Arm Movement Prediction Using Neural Networks
Whether interacting with a collaborative virtual environment, or CVE, locally or one networked across the Internet, any delay in the system can lead to a reduced sense of immersion. Input sensor delay and network delay are two common problems in CVE design that can be overcome with the application of prediction algorithms to the system. The purpose of this experiment was to assess the quality of feed forward back propagation neural networks in predicting natural avatar arm movement typically used in a CVE. In addition the experiment attempts to find the bounds for precise neural network prediction. The results show many different combinations of back propagation neural network topologies are capable of predicting up to 400 ms of human arm movements relatively accurately.