面向动作捕捉的非线性时间序列预测方法研究

In this paper, we study the nonlinear time series prediction method for action capture. A prediction method based on the capture data is studied and implemented by analyzing human motion data to solve the data loss and correction problem caused by sensor failure. Based on this research purpose, the simulation experiment assumes that a sensor in the sequence of actions fails, then uses eight kinds of machine learning methods, and evaluates them with six indexes. The prediction results of different methods are compared and the predicted motions are visualized. Through the experiments, data prediction accuracy by random forest, decision tree, nearest neighbor (KNN) method can reach more than 90%. Thus, the nonlinear time series prediction method for motion capture can accurately reconstruct the action.