Gesture recognition from magnetic field measurements using a bank of linear state space models and local likelihood filtering

Nour Zalmai; Christian Kaeslin; Lukas Bruderer; Sarah Neff; Hans-Andrea Loeliger

ICASSP 2015accepted0 citations

Gesture recognition from magnetic field measurements using a bank of linear state space models and local likelihood filtering

Nour Zalmai, Christian Kaeslin, Lukas Bruderer, Sarah Neff, Hans-Andrea Loeliger

Abstract

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.

BibTeX

@inproceedings{icassp2015_gesturerecogniti,
  title = {Gesture recognition from magnetic field measurements using a bank of linear state space models and local likelihood filtering},
  author = {Nour Zalmai and Christian Kaeslin and Lukas Bruderer and Sarah Neff and Hans-Andrea Loeliger},
  booktitle = {ICASSP 2015},
  year = {2015}
}