Multi-Sensor Data Fusion Analysis for Tai Chi Action Recognition

The continuous development of action recognition technology can capture the decomposition data of Tai Chi movements, provide precise assistance for learners to correct erroneous movements and enhance their interest in practicing Tai Chi. Inertial sensors and human skeletal models are used to collect motion data. Combined with visual sensors, the motion and trajectory of Tai Chi are processed to obtain the relevant coordinate system of the movement trajectory. Then, the inertial sensor and visual sensor are fused for data processing to standardize the human skeleton model, remove noise interference from the collected information, and improve the smoothness performance of movement trajectories, thereby segmenting and clustering Tai Chi movement trajectories. Finally, the support vector machine and dynamic time-warping algorithm are combined to identify and verify the trajectory of Tai Chi movements. According to the results, in the 25%, 50%, and 75% training sample proportions, the lowest recognition accuracy of the Qi Shi movements was 90.87%, 93.53%, and 98.08%, respectively. The optimal recognition accuracy and standard deviation of single nodes in binary classification were 98.48% and 0.47%, respectively. The best recognition accuracy and standard deviation for multi-joint points in binary classification were 99.77% and 0.16%, respectively. This proves the recognition advantages of binary classification and the superiority of data fusion analysis based on multiple sensors, providing a theoretical basis and technical reference for action recognition technology.

@article{Ouyang2024,
  title     = {Multi-Sensor Data Fusion Analysis for Tai Chi Action Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Jingying Ouyang and Jisheng Zhang and Yuxin Zhao and Changhuo Yang},
  doi       = {10.14569/IJACSA.2024.01511103},
  url       = {https://doi.org/10.14569/IJACSA.2024.01511103}
}