Temporal Conditions Suitable for Predicting Human Motion in Walking

In this paper, we clarified the temporal condition in predicting human walking through a neural network. In order to examine the relationship between human walking and prediction, we analyzed the walking motion through two different types of prediction system. One analyzed the positional error of predicted body joints to examine which moment's motion while walking influences the prediction accuracy. As a result, the error peaked around the time when the system uses the information of Toe-Off (TO) for prediction. The other analysis uses the ternary classification which predicts the sudden change of walking behaviors: turn left, turn right or stop. By dividing the temporal data into a shorter period, we examine which phase contains the essence of the proceeding direction. As a result, the correct answer rate was high in the phase around TO. From these results, it is possible to improve the accuracy of gait prediction by using the skeleton data around TO moment since the TO is the time to determine the moving direction.