|M.Sc Student||Polak Simon|
|Subject||Head Motion Anticipation for Virtual-Environment|
Applications Using Kinematics and EMG Energy
|Department||Department of Computer Science||Supervisor||Professor Emeritus Yoram Baram|
Real-time human-computer interaction plays an important role in Virtual-Environment (VE) applications. Such interaction can be improved by detecting and reacting to the user’s head motion. Today’s VE systems use head-mounted inertial sensors to update and spatially stabilize the image displayed to a user through a head-mounted display. This approach causes latencies in the VE system’s reaction to the head motion. Since motion can only be detected after it has already occurred, latencies in the stabilization scheme can only be reduced, but never eliminated. Such latencies slow down manual control, cause inaccuracies in matching real and virtual objects through a half-transparent display, and, reduce the sense of presence. This work presents novel methods for reducing VE latencies by anticipating future head motion based on electromyographic (EMG) signals originating from the major neck muscles and head kinematics. Features extracted from the EMG signals are used to train a feed-forward neural network in mapping EMG data, given present head kinematics, into future head motion. The trained network is then used in real-time for head motion anticipation which gives the VE system the time advantage necessary to compensate for the inherent latencies. The main contribution of this work is the use of the energy of low-pass filtered EMG signals as the key input information and the head acceleration as the key output information of the anticipation system, which results in improved performance compared to previous work, using features of the differential EMG signals as the input information and the head angular velocity as the output.