| Summary: | 碩士 === 國立中正大學 === 資訊工程研究所 === 105 === Empirical mode decomposition (EMD) is a proven technique to decompose breathing where from noisy signal recorded in severe environments. The breathing signal locates in arbitrary intrinsic mode functions (IMF) from EMD and selecting appropriate IMF to compose the breathing signal is critical for EMD-based breathing signal extraction. Multivariate EMD (MEMD) has been proposed to decompose 3-axis accelerometer signal into 3-dimension IMFs, the angle of which cannot be generated from human breathing is utilized as the feature to select IMF. However, MEMD needs huge computations and is not suitable for embedded implementation. This thesis proposes to use detrended fluctuation analysis (DFA) to select IMF from EMD results on 1-axis accelerometer signal. In our experiments, the accuracy on driver’s breathing extraction is comparable to that based on MEMD. In addition, stream DFA is proposed to reduce 54% computations, which makes the proposed breathing extraction with 1-axis accelerometer much more suitable for embedded systems.
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