IdeaX

Incomplete annotation of motion information often leads to issues such as information loss and acceleration variability during pattern recognition. To address this, this study proposes a method for applying acceleration sensors in weakly-labeled motion pattern recognition. By measuring the acceleration of moving targets, an information acquisition platform and sensor network were constructed to collect kinematic data. The incomplete motion information is integrated into a weakly-labeled dataset and complemented using a semantic neighborhood learning algorithm. From the completed dataset, weakly-labeled data features are extracted, and the most relevant statistical features are selected and ranked by importance. These features are then used as input to a decision tree classifier for motion pattern recognition. Simulation results demonstrate that the proposed method achieves a recognition time of less than 3.5 seconds, a confidence level above 90%, and high recognition accuracy.