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As the control input of the virtual reality flight simulator, precise head tracking with no time-delay is essential for user immersion and training effect. We present the results of applying a modified Kalman filter to improve head orientation prediction. A piecewise constant acceleration approach with an electromyography based input estimation is suggested. Head angular acceleration is estimated by a trained feed-forward neural network and fed into the modified Kalman filter. Experimental results show that the proposed method tracks head motion precisely under both smooth and abrupt motion conditions.

Improving Head Orientation Prediction with Electromyography Signals in a Virtual Reality Flight Simulator