# Real-Time Monitoring of Passenger's Psychological Stress
## 3 Discussion  
A lot of improvements are still to be made in the physiological stress model reported in this paper. As further stated in Section 4, we used a simple linear model for regression and classification, and purposely did not perform feature selection. Such an optimization is our next step in developing a human stress model, but this also requires collecting data from a larger set of participants, and to get more information about their psychological feelings and environmental factors that may influence the recorded physiological signals. In that perspective, the feature set could be optimized both by enrichment (considering other kinds of signal properties) and reduction (performing efficient feature selection). Additionally, more complex models should be tested, which might provide more accurate and robust estimations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **No. Ex.** & **RMSE (Raw Output)** & **RMSE (Clipped Output)** & **Cr \%** \\ \hline Shuttle & 68 & 0.202 \(\pm\) 0.041 & 0.189 \(\pm\) 0.039 & 97.1 \(\pm\) 4.0 \\ Train I & 95 & 0.189 \(\pm\) 0.031 & 0.154 \(\pm\) 0.026 & 92.6 \(\pm\) 5.3 \\ Train II & 83 & 0.171 \(\pm\) 0.031 & 0.094 \(\pm\) 0.017 & 100 \(\pm\) 0 \\ _Mean_ & - & _0.187 \(\pm\) 0.035_ & _0.146 \(\pm\) 0.027_ & _96.5 \(\pm\) 3.2_ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performances for each transport mode during the test phase. Still, our approach brought up some interesting results. For instance, the two train examples in Figure 1 illustrate the influence of ambulatory conditions on model performances: as soon as the participant stopped working on his laptop, the stress level assessed by our model decreased, along with RMSEs on the reported stress levels; and all samples were correctly labelled as no-stress data. This phenomenon could be timely monitored as shown in Figure 1c,d. Even though the variability in physiological signals is intrinsically taken into account by our model through the feature set, notably with features based on standard deviation, real-life conditions will still decrease its accuracy. What is more, it should be noted that participant's stress levels unlikely remained the same all the time during each condition, as assumed in this preliminary study--in our approach, we considered as the ground truth the mean stress level reported by the user over a given period. Regarding the matter of ground truth, determining which kinds of events are the most suitable to train a stress detection model is not a trivial question. In an end-user setting, soliciting user intervention as little as possible is a key issue: to calibrate a reliable stress model, an efficient trade-off has to be found between user acceptance and algorithm's requirements, regarding information collection on affective states and environmental factors. In our experimental design, a compromise had thus to be brought up between, on the one hand: full awareness on environmental factors and participant's feelings, and on the other hand: some coarse ground truth, related to specific events. We hence designed our grey-box model on the grounds of three _stressful_ and one _control_ conditions (the last one being non-stressful but still active), delineated in retrospect by the user himself. The three self-reported periods of moderate-to-high stress occurred in different circumstances. Varying participant's environment, mental or physical activity during the stress condition was a prerequisite to the construction of a general model, whose accuracy would extend to any new stressful situation. Furthermore, adding an event that was not related to the trip itself (e.g., giving a speech during a meeting) allows assessing the ubiquitous nature of the psychological state that is quantified--given that stress is not specific to travelling.