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Motion-adaptive duty-cycling to estimate orientation using inertial sensors

Publication Type Conference Paper
Authors Adrian Derungs, Han Lin, Holger Harms, Oliver Amft
Title Motion-adaptive duty-cycling to estimate orientation using inertial sensors
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.
Date 2014
Proceedings Title Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops
Publisher IEEE
Pages 47–54
Series PerCom Workshops
DOI 10.1109/PerComW.2014.6815163
Extra 1st Symposium on Activity and Context Modeling and Recognition (ACOMORE)
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