By Topic

Head motion anticipation for virtual-environment applications using kinematics and EMG energy

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Polak, S. ; Comput. Sci. Dept., Israel Inst. of Technol., Haifa ; Barniv, Y. ; Baram, Y.

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. 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 paper 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; it also reports results for anticipation of 17.5 and 35 ms. Features extracted from the EMG signals are used to train a 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 EMG energy and bounded head acceleration as the key input/output information, which results in improved performance compared to the previous work

Published in:

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on  (Volume:36 ,  Issue: 3 )