# Recognition of Stress Activation by Unobtrusive Multi Sensing Setup
Beside alterations related to cardiac diseases [8], HRV is an important measure of mental stress and, coupled with the HR, is commonly used to monitor individual wellness in behavioral research [9]. The gold standard for HR and HRV assessment is ECG recording that allows the fine localization of heart beats [10]. In recent years, several methods have been studied to allow the non-contact measurement of HR and HRV, including HR from speech [11], thermal imaging [12], microwave Doppler effect [13], and imaging photoplethysmography (iPPG) [14; 15; 16; 17]. The latter approach could greatly simplify data acquisition, making measurement easily available in non-clinical scenarios (e.g., driver monitoring [14], human-machine interaction monitoring [18]). According to our previous experience [19], iPPG can offer a valid alternative to standard PPG. Respiratory rate (RR) carries important information on a person's health condition and physiological stability, an abnormal respiratory rate being a strong illness indicator [20]. In particular, respiratory rate increases significantly under stressful situations [21]. Current methods to collect respiration data include the use of respiration belts, measurement of impedance through ECG electrodes, spirometers, or visual observation/counting. These techniques have drawbacks that limit the frequency and convenience of the respiratory monitoring. The large diffusion of wearable devices has stimulated interest in monitoring athlete training, with the aim of maximizing performance, and minimizing the risk of injury and illness. In these field, chest belts are a common choice. It worth mentioning that respiratory rate too could be monitored by imaging ([22; 23; 24]). Galvanic skin response (GSR) is sensitive to many different stimuli (strong emotion, a startling event, pain, exercise, deep breathing, a demanding task, cognitive workload, and stressing stimulation) and identifying the primary cause of a particular skin-conductivity response may be hard. Anyway, a lot of different studies reported that the electrodermal response represents an adequate measure for stress-related sympathetic activation [25]. GSR can be measured by different methods. In general, GSR sensor measures the real-time skin conductance which is related to the sweat gland activity depending on emotional response and environmental condition [26]. GSR is typically acquired in hand fingers. In recent years, the interest of the scientific community has progressively expanded toward multi-sensing technologies able to integrate different signals and build effective monitoring system useful to detect dangerous conditions and driving coping actions [27]. In particular, machine learning paradigms looks very promising, and their application is an active field of investigation [28; 29; 30]. A common framework of most studies on stress detection/monitoring is the evaluation of response to maximal (or intense) stress. This provides significant details on the individual capability to react to severe stress. On the other hand, maximal activation is not the most common experience in everyday life. People usually face a wide spectrum of stressors implying a variety of activations both positive and negative. In this work, we focus on the impact of mild stimulations which can be somehow comparable to usual conditions that everyone can deal with in daily life. The aim is to set up a general platform useful to observe the individual status in routine setting (e.g., working in an office, driving a car) making it possible to design minimally obtrusive monitoring/testing procedures for detecting stress situations even in response to mild cognitive activation or other daily activities. In the following we will report on the use of machine learning techniques for recognizing activation statuses with respect to resting conditions. To obtain a description of individual status, iPPG, respiratory waves and GSR signals are integrated at feature level.