## 3 State of the Art
### Additional Human State Measuring Techniques for Collaborative Robotics
Either eye trackers or adapted cameras for computer vision can be useful to detect variables related to different human factors; however, they depend on many aspects, such as light, to function properly. Another useful technique that was first used years ago to measure stress and fatigue is cardiac rhythm. A cardiological study [59] revealed that arrhythmias and tachycardias are directly correlated with the level of stress and fatigue felt by patients. Correct measurement with the right sensors can determine the level of stress and, therefore, emotions that a person feels when interacting with a robot [59]. The variety of devices capable of measuring heart rate is quite wide--from wearable devices such as smartwatches to body bands or stickers placed on the chest [60]. Among all of them, it is necessary to take into account different limitations, such as battery, connectivity problems, signal quality, accuracy, security when handling data, and device pricing [60]. The studies mentioned above claim that heartbeat sensors are valuable tools for monitoring stress and fatigue [59]. That is because variability in heart rate can provide indications of a person's emotional and physical state. However, when it comes to determining ErrPsor evaluating complex emotions and cognitive responses, such as those related to EEG, these sensors may not be the best choice due to their limited ability to capture detailed information about specific brain processes. In the context of exploring methodologies relevant to medical research, several technologies have been identified as effective in evaluating an individual's stress levels or physiological state in real time. Notably, the measurement of cortisol, a hormone intricately associated with stress response, has emerged as a significant area of interest [61]. The precision offered by cortisol as a metric is high, yet the challenges lie in the actual process of measurement, particularly in achieving real-time data acquisition. Recent advancements have been made in real-time cortisol measurement, signaling a promising yet still emerging area of scientific exploration. As research in the field advances, there has been a notable increase in the variety of methods being developed to measure human interactions and stress-related emotions. Techniques such as breath rate monitoring [62] and observation of changes in facial complexion, including blushing [63], are indicators of stress levels. These technologies are essential in the realm of human-robot interaction, providing a deeper understanding of human emotional states. Their integration into robotic systems holds the potential to significantly enhance the way robots interpret and respond to human emotions, leading to more empathetic and effective interactions. Questionnaires and scales are self-reporting tools in which people answer questions about their emotional states and level of stress after performing specific tasks. Some of the most relatable examples of self-report questionnaires are the Beck Depression Inventory (BDI), which is used to assess depression, and the Perceived Stress Questionnaire (PSS), which is used to measure perceived stress. The BDI is a broadly used tool for measuring the severity of depression in individuals. It was developed by psychologist [PERSON] in 1961 [64; 65]. The Perceived Stress Questionnaire is used to assess how stressed a person feels in relation to his or her life experiences, circumstances, and tasks [66]. Despite the existence of numerous questionnaires that are consistent and achieve the proposed result, it is always possible to create questionnaires to reveal different variables of interest in a particular experiment. In conclusion, this subsection highlights various techniques for measuring human states in the context of human-robot interaction. While EEG remains prominent, alternative methods like eye tracking, computer vision, and cardiac rhythm analysis offer valuable insights. Each method has its strengths and limitations, making selection context-dependent.