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With the growing popularity of rowing machine exercises, it is vital to create effective risk-avoidance solutions for injuries caused by improper rowing techniques in non-instructional circumstances. We built a two-stage system. In step I, each frame of the sequence image is then fed into the OpenPose module so that the coordinates of the key points can be extracted. The trajectories of the knees and elbows are crucial indicators for assessing the conformity of a rower’s actions. We subsequently computed the angle situation utilizing the knee and elbow angle data. In phase II, the one-dimensional angle sequence combination is inputted into a one-dimensional Convolutional Neural Network (1D CNN) to recognize whether the movement is standard. We incorporated a CoT attention module to enhance the classification network’s feature extraction stage. This addition results in highly condensed and information-rich feature representations. In addition, we evaluated four distinct attention mechanism methods for their performance on this test. We collected 665 correct and 490 erroneous rowing action sequences in all. Each sequence has 200 angle data. 75% of each dataset is randomly assigned for training purposes. The remaining 25% is designated as test data. The results of the trial showed an accuracy of 96.65%. It was demonstrated that it is feasible to use a real-time AI method to detect improper actions on a rowing machine through monitoring.