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Corruption of photopleythsmograms by motion artifacts has been a serious obstacle to the reliable use of pulse oximeters for real-time, continuous state-of-health monitoring. In this work, we propose a motion artifact reduction methodology that is effective even in the case of severe subject movement. The methodology involves an enhanced preprocessing unit consisting of a motion detection unit (MDU), period estimation unit, and a Fourier series reconstruction unit. The MDU aids in identifying clean data frames versus those corrupted with motion artifacts. The period detection unit is used to determine the fundamental frequency of a corrupt frame. The Fourier series reconstruction unit reconstructs the final preprocessed signal. The reconstruction process primarily utilizes the spectrum variability of the pulse waveform. Preprocessed data are then fed to a magnitude- based frequency domain independent component analysis (FD-ICA) unit. This helps reduce motion artifacts present at the frequency components chosen for reconstruction. Experimental results are presented to demonstrate the effectiveness of the proposed motion artifact reduction method. The efficacy of the technique is compared with time domain ICA and complex frequency domain ICA methods.