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Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocardiogram (EEG), electroencephalogram (ECG), skin temperature, along with facial expressions, voice, posture to name a few, in order to classify emotions. Spiking neural networks (SNNs) represent the third generation of neural networks and employ biologically plausible models of neurons. SNNs have been shown to handle Spatio-temporal data, which is essentially the nature of the data encountered in emotion recognition problem, in an efficient manner. In this work, for the first time, we propose the application of SNNs in order to solve the emotion recognition problem with the multimodal dataset. Specifically, we use the NeuCube framework, which employs an evolving SNN architecture to classify emotional valence and evaluate the performance of our approach on the MAHNOB-HCI dataset. The multimodal data used in our work consists of facial expressions along with physiological signals such as ECG, skin temperature, skin conductance, respiration signal, mouth length, and pupil size. We perform classification under the Leave-One-Subject-Out (LOSO) cross-validation mode. Our results show that the proposed approach achieves an accuracy of 73.15% for classifying binary valence when applying feature-level fusion, which is comparable to other deep learning methods. We achieve this accuracy even without using EEG, which other deep learning methods have relied on to achieve this level of accuracy. In conclusion, we have demonstrated that the SNN can be successfully used for solving the emotion recognition problem with multimodal data and also provide directions for future research utilizing SNN for Affective computing. In addition to the good accuracy, the SNN recognition system is requires incrementally trainable on new data in an adaptive way. It only one pass training, which makes it suitable for practical and on-line applications. These features are not manifested in other methods for this problem.
Objectives. This study aims to analyse physical activity (PA) levels of high school and university students; to estimate their perception of built environment with regard to physical PA; and to assess the relation between PA and built environment. Methods. A sociological cross-sectional study with non-experimental design was applied. The International Physical Activity Questionnaire and the Built Environment Characteristics Questionnaire were filled in by a sample of 1.862 students from high schools and the university in Granada, Spain. Results. High school students were significantly more active than university students, the latter reaching insufficient levels of PA. Nevertheless, they consider Granada as a good context for carrying out outdoor exercise. No relations were found between PA levels and built environment. Conclusion. The discrepant outcomes for PA levels and perceived built environment suggest the need of interventions focused on making youth aware of the possibilities that an environment provides to them for exercising. Consequently, environment could have an impact on their health at the same time as youth learn to respect it.