**7. Conclusions**

This study can be considered to be a basic contribution in terms of overcoming the generalization problem for human emotion recognition. The aim was to show the feasibility and the possibility of building such generalized models for relevant application contexts. Furthermore, this study examined the less intrusive sensors based on statistical analyses in real-life datasets and reviewed various state-of-the-art approaches to human emotion recognition in smart home environments.

Additionally, emotion recognition is a cornerstone of advanced intelligent systems for monitoring a subject's comfort. Thus, information on a subject's emotion and stress level is a key component for the future of smart AAL environments.

In our future work, we will focus on human emotion recognition using EDA with respect to different lab–settings, which means, we will try to build a generalized approach which should be trained using lab–settings X and tested using lab–settings Y. Additionally, we plan to combine Stacked Sparse Auto Encoders with CNN. Moreover, CNN essentially learns local (spatial) features. On the other side, RNN does in essence rather learn temporal features. Consequently, combining both neural network concepts will result in a neuro-processor which can learn both contextual dependencies (i.e., spatial and temporal) from inputted local features. As a result, such a combination does potentially improve the overall performance.

**Author Contributions:** F.A.M. and A.E. conceived and designed the approach; E.A.M. and M.A. performed the formal analysis; F.A.M., K.K. wrote the paper.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest. The authors ensure that there are no personal circumstances, interest or sponsors that may be perceived as inappropriately influencing the representation or interpretation of reported research results.
