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Artificial Intelligence-Based Mobile Application for Emotion Sensing for Children Through Art.

Nashva Ali¹, Alaa Abdrazaq¹, Zubair Shah¹

¹Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.

Studies in Health Technology and Informatics

|June 8, 2022

Summary

This study introduces an artificial intelligence (A.I.) app to interpret children's emotions from drawings. The emotion sensing recognition app (ESRA) achieved 55-79% accuracy in classifying drawings as positive or negative.

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Area of Science:

Child Psychology
Artificial Intelligence
Machine Learning

Background:

Understanding children's emotions is crucial for development and well-being.
Traditional methods of emotion assessment can be subjective and time-consuming.
Digital tools offer potential for objective and efficient emotion analysis.

Purpose of the Study:

To develop an artificial intelligence (A.I.) based Emotion Sensing Recognition App (ESRA).
To assist parents and teachers in understanding children's emotions through drawing analysis.
To evaluate the performance of a deep learning model for emotion classification from drawings.

Main Methods:

Development of the Emotion Sensing Recognition App (ESRA) utilizing the Fastai library in Python.
Training a deep learning model on two distinct datasets.
Conducting four experiments to assess model performance.
Classifying children's drawings into positive or negative emotional categories.

Main Results:

The deep learning model demonstrated varying accuracy levels across four experimental conditions.
Model accuracy ranged from 55% to 79% in classifying emotions from children's drawings.
The ESRA successfully processed and analyzed visual emotional cues in drawings.

Conclusions:

The developed A.I. app shows promise in aiding the interpretation of children's emotions via their artwork.
Further research and model refinement could enhance the accuracy and applicability of emotion sensing technology.
The study highlights the potential of A.I. in supporting child emotional development and assessment.