A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

Abstract

The COVID-19 pandemic can be attributed as a main factor to accelerate the current digital transformation and to encourage innovation and technological adoption. Consequently, the care provided to our children, one of the significant aspects of life, needs to be adapted with the life’s changes. Children are our future and our most precious resources. They need our attention in all life domains including health, education, safety and social interaction. Nowadays, technologies have been incorporated with machine learning and it has been proven that they are more powerful, reliable and profitable. Machine learning methods have been applied by many children-related studies to generate predictive models for different applications. The efficacy of the generated models mainly relies on the constructed databases. This article carries out a comprehensive survey on available children’s databases constructed for machine-learning-based solutions with their methodologies, characteristics, challenges, and applications. First, it provides an overview of the available studies and classifies them based on their applications. Next, it defines a set of attributes and evaluates them while also shedding light on their pros and cons. The primary concerns related to collection, development and distribution of children’s databases are also discussed. This study can be considered as a guideline for researchers in multidisciplinary fields to construct reliable databases and to develop more advanced techniques.

Keywords

• Children’s databases
• speech and language analysis
• affect detection
• activity recognition
• pain assessment
• database construction