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Hybrid Model-Based Emotion Contextual Recognition for Cognitive Assistance Services.

N Ayari, H Abdelkawy, A Chibani

IEEE Transactions on Cybernetics

|September 11, 2020

Summary

This study introduces a novel hybrid model for robots to recognize human emotions in context, outperforming existing methods. This approach enhances cognitive assistance services in ubiquitous environments.

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Area of Science:

Robotics
Artificial Intelligence
Cognitive Science

Background:

Ubiquitous robots require advanced emotion recognition for effective human interaction.
Current data-driven methods struggle with contextual emotion recognition.
Sophisticated approaches are needed for recognizing human emotions, sentiments, affects, and moods.

Purpose of the Study:

To propose a hybrid model for emotion contextual recognition in ubiquitous environments.
To enhance cognitive assistance services through improved emotion recognition capabilities.
To address the limitations of existing data-driven pattern recognition techniques.

Main Methods:

A hybrid-level fusion model combining a multilayer perceptron (MLP) neural network and possibilistic logic.
An expressive emotional knowledge representation and reasoning model utilizing the emotion upper ontology (EmUO) and the n-ary ontology of events HTemp (NKRL language).
Validation using a YouTube dataset and a real-world smart devices showroom scenario.

Main Results:

The proposed multimodal emotion recognition model demonstrated superior performance compared to all baseline models.
Experiments confirmed the model's effectiveness in contextual emotion recognition and management.
Successful application in a real-world scenario for cognitive assistance services.

Conclusions:

The hybrid model offers a robust solution for emotion contextual recognition in ubiquitous robotics.
The approach facilitates the development of effective emotion-based cognitive assistance services.
This research advances the field of human-robot interaction through enhanced emotional intelligence.