Motion-adaptive duty-cycling to estimate orientation using inertial sensors. Derungs, A.; Lin, H.; Harms, H.; and Amft, O. In ACOMORE 2014: IEEE International Conference on Pervasive Computing and Communications Workshops, of PerCom Workshops, pages 47--54, 2014. IEEE. 1st Symposium on Activity and Context Modeling and Recognition
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We present a motion-adaptive duty-cycling approach to estimate orientation using inertial sensors. In particular, we deploy a proportional forward-controller to adjust the duty-cycle of inertial sensing units~(IMU) and the orientation estimation update rate of an extended Kalman filter~(EKF). In sample data recordings and a simulated daily life dataset from a wrist-worn IMU, we show that our motion-adaptive approach incurs substantially lower errors that a static duty-cycling approach. During phases with low or no rotation motion, as it is often occurring in daily activities, our approach can dynamically reduce the IMU operation to 20% of the regular rate. Results show that duty-cycles of 50% are common during low-wrist rotation activities, such as reading and typing, while orientation error is below 1$\degree$. We further show the power saving benefits of our approach in a case study of the ETHOS IMU device.
@InProceedings{Derungs2014-P_ACOMORE,
  Title                    = {Motion-adaptive duty-cycling to estimate orientation using inertial sensors},
  Author                   = {Adrian Derungs and Han Lin and Holger Harms and Oliver Amft},
  Booktitle                = {ACOMORE 2014: IEEE International Conference on Pervasive Computing and Communications Workshops},
  Year                     = {2014},
  Note                     = {1st Symposium on Activity and Context Modeling and Recognition},
  Pages                    = {47--54},
  Publisher                = {IEEE},
  Series                   = {PerCom Workshops},

  Abstract                 = {We present a motion-adaptive duty-cycling approach to estimate orientation using inertial sensors. In particular, we deploy a proportional forward-controller to adjust the duty-cycle of inertial sensing units~(IMU) and the orientation estimation update rate of an extended Kalman filter~(EKF). In sample data recordings and a simulated daily life dataset from a wrist-worn IMU, we show that our motion-adaptive approach incurs substantially lower errors that a static duty-cycling approach. During phases with low or no rotation motion, as it is often occurring in daily activities, our approach can dynamically reduce the IMU operation to 20\% of the regular rate. Results show that duty-cycles of 50\% are common during low-wrist rotation activities, such as reading and typing, while orientation error is below 1$\degree$. We further show the power saving benefits of our approach in a case study of the ETHOS IMU device.},
  Doi                      = {10.1109/PerComW.2014.6815163},
  File                     = {Derungs2014-P_ACOMORE.pdf:Derungs2014-P_ACOMORE.pdf:PDF},
  Owner                    = {oamft},
  Timestamp                = {2013/12/24}
}
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