The existing sleep monitoring researches mainly focus on non-interfering monitoring methods for sleep quality, and lack research on active adjustment methods of sleep quality. The researches of mental state and sleep staging based on Heart Rate Variability (HRV) analysis focus on the acquisition of these two kinds of information, which needs people wearing professional medical equipment, and the researches lack the application and adjustment of the information. Music can be used as a non-pharmaceutical method to solve sleep problems, but existing music recommendation methods do not consider the differences in individual sleep and mental states. A music recommendation system according to mental stress and sleep state by mobile devices was proposed to solve above problems. Firstly, the photoplethysmography signals were collected by the watch to extract features and calculate the heart rate. Then, the collected signals were transmitted to the mobile phone via bluetooth, and these signals were used by the mobile phone to evaluate the person’s mental stress and sleep state to play the adjusted music. Finally, the music was recommended according to the sleep time per night of the individual. The experimental results show that after using the sleep music recommendation system, the total sleep time of users increases by 11.0%.

Continuous and accurate respiratory volume monitoring during sleep helps to infer the user’s sleep stage and provide clues about some chronic diseases. The existing works mainly focus on the detection and monitoring of respiratory frequency, and lack the means for continuous monitoring of respiratory volume. Therefore, a system named RF-SLEEP which uses commercial Radio Frequency IDentification (RFID) tags to wirelessly sense the respiratory volume during sleep was proposed. The phase value and timestamp data returned by the tag array attached to the chest surface was collected continuously by RF-SLEEP through the reader, and the displacement amounts of different points of the chest caused by breathing were calculated, then the model of relationship between the displacement amounts of different points of the chest and the respiratory volume was constructed by General Regression Neural Network (GRNN), so as to evaluate the respiratory volume of user during sleep. The errors in the calculation of chest displacement caused by the rollover of the user’s body during sleep were eliminated by RF-SLEEP through attaching the double reference tags to the user’s shoulders. The experimental results show that the average accuracy of RF-SLEEP for continuous monitoring of respiratory volume during sleep is 92.49% on average for different users.

Aiming at the characteristics of large volume, rapid generation and concept drift of current stream data, a stream data anomaly detection method based on Long Short-Term Memory (LSTM) network and sliding window was proposed. Firstly, the LSTM network was used for data prediction, and the difference between the predicted value and the actual value was calculated. For each datum, the appropriate sliding window was selected, and the distribution modeling was performed to all the differences in the sliding window interval, then the probability of data anomaly was calculated according to the probability density of each difference in the current distribution. The LSTM network was not only able to predict data, but also able to predict and learn at the same time, as well as update and adjust the network in real time to ensure the validity of the model. The use of sliding windows was able to make the allocation of abnormal scores more reasonable. Finally, the simulation data made on the basis of real data were used for experiment. The experimental results verify that the average Area Under Curve (AUC) value of the proposed method in low-noise environment is 0.187 and 0.05 higher than that of direct difference detection and Abnormal data Distribution Modeling (ADM) method, respectively.