Detection and Classification of Teacher-Rated Children’s Activity Levels Using Millimeter-Wave Radar and Machine Learning: A Pilot Study in a Real Primary School Environment

Traditional assessments of children’s health and behavioral issues primarily rely on subjective evaluation by adult raters, which imposes major costs in time and human resource to the school system. This pilot study investigates the utilization of millimeter-wave radar coupled with machine learning for the objective and semi-automatic detection and classification of children’s activity levels, defined as restlessness, within a real classroom environment. Two objectives are pursued: confirming the feasibility of restlessness detection using millimeter-wave radar and applying standard machine learning method for restlessness classification. The experiment involves a nine-day observational study, using two radar systems to monitor the activities of 14 children in a primary school. Radar data analysis involves the extraction of distinctive features for restlessness detection and classification. Results indicate the successful detection of restlessness using millimeter-wave radar, demonstrating its potential to capture nuanced body movements in a privacy-protected manner. Machine learning models trained on radar data achieve a classification accuracy of 100%, outperforming other methods in terms of non-invasiveness, lack of body restraint, multi-target applications, and privacy protection. The study’s contributions extend to children, parents, and educational practitioners, emphasizing non-invasiveness, privacy protection, and evidence-based support. Despite limitations such as a short monitoring duration and a small sample size, this pilot study lays the foundation for future research in non-invasive restlessness detection using non-contact monitoring technologies. The integration of millimeter-wave radar and machine learning offers a promising avenue for efficient and ethical trait assessments in real-world educational environments, contributing to the advancement of child psychology and education. This work supports efforts for non-contact monitoring of children’s activity holding promise such as non-invasive, privacy protection, multi-targets, objective evaluation, and computer-aided screening.