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Gesture recognition from magnetic field measurements using a bank of linear state space models and local likelihood filtering
2015
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Detecting and inferring the trajectory of a moving magnet from magnetic field measurements is a challenge due to a wide range of time scales and amplitudes of the recorded signals and limited computational power of devices embedding a magnetometer. In this paper, we model the magnetic field measurements using a bank of autonomous linear state space models and provide an efficient algorithm based on local likelihood filtering for reliably detecting and inferring the gesture causing the magnetic field variations.
doi:10.1109/icassp.2015.7178435
dblp:conf/icassp/ZalmaiKBNL15
fatcat:mn3lfpxltrdfdok4vmyy2hnj3a