@conference {Derungs2014-P_ACOMORE, title = {Motion-adaptive Duty-cycling to Estimate Orientation using Inertial Sensors}, booktitle = {ACOMORE 2014: IEEE International Conference on Pervasive Computing and Communications Workshops}, series = {PerCom Workshops}, year = {2014}, note = {1st Symposium on Activity and Context Modeling and Recognition}, pages = {47{\textendash}54}, publisher = {IEEE}, organization = {IEEE}, 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}, author = {Adrian Derungs and Han Lin and Holger Harms and Oliver Amft} }