# Recognition of Stress Activation by Unobtrusive Multi Sensing Setup
## 7 Discussion and Conclusions  
In this work we reported on the use of a measurement setup aiming to the unobtrusive monitoring of psychophysical signals for detecting and analyzing potential stressing conditions in everyday life settings. We have jointly analyzed two different datasets (MCA collected by our research group and SRAD from MIT Media lab). Aiming to assess stress activation, both were produced by recording a set of physiological signals in different settings. Our investigation was mainly conducted using the SRAD dataset (the most numerous) as development set, while MCA data were used for independent testing. The work is focused on two main aspects: (a) recognition of activation statuses from resting ones and (b) building a comprehensive but compact description of individual status that could be useful in monitoring individual well-being. From the original data space we extracted a set of 12 features including descriptors of HR, HRV, RR and GSR which are sensitive to individual response to stressors with emphasis on ANS response. Analysis of SRAD feature space by SFFS supports the conclusion that median RR and GSR peaks number has a prominent discriminating power and can lead to recognize activation statuses, which is also confirmed by MCA data analysis. We also tested AE features obtained from the SRAD dataset. They are estimated using whole original data and are expected to reduce the potential information loss of SFFS mechanism. Results suggest that using two AE features can lead to good discrimination of rest states from activation ones. A similar conclusion is obtained using the same AE features for MCA dataset. It stands to reason that our data space is intrinsically two-dimensional with respect recognition of activation condition. This conclusion is also in accord with SFFS results. Though the obtained results are consistent with background literature, they are in a sense surprising as the two datasets used for development and testing were acquired under completely different experimental conditions (while driving or while performing the SCWT). This supports the idea that the used feature set is highly descriptive of individual activation status and able to predict a wide spectrum of activation conditions. When applying standard k-means algorithm using the two most relevant features we observed two clusters that well represent rest and activation labels. This clustering is consistent with MCA data. Figure 9: SOM units labeled according to a majority-voting scheme described in [46]. It must be pointed out that individual responses are intrinsically variable and the use of flexible but compact representations of individual status are highly desirable. In this context, the use of SOM networks revealed promising. Being non-supervised, SOM can autonomously discover significant pieces of information embedded in data space. In addition, they project input data manifold onto network space by preserving topology and related structural properties such as clustering. SOMs derived on SRAD data show the existence of two virtually separated zones in the map: one of them tends to respond to rest statuses, while the other best matches activation statuses. After labeling the SOM with a majority-voting scheme, we correctly classified 85% of MCA data blocks, as compared to 90% by k-means clustering with SFFS. SOM activation maps show that misclassification occurred next the border between the two groups. It is expected that using fuzzy labeling schemes along with extended training and testing data can lead to performance improvement. A significant aspect of SOMs that is relevant for applications is their ability to discover and represent the internal structure of large clusters. In particular, each map unit can be viewed as a prototype (or code) of the individual's status. In this view, activation (or rest) of a person is naturally represented by a structured family of codes. Investigation of the structure of such codes is our current focus of research.