| Summary: | 碩士 === 長庚大學 === 電機工程研究所 === 92 === Effective methods based on Kalman filter for noise reduction of human body motion signals are introduced in this research.
Gyroscopes and accelerometers are commonly used for motion signals measurement. However, the outputs of the sensors consist with noise. For gyroscopes, the output is affected not only by the noise, but also the DC drift component. And the error accumulates to a serious extent, which is not negligible. Accelerometers are not suitable for high speed or a vibration measurement for noise contamination of noise frequency. Hence, noise reduction is an important issue in human motion body measurement and necessary signal processing.
Based on the Kalman filter theory, we design an algorithm for noise reduction. We treat the covariance Q and R of the iterative equations as parameters and confine the parameters. This method is capable to reduce the nuisance of the noise for improving the signal extraction at noisy environment. We can choose the Kalman gain directly for filtering, and parallel a bank of Kalman filters with different chosen parameters for filtering. The proposed methods are capable to reduce the noise artifact of measuring human motion signals.
Experiments and MTALAB programming are demonstrated that the proposed methods are effective in noise reduction for human body motions measurement and significant signal extraction.
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