Noise reduction of PPG signals using a particle filter for robust emotion recognition

In this paper, we address the problem of noise reduction of photoplethysmography (PPG) signals acquired from an PPG array sensor. The previous noise reduction approaches assumed that the noise sources are stationary. However, in real environments PPG signals often get corrupted by nonstationary movement noise. To reduce such noise, we propose to estimate the desired signal from corrupted signals by using a particle filter. In computer experiments using real PPG signals acquired from a wristwatch-type PPG array sensor, the proposed algorithm is shown to effectively reduce the movement noise and improve emotion recognition accuracy absolutely by 12.7 % and 10.9 % in the situations where users move arms and walk on a road, respectively, compared with the conventional normalized least-mean-square (NLMS)-based algorithm. The output signal-to-noise ratio (SNR) is also improved by 4.5 dB on average in the same situations.