ToddlerAct: A Toddler Action Recognition Dataset for Gross Motor Development Assessment

TL;DR

ToddlerAct is a new dataset of toddler gross motor actions designed to improve early childhood development assessment through action recognition, addressing the lack of age-specific data in existing datasets.

Contribution

The paper introduces ToddlerAct, a specialized dataset for toddler action recognition, and benchmarks multiple methods to advance research in early motor development assessment.

Findings

Domain-specific datasets improve accuracy in toddler motor assessment

State-of-the-art methods show varying performance on toddler actions

Dataset availability will facilitate future research in early childhood development

Abstract

Assessing gross motor development in toddlers is crucial for understanding their physical development and identifying potential developmental delays or disorders. However, existing datasets for action recognition primarily focus on adults, lacking the diversity and specificity required for accurate assessment in toddlers. In this paper, we present ToddlerAct, a toddler gross motor action recognition dataset, aiming to facilitate research in early childhood development. The dataset consists of video recordings capturing a variety of gross motor activities commonly observed in toddlers aged under three years old. We describe the data collection process, annotation methodology, and dataset characteristics. Furthermore, we benchmarked multiple state-of-the-art methods including image-based and skeleton-based action recognition methods on our datasets. Our findings highlight the importance of domain-specific datasets for accurate assessment of gross motor development in toddlers and lay the foundation for future research in this critical area. Our dataset will be available at https://github.com/ipl-uw/ToddlerAct.