Design and Implementation of Machine Learning-based Assisted Sleep System
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Jianhao Yang, Yike Xu, Shaocong Guo, Huawei Zhang, Shiyuan Liu, and Fanfan Zheng
With the acceleration of the rhythm of life in today's society, people's life pressure is increasing day by day, and the quality of sleep is also getting lower and lower. In order to help people improve sleep quality effectively, an assistant sleep system based on machine learning is designed. The system consists of three parts: wearing terminal, cloud platform and APP. The wearing terminal collects EEG signal and assists the sleep of the users; the cloud platform runs the EEG processing algorithm based on wavelet transform and the sleep staging algorithm based on convolutional neural network to get the real-time sleep state; APP performs user feature information input, wake-up time setting and sleep status checking. This design combines signal processing algorithm and machine learning to realize an intelligent and effective assistant sleep system. The experimental results show that the system has high accuracy for sleep staging of users.
Signal and information processing, EEG signal, Wavelet transform, Convolutional neural network, Assisted sleep, Sleep staging