# Biosignals Monitoring of First Responders for Cognitive Load Estimation in Real-Time Operation
## 2 Theoretical Framework  
### Biosignals  
In face-to-face situations, users communicate and exchange information enriched with emotional cues such as facial expressions, voice intonations, gestures, or body positions to transmit what respondents want (need, desire, love, etc.) or do not want (afraid, dislike, hate, detest, etc.). In 1988, [PERSON] et al. [37] identified four sources of evidence, namely: (a) self-report, (b) physiology, (c) behavior, and (d) language. The selection of each tool involves the consideration of different factors [38] such as:  
* **Awareness**: Relates to the degree of the respondent's cognizance of his/her emotions being captured. Physiological emotion signals are considered more implicit than self-reporting that is based on the user's subjective experience.
* **Obtrusiveness**: The user's experience of the medium. Sensors that are attached to the human body (EMG or Electrooculography EOG) have been reported to be obtrusive.
* **Invasiveness**: Realistic use in real-life settings. The standard computer equipment (webcams for recording facial expressions, measuring keyboard pressure or mouse clicks from log files) considered non-invasive in contrast with the use of extra equipment (professional cameras or artificial labs) or long questionnaires for self-report.
* **Task relevance**: Measurement is applied in parallel with the user's task (real-time) without interrupting the learning process. Task irrelevance is the main flaw of self-reporting.  
Other factors include the possible cost of special equipment or special expertise needed to run the equipment universality (e.g., language and cultural barriers).  
Wearables that measure sympathetic autonomous nervous system (SANS) signals (i.e., heart rate, blood pressure, skin conductance), usually obtain measurements from the finger, the chest, or the hand/wrist. These sensors provide an objective measure of physiological signals with high transparency, deploying continuous monitoring of the user state, and usually, are not disruptive to task performance. They are more appropriate to detect states of high arousal like stress, anger, fear, excitement, or panic (and not affective states such as boredom), as the more intense the emotion signal, the more precise is the measurement. Normally, these metrics require special and expensive devices and technical expertise is needed to run the equipment, which is sensitive to confounding factors (e.g., heat, lighting) resulting in noisy data. Popular wearable physiological signals include [39]:  
* Electromyography: measured by the muscle response to a nerve's stimulation of a certain muscle.
* Electroencephalography (EEG): brain activity.
* Galvanic Skin Response (GSR)/Electrodermal Activity/Skin Conductance (EDA or SC): record the electrical activity in the skin.
* Electrocardiogram (ECG): heart activity (heart rate, inter-beat interval, heart rate variability).
* Electrocoulogram: eye pupil's size and movement.
* Blood Volume Pulse (BVP): relative blood in an area.
* Respiration: measures rate of respiration and depth of breath.