Enhancing Motoric Impulsivity Detection in Children through Deep Learning and Body Keypoint Recognition

Abstract

Quantifying motoric impulsivity in pediatric settings is crucial for safeguarding children and for devising effective intervention strategies. Existing quantitative techniques, such as accelerometry, have been utilized to assess it, but they often prove insufficient for accurately differentiating impulsive movements from regular ones. Conventional assessment methods are frequently used and rely on subjective assessments, which hinders the accurate characterization of impulsive behavior. To address this research gap, our study introduced an innovative objective approach using computer vision and deep learning techniques. We utilized MediaPipe to track precise body movement data from a child. The data were then analyzed using a Bidirectional Long Short-Term Memory (Bi-LSTM) network to process sequential information and recognize patterns indicative of impulsivity. Our approach successfully distinguished impulsive movements, marked by rapid changes in position and inconsistent movement velocities, from typical behavioral patterns with an accuracy rate of 98.21%. This research demonstrates the effectiveness of combining computer vision and deep learning to measure motoric impulsivity more precisely and impartially than prevailing qualitative techniques. Our model quantifies behaviors, enabling the development of improved safety protocols and targeted interventions in educational and recreational settings. This research has broader implications, suggesting a framework for future studies on pediatric motion analysis and behavioral assessment.