关键词: 心冲击信号, 小波变换, 特征提取, 神经网络, 睡姿识别

Abstract: Studies show that sleep position can affect one’s sleep quality, and inappropriate position may potentially increase risks for multiple diseases. In this paper, a Ballistocardiogram（BCG） signal based sleeping position pattern recognition algorithm is presented. BCG signals from four different sleeping positions（supine, prone, left lateral and right lateral） are collected with a piezoelectric film sensor in a non-contact way. Eight characteristics are extracted from the preprocessed BCG signals after wavelet-based denoising, and the sleep position pattern is recognized using BP neural network and KNN. The experimental results show that recognition accuracy of the neural network algorithm and KNN are 93% and 84%, respectively. Therefore, BCG-based sleep position recognition with neural network has a potential to be widely used in sleep monitoring applications.

Key words: ballistocardiogram, wavelet transform, feature extraction, neural network, sleeping position recognition