Temporal Conditions Suitable for Predicting Human Motion in Walking
Temporal Conditions Suitable for Predicting Human Motion in Walking
复制标题
适合预测人体行走运动的时间条件
DOI：
10.1109/smc.2019.8913941
复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
H. Shinoda
中科院分区：
文献类型：
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作者：
Takafumi Kurai;Yutaro Shioi;Yasutoshi Makino;H. Shinoda
本文阐明了用神经网络预测人类行走的时间条件。为了研究人类行走与预测之间的关系，我们通过两种不同类型的预测系统分析了人类的行走运动。一个分析了预测的身体关节的位置误差，以检查行走时哪个时刻的运动影响预测精度。因此，误差在系统使用Toe-Off （TO）信息进行预测时达到峰值。另一种分析使用三元分类来预测行走行为的突然变化：左转、右转或停止。通过将时间数据划分为较短的时间段，我们可以检查哪个阶段包含前进方向的本质。因此，在TO附近的阶段，正确率很高。从这些结果可以看出，由于to时刻是确定运动方向的时间，因此可以利用to时刻附近的骨骼数据来提高步态预测的准确性。
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.