scholarly journals Effectiveness of Variable-Gain Kalman Filter Based on Angle Error Calculated from Acceleration Signals in Lower Limb Angle Measurement with Inertial Sensors

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Yuta Teruyama ◽  
Takashi Watanabe
Keyword(s):  
Kalman Filter ◽  
Lower Limb ◽  
Measurement Accuracy ◽  
Inertial Sensors ◽  
Angle Measurement ◽  
Angle Error ◽  
Variable Gain ◽  
Improve Measurement ◽  
Kalman Gain ◽  
Acceleration Signals

The wearable sensor system developed by our group, which measured lower limb angles using Kalman-filtering-based method, was suggested to be useful in evaluation of gait function for rehabilitation support. However, it was expected to reduce variations of measurement errors. In this paper, a variable-Kalman-gain method based on angle error that was calculated from acceleration signals was proposed to improve measurement accuracy. The proposed method was tested comparing to fixed-gain Kalman filter and a variable-Kalman-gain method that was based on acceleration magnitude used in previous studies. First, in angle measurement in treadmill walking, the proposed method measured lower limb angles with the highest measurement accuracy and improved significantly foot inclination angle measurement, while it improved slightly shank and thigh inclination angles. The variable-gain method based on acceleration magnitude was not effective for our Kalman filter system. Then, in angle measurement of a rigid body model, it was shown that the proposed method had measurement accuracy similar to or higher than results seen in other studies that used markers of camera-based motion measurement system fixing on a rigid plate together with a sensor or on the sensor directly. The proposed method was found to be effective in angle measurement with inertial sensors.

Research on Data Fusion Technology of Body Posture Detection Based on Kalman Filter

2014 ◽  
Vol 668-669 ◽  
pp. 1003-1006 ◽  
Author(s):  
Xian Wei Wang ◽  
Fu Cheng Cao
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Inertial Sensors ◽  
Low Cost ◽  
Micro Electro Mechanical System ◽  
Angle Measurement ◽  
The Body ◽  
Body Posture ◽  
Posture Estimation ◽  
The Impact

This paper discusses the body posture detection problem using low cost Micro-Electro-Mechanical System (MEMS) inertial sensors, for which a complementary sensor fusion solution is proposed. Considering the impact from the noise and bias drifts, through Kalman filter to complete the multi-sensor information fusion, achieved an accurate attitude determination. The experimental results show that, after using Kalman filtering algorithm to fuse acceleration sensor and signal gyroscope, it can effectively eliminate the accumulative error and significantly better dynamic characteristics of attitude angle measurement, Improving the reliability and accuracy of body posture estimation.

Fuzzy adaptive Kalman filter for the drive system with an elastic coupling

2013 ◽  
Vol 62 (2) ◽  
pp. 251-265 ◽  
Author(s):  
Piotr J. Serkies ◽  
Krzysztof Szabat
Keyword(s):  
Kalman Filter ◽  
Control Structure ◽  
Experimental Tests ◽  
Drive System ◽  
Adaptive Kalman Filter ◽  
State Matrix ◽  
State Controller ◽  
Kalman Gain ◽  
Robust Adaptive ◽  
Fuzzy Adaptive

Abstract In the paper issues related to the design of a robust adaptive fuzzy estimator for a drive system with a flexible joint is presented. The proposed estimator ensures variable Kalman gain (based on the Mahalanobis distance) as well as the estimation of the system parameters (based on the fuzzy system). The obtained value of the time constant of the load machine is used to change the values in the system state matrix and to retune the parameters of the state controller. The proposed control structure (fuzzy Kalman filter and adaptive state controller) is investigated in simulation and experimental tests.

scholarly journals Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement

Signals ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 434-455
Author(s):  
Sujan Kumar Roy ◽  
Kuldip K. Paliwal
Keyword(s):  
Kalman Filter ◽  
Speech Enhancement ◽  
Linear Prediction ◽  
Real Life ◽  
Model Parameters ◽  
Noise Variance ◽  
Noisy Speech ◽  
Kalman Gain ◽  
Whitening Filter ◽  
Prediction Coefficient

Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. The existing methods propose a tuning of the biased Kalman gain, particularly in stationary noise conditions. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then, we construct a whitening filter (with its coefficients computed from the estimated noise) to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. We then construct the KF with the estimated parameters, where the robustness metric offsets the bias in KF gain during speech absence of noisy speech to that of the sensitivity metric during speech presence to achieve better noise reduction. The noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on the NOIZEUS corpus demonstrate that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

scholarly journals Modifying Kinect placement to improve upper limb joint angle measurement accuracy

2016 ◽  
Vol 29 (4) ◽  
pp. 465-473 ◽  
Author(s):  
Na Jin Seo ◽  
Mojtaba F. Fathi ◽  
Pilwon Hur ◽  
Vincent Crocher
Keyword(s):  
Upper Limb ◽  
Measurement Accuracy ◽  
Joint Angle ◽  
Angle Measurement

scholarly journals RESEARCH ON THE ACCURACY OF ANGLE MEASUREMENT / KAMPŲ MATAVIMO TIKSLUMO TYRIMAS

2012 ◽  
Vol 3 (6) ◽  
pp. 15-18 ◽  
Author(s):  
Artūras Prielaidas ◽  
Rimas Lazdinas
Keyword(s):  
Main Part ◽  
Measurement Accuracy ◽  
Angle Measurement ◽  
Rotary Disk

Rotary encoders are the main devices in industrial angle measurement. Accuracy is very important and is assured by the technology of manufacture. The main part (rotary disk) is under examination, and therefore a number of its characteristics are established and a comparison with the assembled encoder is presented. In conclusion, an error in the angle of the rotary disk makes a possibility of forecasting an error in the assembled encoder angle. Santrauka Nagrinėjamas limbų paklaidų matavimas, jų vertinimas, fotoelektrinių matavimų keitiklių paklaidų matavimas, bandoma nustatyti keitiklio paklaidų priklausomybę nuo limbo paklaidų. Pateikta limbų, keitiklių apžvalga, analizė, pagrindinės schemos. Atlikta limbų ir keitiklių paklaidų aproksimacija parametrinėmis funkcijomis. Apibendrinti visų matavimų rezultatai – kas būdinga paklaidų kreivėms, kokie dydžiai, jų aproksimacijos parametrinėmis funkcijomis rezultatai, formulės, analizė. Atlikti koreliacijos tarp limbo ir matavimo keitiklio paklaidų tyrimai.

scholarly journals The Design and Implementation of an Inertial GNSS Odometer Integrated Navigation System Based on a Federated Kalman Filter for High-Speed Railway Track Inspection

2021 ◽  
Vol 11 (11) ◽  
pp. 5244
Author(s):  
Xinchun Zhang ◽  
Ximin Cui ◽  
Bo Huang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Speed ◽  
Measurement Accuracy ◽  
Railway Track ◽  
Filter Method ◽  
Integrated Navigation ◽  
High Speed Railway ◽  
Track Geometry ◽  
Integrated Navigation System

The detection of track geometry parameters is essential for the safety of high-speed railway operation. To improve the accuracy and efficiency of the state detector of track geometry parameters, in this study we propose an inertial GNSS odometer integrated navigation system based on the federated Kalman, and a corresponding inertial track measurement system was also developed. This paper systematically introduces the construction process for the Kalman filter and data smoothing algorithm based on forward filtering and reverse smoothing. The engineering results show that the measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h. Thus, compared with the traditional Kalman filter method, the proposed design improved the measurement accuracy and met the requirements for the detection of geometric parameters of high-speed railway tracks.

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