<![CDATA[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.]]>
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