## 6 Conclusions  
This work shows that emotion recognition can be performed with high accuracy from ECG and GSR signals. In addition, using a new MSE-based feature RCMDE, we found that the derived features of GSR along with the energy, and zero-crossing rate of its EMD patterns, allows for the correct classification of target emotional states. For the GSR signal, its stability characteristics can be used to predict the stress value, while the ECG has a strong mutation, and its frequency characteristics are more important to emotion recognition. Several classification models are trained in the machine learning method to select the model that maximizes the accuracy. In practical applications, the emotion recognition model should not only focus on accuracy, but also on timeliness. Only faster feedback can improve the human interaction experience. In this paper, the public multi-physiological signal database AMIGOS is used as the experimental data to perform preprocessing, feature extraction, and feature selection to verify the effectiveness of the method proposed in this paper. The stimulation materials and acquisition processed of the physiological signal dataset proposed in this paper are briefly introduced. The acquired dataset is used to verify the effectiveness of the proposed method in real scenarios. When analyzing physiological data, it is first proposed to use discrete wavelet analysis, butterworth filter and empirical mode decomposition method to denoise the data. The feature engineering is divided into two categories: manual feature extraction and deep network automatic extraction. Machine learning uses time domain, frequency domain and nonlinear feature analysis to perform traditional feature extraction for two physiological signals: ECG signal and GSR signal. With a 3 s sliding window. Deep network methods are automatically extracted with convolutional neural networks, but their interpretability is not high. The shallow emotional features extracted by machine learning and the deep emotional features obtained by deep learning are, respectively. We present a novel computational framework for affective computing, and the proposed system helps make affective computing applicable to solve problems in our lives, and helps bridge the gap between the representation of low-level physiological signal sensors and high-level contextually relevant interpretations of human emotions. The experimental results obtained from the two optimal algorithms using public datasets show that our feature selection processes use the JMI-Scores algorithm proposed in this paper for feature selection, and the dimension reduction effects is obvious. Feature sets, model parameter are set better than outperform state-of-the-art recognition rates. In fact, we observed an average 0.85% improvement in accuracy. There is also an extensive analysis of feature selection, model selection, time dimensions. Physiology is processed separately with deep learning and machine learning. It turns out that after feature selection and parameter tuning, the two architectures based on new computing systems are effective in emotion recognition, that is, better than previous methods, and in time dimension, the computational space dimension has been optimized. Future work includes optimizing protocols in cloud-side computing systems, taking more into account security, and coordination. Applying more intelligent algorithms to new computing architectures, and developing real-time sentiments detection of wearable systems. Conceptualization, M.C., Z.W. and L.X. ; methodology, M.C. ; software, M.C. ; validation, M.C. and C.L. ; formal analysis, L.X. and Z.W. ; investigation, M.C. ; resources, M.C. ; data curation, M.C. ; writing--original draft preparation, M.C. ; writing--review and editing, M.C., L.X. and Z.W. ; visualization, M.C. ; supervision, M.C. and L.X. ; project administration, M.C. All authors have read and agreed to the published version of the manuscript. This research was funded by National Key R&D Program of China (Grant No.