Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture.
ICML(2017)
摘要
We focus on predicting sleep stages from radio measurements without any attached sensors on subjects. We introduce a new predictive model that combines convolutional and recurrent neural networks to extract sleep-specific subject-invariant features from RF signals and capture the temporal progression of sleep. A key innovation underlying our approach is a modified adversarial training regime that discards extraneous information specific to individuals or measurement conditions, while retaining all information relevant to the predictive task. We analyze our game theoretic setup and empirically demonstrate that our model achieves significant improvements over state-of-the-art solutions.
更多查看译文
关键词
Sleep Stages,Artificial neural network,Pattern recognition,Computer science,Artificial intelligence,Observation period,Observation time
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络