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Corruption of photopleythysmograms (PPGs) 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 paper, we propose an automated, two-stage PPG data processing method to minimize the effects of motion artifacts. The technique is based on our prior work related to motion artifact detection (stage 1) [R. Krishnan, B. Natarajan, and S. Warren, ``Analysis and detection of motion artifacts in photoplethysmographic data using higher order statistics,'' in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP 2008), Las Vegas, Nevada, Apr. 2008, pp. 613-616] and motion artifact reduction (stage 2) [R. Krishnan, B. Natarajan, and S. Warren, ``Motion artifact reduction in photoplethysmography using magnitude-based frequency domain independent component analysis,'' in Proc. 17th Int. Conf. Comput. Commun. Network, St. Thomas, Virgin Islands, Aug. 2008, pp. 1-5]. Regarding stage 1, we present novel and consistent techniques to detect the presence of motion artifact in PPGs given higher order statistical information present in the data. We analyze these data in the time and frequency domains (FDs) and identify metrics to distinguish between clean and motion-corrupted data. A Neyman-Pearson detection rule is formulated for each of the metrics. Furthermore, by treating each of the metrics as observations from independent sensors, we employ hard fusion and soft fusion techniques presented in [Z. Chair and P. Varshney, ``Optimal data fusion in multiple sensor detection systems,'' IEEE Trans. Aerosp. Electron. Syst., AES, vol. 1, no. 1, pp. 98-101, Jan. 1986] and [C. C. Lee and J. J. Chao, ``Optimum local decision space partitioning for distributed detection,'' IEEE Trans. Aerosp. Electron. Syst., AES, vol. 25, no. 7, pp. 536-544, Jul. 1989], respectively, in order to fuse individual decisions into a global system decision. For stage two, we propo- - se a motion artifact reduction method that is effective even in the presence of severe subject movement. The approach involves an enhanced preprocessing unit consisting of a motion detection unit (MDU, developed in this paper), period estimation unit, and Fourier series reconstruction unit. The MDU identifies clean data frames versus those corrupted with motion artifacts. The period estimation unit determines the fundamental frequency of a corrupt frame. The Fourier series reconstruction unit reconstructs the final preprocessed signal by utilizing the spectrum variability of the pulse waveform. Preprocessed data are then fed to a magnitude-based FD independent component analysis unit. This helps reduce motion artifacts present at the frequencies of the reconstruction components. Experimental results are presented to demonstrate the efficacy of the overall motion artifact reduction method.