Method for Prediction of Motion Based on Recursive Least Squares Method with Time Warp Parameter and its Application to Physical Therapy

We build an exercise therapy support system for children with disabilities that applies artificial intelligence technology. In this study, a 3DCG character shows a model body-building exercise, and at the same time provides feedback such as calling out to the trainee. At that time, to make the exercise therapy work more effectively, the trainee's movement is attempted to be corrected by notifying the trainee with a voice or other means before the trainee's movement deviates significantly from that of the 3DCG character. Since there is inevitably a delay between the movements of the 3DCG characters playing the role of the trainee and the trainer, it is necessary to predict this delay using time series analysis. The Recursive Least-Squares estimation: RLS method was used for this prediction method. In addition, the similarity of the movements of both companies was evaluated using the Dynamic Time Warping: DTW method, and the time warp calculated in this process was used as input for the RLS method. The results of the experiment confirmed that the predictions were made with sufficient accuracy and that when the degree of similarity was low, the 3DCG character playing the trainer's role spoke to them, leading to improvements in the trainees' movements.

@article{Arai2024,
  title     = {Method for Prediction of Motion Based on Recursive Least Squares Method with Time Warp Parameter and its Application to Physical Therapy},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Kosuke Eto and Mariko Oda},
  doi       = {10.14569/IJACSA.2024.0150714},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150714}
}