## 4 Discussion  
In stress detection using machine-learning techniques, the most significant performance is obtained when physiological markers from heart rate are used together, temperature, humidity, blood pressure and vocal timbre [58; 59]. In such a competitive job market, overwork has become a handicap that is difficult to overcome, resulting in reduced performance in professional activity. In many cases, self-imposed social pressure is also felt in the academic field, with increasingly unbridled levels of demand or in activities as routine as driving a vehicle, in which precisely a lack of concentration, the result of fatigue, can lead to a fatal outcome [60]. Therefore, early detection of stress not only helps to prevent accidents or severe morbid disorders but also to consolidate healthier work or study climates. Although the evaluation of stressful situations was traditionally limited to highly controlled environments, with the technological development of peripheral devices, stress detection is undertaken in real-time through portable devices, such as a bracelet, a watch or the mobile phone itself, even very low-cost home-made devices. The growth experienced by sensor technology has been transferred to the ubiquitous universe of mobile devices. Today, multiple physiological parameters, such as, e.g., nasal skin temperature, heart rate, eyelid  
Figure 8: (Color online) PR and PR AUC curve for the proposed stress-detection model. Figure 9: (Color online) \(F_{1}\)-score curve for the proposed stress-detection model. movement, voice inflection and typing, can be captured through the different interfaces or sensory extensions that portable devices consist of without altering the behavioral routine. In any case, the performance exhibited by real-time stress-detection techniques is still somewhat relevant compared to traditional methods practiced in controlled environments [61]. The most common classification algorithms that support learning algorithms in the process of detecting mental stress are circumscribed to logistic regression, KNN (_K-Nearest Neighbor_), RF (_Random Forest_) and SVM (_Support Vector Machine_). For the validation of classification models, cross-validation _k-fold_ (\(k=5\) or \(k=10\)) and cross-validation leaving one subject out (_leave-one-out cross-validation_) [59]. Of all physiological indicators, ECG-derived signals such as heart rate and skin conductance (GSR) provide the highest performance in terms of accuracy. However, it is necessary to extract many variables to place the performance at acceptable levels, which are often insignificant. In any case, with the number of variables, the computation time also increases, which jeopardizes its application potential in real-time environments. The characteristics usually provided to classification models attend to linear aspects, both in the time domain and frequency domain and to non-linear aspects of biological signals [62; 63; 64; 65]. Concerning the PPG signal, its close relationship with the cardiovascular system and its easy acquisition, even with the simple camera of a mobile [66], makes it a relevant candidate for stress detection. Previous works related to the identification of a stressful situation through the PPG signal are summarized in Table 4, in which it can be seen how the performance obtained is notorious due to the marked bias that noise induces in the morphology of the signal, as previously mentioned. It should be noted that in all the studies that use the PPG signal, either in isolation or with other biological signals (multimodal analysis) [65], the characteristics that make their cataloging possible refer to the morphology of the signal implicitly or explicitly. As a result of this, all of them are strongly conditioned by psychophysiological variations (e.g., changes in an emotional state, physical activity), by noise disturbances coupled in the acquisition phase of the data or due to statistical inconsistencies due to the non-stationarity of the signals (time interval of the measurements) [27].