AGUMENTED DATASET FOR RESPIRATORY MOVMENT PREDICTION
This is the augmentation dataset used in the paper named "LSTformer: Long Short-term Transformer for Real Time Respiratory
Prediction". We made an augmentation dataset utilizing an RGB-D camera to collect motion signals in a breathing simulator phantom device. It is worth noticing that the movement of the simulator is driven by the clinical patient’s respiration, which is from a public dataset (https://signals.rob.uni-luebeck.de/index.php). Other details can be seen in our previous work : H. Peng, L. Deng, Z. Xia, Y. Xie, and J. Xiong, “Unmarked external breathing motion tracking based on b-spline elastic registration,” in International Conference on Intelligent Robotics and Applications. Springer, 2021, pp. 71–81.
To increase the diversity of the public dataset and improve the generalizability of the trained model, we obtained the augmented dataset via a breathing simulator. we trained our LSTformer model by randomly picking 97 training data segments (90% dataset). The rest of the 9 data segments (10% dataset) are for testing, and each segment contains 7500 points of respiratory motion.