In this research, we present a motion-adaptive duty-cycling approach to estimate orientation using inertial sensors.
We investigated how a proportional forward-controller could be used to adjust the duty-cycle of inertial sensing
units (IMU) and the orientation estimation update rate of an extended Kalman filter (EKF).
As an input for the forward-controller, we used the sensitive gyroscope, which responds quickly to rotational motion.
In sample data recordings and a simulated daily life dataset from a wrist-worn IMU, we showed 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 showed that duty-cycles of 50% are common during low-wrist rotation activities, such as reading and typing, while orientation error is below 1°.
This research is an initial step towards power-efficient algorithms which could increase the recording duration of IMUs when orientation estimates need to be calculated in real-time on the sensor node.
Work with us
For this project, we aim to investigate different controller-strategies and their effects on the orientation estimation. Additional sensor-fusion algorithms for the orientation estimation using different orientation representation could be investigated.
"Motion-adaptive duty-cycling to estimate orientation using inertial sensors", Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops, IEEE, 2014.,