Sleep Behavior Detection Based on Pseudo-3D Convolutional Neural Network and Attention Mechanism

Abstract

Good sleep is very important for everyone to protect physical and mental health. People's sleep behavior at night reflects their sleep status. In this work, we propose a method to detect people's sleep behavior at night by adopting Pseudo-3D (P3D) convolution neural network with attention mechanism.In particular, we propose a new structure, which integrates Squeeze-and-Excitation (SE) blocks into P3D blocks, named P3D-Attention. For the input video, we use P3D blocks to extract spatial-temporal features, and use SE blocks to pay more attentions to the important channel features. The proposed network is tested on the Sleep Action (SA) dataset, which consists of five different actions, namely turn over, get up, fall off bed, play mobile phone, and normal sleep. Experimental results show that the proposed network achieves reasonably good detection results, and the accuracy rate on the test set can reach 90.67%. Compared with 3D convolutional neural networks (C3D), our proposed network can increase the accuracy by about 6% with only 1/6 model parameter size, and achieves an average prediction speed about 1.75 item/s. Compared with the residual spatiotemporal convolution network (R(2+1)D), our proposed network can increase the accuracy rate by about 1.5% with less than 1/2 model parameter size.

Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection

“…Song and other researchers constructed convolutional neural networks to classify sleep stages using single-channel electrocardiogram signals (Song et al, 2016;Sors et al, 2018;Wang et al, 2019;Eldele et al, 2021;Haghayegh et al, 2023). Guo et al (2022) proposed a pseudo-3D convolutional neural network method to detect people's nocturnal sleep behavior, with an accuracy of 90.67% on the test set. du- Yan et al (2022) used convolutional neural networks to analyze sleep stages using heart rate variability.…”