Auto-encoder Based Data Clustering for Typical and Atypical Repetitive Child Hand Movement Pattern Identification

Abstract

This study is dedicated to the important task of identifying unique repetitive hand movement patterns in children, with the aim of facilitating early anomaly detection. The current body of literature lacks a comprehensive model capable of effectively discerning distinctive patterns in child repetitive hand movements. To address this gap, our innovative approach employs autoencoders to efficiently compress intricate data and extract latent features from a dataset with inherent limitations. By utilizing clustering techniques, we analyze these features to reveal distinct behaviors associated with child hand movements. Despite the challenges posed by binary annotated datasets, our model demonstrates outstanding performance in categorizing movements into four distinct types, thereby providing valuable insights into the intricate landscape of child hand movement patterns. Statistical assessments further underscore the superiority of our autoencoder, achieving a mean Bayesian value of 0.112, outperforming state-of-the-art algorithms in this domain. Subsequent in-depth analysis exposes notable inter-cluster patterns, elucidating transitions from typical to atypical behavior in child hand movements. This research constitutes a significant advancement in the field of child hand movement pattern analysis, offering a powerful and sophisticated tool for healthcare professionals and researchers alike. The automation capabilities embedded in our model empower these professionals to address childhood behavioral disorders more effectively and efficiently. In essence, our research not only contributes to the enhancement of early anomaly detection systems but also serves as a valuable resource for professionals engaged in child healthcare and behavioral research, facilitating a deeper understanding of these nuanced patterns.