<![CDATA[The Jumping Jack Counter is an image processing-based application developed to automatically count the number of jumping jack movements in exercise videos. This study aims to implement the YOLOv11 model to detect and count jumping jack movements by analyzing body posture. YOLOv11 is utilized to identify body positions categorized into two main classes: "open" (arms and legs spread apart) and "closed" (arms and legs together). The dataset consists of 15,000 video frames collected from various exercise videos, with research stages including data collection, data labeling, preprocessing, model training, and testing. The results demonstrate that YOLOv11 achieves a 92% accuracy rate in counting jumping jack movements. These findings are expected to assist coaches and users in monitoring physical exercise in real-time, thereby enhancing training effectiveness. The majority of movement detections (78%) were for the open position, followed by the closed position (20%), with 2% detection errors attributed to lighting variations or camera angles. [1].]]>
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