# Psychophysical State Aspect during UAV Operations
## 1 Introduction  
Unmanned aviation is one of the fastest growing modes of transport, and having a pilot's license is becoming increasingly popular. Since 2016, the number of unmanned aerial vehicle operator (UAVO) certificates issued in Poland has increased by 50% annually [1; 2; 3; 4]. Flight safety is the most important aspect in air transportation [5] and unmanned aerial vehicle (UAV) traffic can be monitored and managed through three main elements: operators, UAVs and airspace [6]. One study presents a classification of UAV safety solutions that can be found in the scientific literature, but none of them are concerned directly with the human factor [7]. Another highlights the significant matter of the increasing need for skilled UAV operators and the problem of research on effective UAV operator training not keeping pace with demand [8]. An important finding was that student operators who were overconfident were 15 times more likely to crash [8]. With the knowledge that commercial UAV licensing is expected to significantly increase in the next few years, these results suggest more work is needed in the UAVO training field, and the methods used so far have to be developed. According to the literature, human error is the most difficult component to assess in the prevention of aviation accidents [9; 10; 11]. Assessments of the mental workload for UAV operations are essential in realising the causes of UAV accidents [12]. The development of UAVs, the increasing air traffic of such devices and the increase in the number of issued pilots' certificates make it necessary to pay attention to the matter of the human factor in UAV operations. Providing appropriate safety and efficiency during different operations is possible when the operator performs each task at the right cognitive load level [13; 14; 15; 16]. By analysing the legal regulations (at the level of the European Union and Poland) regarding training for UAVOs, the following problems were identified: the relationship between the assessment of a student operator's predisposition and capacity to perform individualexercises based on the subjective assessment of an instructor; the subjective assessment of a student operator's performance of individual exercises; the lack of objective methods, based on behavioural science, to assess the activities performed by student operators during training; and the small amount of time allocated to individual exercises. The legal regulations also do not provide explicit information about the possibility of adapting the training program to the individual needs and predispositions of the candidate. It was decided to characterize the defined problems as those related to the effectiveness of UAVO training, which in terms of research concerns ensuring safety. How can we manage the cognitive load level and monitor the operator's psychophysical state during training and operations? Human error is usually studied by behavioural and subjective measures. However, subjective and behavioural measures are known as overt measurements and are relatively limited, as they do not allow for continuous and direct assessment of cognitive processes [17]. On the other hand, objective physiological measures can be a good solution for performing psychophysical state assessment. In this research, objective methods (for example, electroencephalography, eye tracking, electrocardiography, etc.) are focused on. These types of measurements allow for analysis to be performed using specific quantitative values originating from human physiological parameters. Most research has applied the psychophysical state analysis method based on cardiovascular system work [18; 19]. In this article, the decision was made to focus on the human factor in training for UAVOs in visual line of sight (VLOS) and to conduct tests in real conditions on the student operators. The parameters of the human cardiovascular system, and more specifically the heart rate variability (HRV), were used to conduct research and analyse psychophysical states.