# Emotional Health Detection in HAR: New Approach Using Ensemble SNN
## 2 Related Works
SVR was also applied for facial predictions. Both features were merged. Long short-term memory networks (LSTM) were utilized to accomplish the decision-level fusion and capture the temporal dynamics of emotions. To verify the validity of their classification method, the authors compared their t-SNE feature selection method with principal component analysis (PCA) with different dimensions. The results showed that the precision of EEG-based emotion recognition improves with decreasing feature dimensions. Both methods demonstrated the validity of the recognition system. Moreover, t-SNE achieved more significant improvement than PCA. The best performances achieved by t-SNE and PCA were 0.534 \(\pm\) 0.028 and 0.464 \(\pm\) 0.032, respectively, when the dimension of the mapped feature was 15. The results of continuous emotion recognition showed that the fusion of two modalities provided better results than EEG and facial expressions separately. For the results of a single modality, facial expressions were better than EEG. The experimental results found that three steps of LSTM yielded the best CCC (concordance correlation coefficient) of 0.625 \(\pm\) 0.029. The possibility of using wireless devices and networks has stimulated research in the design of innovative models capable of recognizing human activities through the collection of physiological, environmental, and position data to obtain valuable information on the state of health of people, to develop intervention strategies to improve living conditions. In addition, the development of machine learning algorithms makes it possible to deduce human emotions from sensory data that can facilitate the identification of mental situations in need of help. In this context, [PERSON] et al. [19] proposed a model that is based on the detection of emotions in motion and in real-life environments, different from other models in which emotions are detected in laboratory environments and with samples in which emotions are stimulated by audiovisual means or by asking participants to perform activities designed to induce emotional states. The experience was developed with the following:  
* The use of multimodal sensors: physiological, environmental, and position data collected in a global template representing the signal's dynamics together with the temporal relationships of each mode. * The application of different deep learning models to extract emotions automatically. * Collecting data in real situations from subjects wearing a bracelet and a smartphone. * In classifying emotions, the characteristics of the three types of signals are examined individually and combined. The authors used the EnvBodySens dataset, already tested in a previous work [20], which collected data from 40 participants who walked on specific paths. The data were obtained for heart rate (HR), galvanic skin response (SGR), body temperature, motion data (accelerometer and gyroscope), environmental data such as noise levels, UV, atmospheric pressure and location data, GPS locations, and self-reported emotion levels recorded on Android phones (Nexus), wirelessly connected to Microsoft Wrist Band 2 [21]. The self-reported data referred to the responses given by the participants about the sensations they experienced while walking based on a predefined scale of emotions. The data collected included 550,432 sensor data frames and 5345 self-report responses. The signals were preprocessed and subsequently inputted into a hybrid model of a convolutional neural network and long short-term memory recurrent neural network (CNN-LSTM). The results showed that deep learning algorithms effectively classify human emotions when using many sensor inputs. The average accuracy was 95%. In addition, tests carried out using MLP, CNN, and CNN-LSTM models showed that with the hybrid model, the accuracy of emotional states increased by more than 20% compared to a traditional MLP model. The application developed by [PERSON] et al.