Neural Networks for Human Arm Movement Prediction in CVEs
Fred Stakem, Ghassan AlRegib · 2008
Whether interacting with a Collaborative Virtual Envi-ronment, or CVE, locally or one networked across the In-ternet, 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 predict-ing natural avatar arm movement used in a CVE. In addi-tion the experiment attempted to find the bounds for precise neural network prediction. The results show many different combinations of back propagation neural network topolo-gies are capable of predicting up to 400 ms of human arm movements relatively accurately. 1.