Gated Variable Selection Neural Network for Multimodal Sleep Quality Assessment

抄録

Sleep quality can be affected by several factors, such as sleep environment, lifestyles and so on. Existing sleep quality evaluation methods did not consider the impact of these factors. This research proposed a novel deep learning architecture with multiple-factors for sound-based sleep quality assessment. Utilizing sleep sound for sleep quality evaluation is low-cost and contactless, also, sound data can reflect several physical behaviors such as snore, cough and body movements, which are important when human experts manually evaluate sleep quality. This research utilized VAE-LSTM to learn sleep patterns in sleep sound and applied Gated Variable Selection Network (GVSN) to select useful information in factors. We recorded whole night sleep sounds of more than 100 subjects by microphone at home and collected questionnaires for experiment. The results show that the proposed method can perform accurate sleep quality prediction as well as factor importance analysis.