<![CDATA[Exercise provides significant benefits for physical health, and weightlifting has become increasingly popular among fitness enthusiasts. However, improper lifting techniques often lead to injuries, discouraging beginners and affecting long-term training consistency. To address this issue, this study proposes a deep learning approach that automatically evaluates weightlifting form through movement classification. The proposed method integrates the YOLO11n-pose algorithm for detecting keypoints from exercise video recordings and the Long Short-Term Memory (LSTM) network for classifying movement types and determining the correctness of form execution. The model achieved a mean average precision of 88.8% using side-view recordings of single- repetition weightlifting exercises. YOLO11n-pose extracts the coordinates of body keypoints, which are converted into joint angle data and analyzed over time using LSTM to identify movement quality based on expert-validated training data. The trained model was implemented into an iOS application called KorForm, developed using FastAPI, to provide real-time feedback for users. The results demonstrate that combining YOLO11n-pose and LSTM effectively supports weightlifting form evaluation and offers a practical solution for promoting safer and more consistent exercise habits.]]>
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