期刊
IEEE WIRELESS COMMUNICATIONS
卷 26, 期 3, 页码 62-68出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MWC.2019.1800419
关键词
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类别
资金
- Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia [RG-1436-023]
Automatically recognizing emotions of patients can be a good facilitator of a connected healthcare framework. It can give automatic feedback to the stakeholders of the healthcare industry about patients' states and satisfaction levels. In this article, we propose an automatic audio-visual emotion recognition system in a connected healthcare framework. The system uses a 2D CNN model for the speech modality and a 3D CNN model for the visual modality. For the speech signal, preprocessing is done to extract the PS-PA feature vector. The features from the two CNN models are blended by two ELM networks. The first ELM is trained with gender-specific data, while the other one is trained with emotion-specific data. The proposed system is evaluated using three databases, and the experiments prove the success of the system. In the healthcare framework, we use edge computing prior to intensive-processing cloud computing. In the edge computing, we realize edge caching, which can store the CNN model parameters and thereby perform the testing fast.
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