Using Artificial Neural Networks for Prediction Of Dynamic Human Motion
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چکیده
Researchers in robotics and other human-related fields have been studying human motion behaviors to understand and mimic them in humanoid motion prediction, obstacle avoidance, and ergonomic studies. Human motion, however, is not an easy system or kinematic to study when it includes highly complex relationships between factor—such as human anthropometry and speed and the output motion profile for human degrees of freedom (DOF)—involved in the task to be accomplished. Artificial neural network (ANN) is a method that has been introduced to analyze motion prediction problems because of its power in studying high-dimensional problems and predicting future system behaviors. In this study we used a general regression neural network (GRNN) to predict the human walking forward task as an example of ANN’s ability to predict human performance. The results showed that the GRNN was able to predict the motion realistically, accurately, and by a fraction of a second. This study shows that ANN has great potential to be widely used in task-based prediction of dynamic human motion. The novelty of this work is demonstrated by using ANN to predict human performance by studying motion prediction as an example. This will lead to an understanding of what drives human task performance.
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تاریخ انتشار 2012