## 3 State-of-the-Art Technologies for Emotional Management
### Emotion Recognition  
From the technological point of view, there are two main methods to identify users' emotions: measuring physiological changes and analysing expressive behaviour. Recognising emotions based on users' expressive behaviour has the main advantage of not requiring contact with a measuring device or a sensor, because usually this type of recognition is achieved by analysing facial expressions [32] and body gestures [33] by means of computer vision and artificial intelligence techniques or by analysing variations in the tone or speed of speech by audio-signal techniques [34]. Nevertheless, current emotional recognition systems that use physiological signals have increased research interest [35; 36]. Although plenty of devices are capable of measuring these physiological signals in the human body [37], the most frequently used devices in the field of Affective Computing are described below. * Analysis of electrodermal activity (EDA): it is related to the sympathetic nervous system. The human being reacts to behavioural, cognitive and affective phenomena activating sweat glands to prepare the body for action. The sensors measure this secretion of sweat [38]. * Analysis of the electrocardiogram (ECG): ECG is a reliable source of information and it has considerable potential to recognise and predict human emotions such as anger, joy, trust, sadness, anticipation and surprise. More specifically, to detect these emotions, it is required to extract the Heart Rate Variability (HRV) from ECG measures [39]. * Analysis of the electroencephalogram (EEG): EEG is an electrophysiological monitoring method used to register the electrical activity of the brain by the use of electrodes. EEG signals can reveal relevant characteristics of emotional states. Recently, several BCI emotion recognition techniques have been developed based on EEG [40]. Knowing that ECG, EDA and EEG are the main methods to detect emotions without users'  
Figure 3: Research about the facial movements employed to convey universal emotions. Image source [29]. voluntary control over them, some authors prefer EEG-based systems for emotion recognition [41]. This is because ECG measurements experience a large delay between stimuli and emotional response, and EDA-based systems cannot report the valence dimension when used on their own. * Analysis of the respiratory rate (RR): emotional states can be identified by means of their respiratory pattern. For instance, happiness and other related positive emotions produce significant respiratory variations, which include an increase in the pattern variability and a decrease in the volume of inspiration and breathing time. Positive emotions vary their effects in the respiratory flow depending on how exciting they are; i.e. the more exciting ones increase the respiratory rate. On the other hand, feeling disgust suppress or cease respiration, probably as a natural reaction to avoid inhaling noxious elements [42]. As mentioned in Section 3.2, discrete and dimensional models can be used to classify a set of emotions. Discrete models are widely used when emotion recognition is performed by analysing expressive behaviour. In particular, it is even more common to recognise emotions by facial expressions due to [PERSON]'s research [43]. When recognising emotions by means of physiological signals, the preferred models are the dimensional ones, that is the models that are based on valence, arousal and dominance (Figure 4). Frequently, several types of physiological signals are mixed. For instance, in the literature, heart rate has been used to measure different levels of valence; electrodermal activity (EDA) and eye tracking have been used to measure arousal. Analysis of facial expressions and head poses, has been used to predict both valence and arousal. An example of the combination of recognition techniques is [44], in which the dimensional model is used to describe how emotional states provoked by affective sounds could be recognised efficiently by estimating dynamics in the autonomic nervous system (ANS).