## 1 Introduction
Lastly, physiological signals have a higher potential to lead to objective results, because they are an immediate outcome of the human autonomic nervous system (ANS) and the endocrine system, which directly enable emotional responses [12,14]. Different from behavioural parameters, the assignment to a certain emotion is not influenced by the subjective perception of the investigator. As the ANS cannot be controlled actively or consciously, it is impossible to mask or hide an emotional response. This makes physiological ANS responses more robust reflections of emotional states [15]. However, many of these measurement techniques, among others electromyography (EMG) [15], electroencephalography (EEG) [16], electrocardiogram (ECG) [17] and functional magnetic resonance imaging (fMRI) [18], are not comfortable and/or possible for real-life monitoring of emotions, since the equipment can restrict free movement or requires too often battery recharging and is therefore not convenient for ambulatory assessment. As in many other research fields, machine learning (ML) and deep learning (DL) techniques are becoming more popular, instead of the traditional statistical analysis methods. In ML methods, the features for the classifier are hand-crafted. However, this kind of method requires many labelled data, which is the biggest challenge in the field of emotion recognition. Furthermore, feature extraction and selection are very time-consuming and laborious. DL algorithms, on the other hand, can extract and select features automatically [19]. An important disadvantage of most ML and DL models is that they have so many parameters, that it is impossible to estimate them in real-time on each individual subject. Another important disadvantage of DL methods is that they are purely black-box, meaning that it is extremely hard to understand the selected features [20]. Thus, also on the analysis level, there are still many challenges in the field of emotion recognition. The Mindstretch mobile application was developed by a spin-off company of KU Leuven (Belgium) to monitor individuals' mental energy use and recovery [21]. These data are used to gain more insights into individuals' mental energy balance and to early detect or even prevent serious mental health problems. Mental energy can be described as the metabolic energy used to perform cognitive tasks [22; 23]. It is calculated based on heart rate (HR) and acceleration data collected by any Fitbit wristband on the market. Mindstretch takes into account that the responses of living organisms are complex, individually different, time-varying and dynamic [24]. The human body is in constant need of energy to provide the basal metabolism, the immune system and the thermal regulation of energy while performing mental and physical activities and to maintain homeostasis. For most people during most of their lifetime, this energy is provided by aerobic metabolism. This means that oxygen is used to produce metabolic energy. This oxygen is pumped to the cells throughout the body via the cardiovascular system. HR measurements can thus be used as a measure for oxygen consumption and consequently also for metabolic energy production [25]. Metabolic energy consumption can be designated into four categories. The first category is basal metabolism, which can be considered the minimal energy consumption of the body, keeping all organs functioning when no need for thermal control of the body in rest [26; 27]. The magnitude of the basal component depends on the age, weight, metabolism, etc., of the individual. Secondly, energy is used for thermoregulation. The body temperature needs to be kept constant to make sure that cell processes take place under optimal conditions. Thirdly, physical activity also requires metabolic energy. This component can be estimated based on the dynamic responses of heart rate to the activity monitored by the acceleration data [24; 28]. Lastly, there is metabolic energy used for the category'mental activity'.