Real-Time Detection of Autistic Children's Activities Using YOLOv8 on Social Monitoring Robots - Prihatini, Ekawati; Muslimin, Selamat; Hadi, Kurnia (Indonesian Journal of Artificial Intelligence and Data Mining, 2025)
Children with autism spectrum disorder require special attention in both therapy and daily activity monitoring. One approach that can assist is the utilization of a Social Monitoring Robot (SMR) with the capability of automatic activity monitoring. This study aims to develop a real-time activity detection system for children with autism using the You Only Look Once version 8 (YOLOv8) algorithm on the SAR platform. The system is designed to recognize key activities such as eating, studying, and walking, through video input from a webcam processed by a Raspberry Pi. The recognition process is carried out by detecting bounding boxes and confidence scores for the child and their activities. The detection results are then visualized through a Human Machine Interface (HMI). Based on the testing, the system is capable of detecting and classifying children's activities with a fairly high level of reliability under real-world environmental conditions. These results indicate that the implementation of YOLOv8 in an SMR-based monitoring system has the potential to enhance supervision and intervention for children with autism in a more responsive and personalized manner.