring human states, apart from EEG, are described for their application in human-robot environments. A comprehensive overview is provided in Figure 6, which features a detailed diagram of the human body, indicating the placement of various bio-sensors. These sensors include EOG (Electro-oculography), RSP (Respiration Rate), GSR (Galvanic Skin Response), ECG (Electrocar-diography), and BVP (Blood Volume Pulse). This figure serves as a valuable illustration, showcasing the diversity and potential applications of bio-sensors in collaborative robotics. Further exploration of some bio-sensor devices, as highlighted in [51], is undertaken to provide an overview of their various applications within collaborative robotics and reinforcement learning. This examination aims to illuminate how these sensors can be integrated into robotic systems to enhance human-robot interaction, ensuring more intuitive and effective collaboration. One of the techniques that has been useful in human-robot interaction is eye tracking. An eye tracker is a device that records and follows a person's eye movements during human-robot interaction, allowing them to understand how a human focuses attention and responds to visual stimuli. Numerous studies have revealed that the eyes can play a significant role in communication and anticipation of a robot's actions [52; 53]. Eye trackers have been used in different investigations to determine the actions of collaborative robots, as well as in stress detection. The idea behind using an eye tracker is associated with the ease of reading the signals with the right device. The only drawback of this type of technology is the delay between reading and interpreting the signal, which can mean that the human is in a completely different state. Stress detection is one of the most interesting areas behind eye trackers, as it could allow for the training of a RL agent based on the investigations discussed in the previous paragraphs. For the detection of this type of stress-related variable, it is important to take into account pupil diameter and the number of gaze fixations [54]. Another technology that allows for the detection of different emotions and movements is computer vision. Computer vision refers to the application of different algorithms in combination with image and video processing techniques to identify gestures, postures, and facial expressions in real time. The aim of this kind of technique is to understand and analyze human behaviors and associated emotions [55]. One of the most interesting disciplines in computer vision is human activity recognition (HAR). These types of disciplines have wide applications in fields such as human-machine interaction, robotics, and video games, where HAR improves understanding of human intentions and emotions [55; 56]. Thanks to human activity recognition, computer vision may have a place in robotics and reinforcement learning applications, as shown in an interesting recent study [57]. However, computer vision research normally targets the use of computer vision in anomaly de  
Figure 6: Different bio-sensors and their positions in the human body [51]. tection, with insufficient examples of its integration with reinforcement learning. Anomaly detection implies using computer vision techniques to identify unusual behaviors and events in videos or images; this plays a significant role in security surveillance systems [56; 58]. Although computer vision is a widely used research technique, it has many drawbacks. Reliability under variable lighting conditions or in blurred images is usually a problem, as these systems often fail under harsh conditions. In addition, interpreting complex images or detecting objects in unusual situations still represents a significant challenge for computer vision. Finally, there is a risk of bias and discrimination in computer vision systems when training datasets are not properly represented [55].