# Real-Time Monitoring of Passenger's Psychological Stress
## 1 Introduction
### Narrow Context and Related Work  
Physiological correlates of acute stress are well known from studies performed in laboratory conditions. Cardiac activity has been widely explored in that perspective [8], as the interbeat intervals can be timely extracted from an electrocardiogram or blood pulse signal, and allow for the retrieval of instantaneous heart rate and its time course, heart rate variability. Another well-known indicator is skin conductance on fingers or wrist: it reflects sdomotor activity, which is mainly controlled by the sympathetic nervous system [9]. A number of statistical features can be extracted from those signals, like the mean heart rate, the mean skin conductance level or the number of skin conductance responses over a given time window, which have shown consistent variations with psychological stress [9]. To this day however, only few studies have been conducted in real-life environments, which can dramatically affect the signals and features previously reported. The ambulatory condition requires carefully considering additional information on context and physical activity for data processing. Regarding the travel topic, a pioneering study was conducted in semi-ambulatory conditions: a car driving session [10]. Authors recorded skin conductance, heart rate, respiration and electromyography, and inferred a continuous measure of stress from contextual information. They subsequently determined the mean heart rate and mean skin conductance, and the power of heart rate signal in the low frequencies, as the most suitable features to detect stress on drivers. On the grounds of a linear discriminant analysis performed on a larger features set, they successfully classified three stress conditions based on context: rest, driving in town and driving on highway. Though, they reported a great inter-individual variability in features' responses to each condition. In a broader sense, this fosters the use of personalized physiology-driven models to estimate stress in ambulatory conditions. Regarding real-life stress detection attempts, the most common approach applies, on the field, a stress model that was elaborated in controlled environments. For instance, authors in [11] calibrated a physiological stress model by means of a laboratory experiment implying four distinct stressful tasks and 20 subjects. When applying their stress model in another real-life experiment, they reached 72% accuracy in stress recognition on time windows with low physical activity. While elaborated from clean and reliable data, a laboratory-made stress model is necessarily unique and may not fit the versatile physiological states of every individual, especially in ambulatory conditions. An alternative approach could be found in personalizing a stress model by measuring a broad spectrum of physiological features directly on the field. In that perspective, stressful-and non-stressful events should be reported by the user themselves during a short learning phase, after which a sensor fusion model could be elaborated offline from the corresponding physiological data. A main challenge should be found in the trade-off to be made: between the quantity of training data (the number of episodes), which increases model accuracy and generalization abilities, and the burden brought to the user by the task of rating and delimiting meaningful time intervals. In the current paper, a small number of well-chosen episodes are shown to be sufficient for reliable stress detection in the travelling context (i.e., full ambulatory conditions with low physical activity). The main study goal was to assess the feasibility of estimating transport-related stress for a single person, within an end-user framework. Hence, the following results are not intended to highlight the best feature set or machine learning technique to quantify human stress: they provide an example of real-time stress estimation from a wearable sensor, in real-life settings.