In this paper, we propose an algorithm to remove movement noise in photoplethysmography (PPG) signals. To that end, we use the multipath diversity of PPG signals measured at different locations and the wavelet transform to detect movement noise in the signals. In experiments, when PPG signals have movement noise for 30% of the time, the proposed algorithm can reduce that noise to 5.18%.

In this paper, we propose a methods to eliminate PPG sensor noise resulted from user motion during measurement. Measured PPG signals require approperiate signal processing methods since various types of noises such as a motion noise by user movement and signal noises occurred from the change of measuring environments. This paper suggests a signal processing method that eliminates motion noises by measuring several PPG channels that are based on the stable patterns of the practical users. The PPG signals are measured by the two channels in this experiment. When the individual error rates are 20%, the proposed algorithm reduces the errors to 9.56%. ※ 본 논문은 2012년 울산대학교 연구비에 의하여 연구되었습니다.First Author：울산대학교 전기공학부,

In this paper, we propose an algorithm to remove movement noise from second derivative of photoplethysmography (SDPPG) signals. SDPPG is widely used in healthcare applications because of its easy and comfortable measurement. However, an SDPPG signal is vulnerable to movement, which degrades the signal. Degradation of SDPPG signal shapes can result in incorrect diagnosis. The proposed algorithm detects movement noise in a measurement signal using wavelet transform, and removes movement noise by selecting the best signal from among multiple signals measured at different locations. Experiment results show that the proposed algorithm outperforms the previous filter-based algorithm, and that movement noise with 30% time duration can be reduced by up to 70.89%.

In this paper, we propose an algorithm to detect movement noise in PPG(Photoplethysmography) measurements.Movement noise significantly deteriorate PPG signals in measurement, so that a movement noise detection algorithm is critical before using measured PPG signals for applications such as diagnosis. To detect movement noise, we apply wavelet transform to PPG signals instead of short-time Fourier transform and decide if the measured signlas include movement noise. To that end, we adaptively choose a wavelet, which is the most similar to the subject's PPG pattern. In the case when movement noise is intentionally added in the 20% and 30% of the total experiment time, our algorithm detects time-slots including movement and outperforms previous works.