Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial Sensors

Abstract

Position sensing with inertial sensors such as accelerometers and gyroscopes usually requires other aided sensors or prior knowledge of motion characteristics to remove position drift resulting from integration of acceleration or velocity so as to obtain accurate position estimation. A method based on analytical integration has previously been developed to obtain accurate position estimate of periodic or quasi-periodic motion from inertial sensors using prior knowledge of the motion but without using aided sensors. In this paper, a new method is proposed which employs linear filtering stage coupled with adaptive filtering stage to remove drift and attenuation. The prior knowledge of the motion the proposed method requires is only approximate band of frequencies of the motion. Existing adaptive filtering methods based on Fourier series such as weighted-frequency Fourier linear combiner (WFLC), and band-limited multiple Fourier linear combiner (BMFLC) are modified to combine with the proposed method. To validate and compare the performance of the proposed method with the method based on analytical integration, simulation study is performed using periodic signals as well as real physiological tremor data, and real-time experiments are conducted using an ADXL-203 accelerometer. Results demonstrate that the performance of the proposed method outperforms the existing analytical integration method.

Multi-step tremor prediction autoregressive (AR) model and Kalman filter (KF) for surgical robotic applications

“…For physiological tremor compensation using Itrem [12], 8 -12ms phase delay was identified due to hardware and software filtering between actual tremor and actuation input to cancel tremor, discussed in Results section. The presence of phase delay that is mainly due to hardware filters and software filters in the tremor compensation loop severely affects the accuracy of robotics based surgical instruments.…”

Drift-Free Position Estimation for Periodic Movements Using Inertial Units

“…Through the literature, different methods can be found to partially solve this problem: aided sensors or sensing systems data fusion [11]- [14], wavelet analysis [15], [16], Fourier-based filters [17]- [21], band-pass filtering [22], and polynomial data fitting [23]. Generally, they tend to use the aid of an externally referenced sensor or prior knowledge of the motion as well as complex linear and adaptive filtering or other data processing to estimate displacement from the acceleration signal.…”

“…High-accuracy position measurements from an optical system (Vicon Nexus 1.0) were used to validate the proposed method and to compare its performance with another drift-correction algorithm [17].…”

Dual-Axis Test Rig for Mems Tilt Sensors

“…As can be observed, the error of determining the roll did not exceed 0.23 and had a similar course within the whole range of the roll, owing to application of arc tangent-type Eq. (9). With regard to the respective catalog datasheet [23], error of the accelerometer indications resulting only from the noise would exceed 0.3, while the declared cross-axis sensitivity would increase it by another 1.8.…”

“…However, if the test rig is integrated using some standard components, the researchers often have not paid enough attention to the employed test rig and the applied methodology, as it is the case e.g. in [6][7][8][9], where the result were overestimated errors of the tested sensors, e.g. hysteresis evaluated by Ang et al in [8], being rather the hysteresis of the employed test rig itself.…”