Sleep quality monitoring method based on smartphone

A sleep quality, smartphone technology, used in diagnostic recording/measurement, medical science, sensors, etc.

Inactive Publication Date: 2018-09-14
3 Cites 5 Cited by

AI-Extracted Technical Summary

Problems solved by technology

[0005] Aiming at the defects existing in the existing sleep monitoring method, t...
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The invention discloses a sleep quality monitoring method based on a smartphone and belongs to the technical field of mobile sensing of smart phones. Firstly, a sensor of the phone is utilized to acquire sleep-related physiological characteristic signals in the sleeping process, then characteristic extraction is conducted on the physiological characteristic signals, then a fuzzy logic theoreticalmethod is utilized to map corresponding characteristics into corresponding sleep states, and finally sleep quality score is given according to age and environment lighting information of sleepers. Bycomparing 20 groups of sleep data, the judgement accuracy of rapid-eye-movement sleep (REM) can be up to 75.3%, the judgement accuracy of a light sleep stage is up to 81.2%, and the judgement accuracyof a deep sleep state is up to 78.8%. It can be found that the judgement accuracy of light sleep is the highest and the judgement accuracy of REM is the lowest because that brain neuron activities atthe REM stage and a sober stage are similar, the two stages cannot be effectively distinguished according to limb movements and breathing frequency and accordingly erroneous judgement is likely caused.

Application Domain

Respiratory organ evaluationSensors

Technology Topic

BreathingPhases of clinical research +13


  • Sleep quality monitoring method based on smartphone
  • Sleep quality monitoring method based on smartphone
  • Sleep quality monitoring method based on smartphone


  • Experimental program(1)

Example Embodiment

[0064] In the present invention, in order to ensure the accuracy of the test results, a total of 60 days of sleep data of 20 volunteers were collected, and each volunteer collected three days of sleep data. To ensure the randomness of sampling, the age distribution of volunteers ranges from 20 to 60 years old and is divided into 10 males and 10 females. Each volunteer wears a Jawbone UP bracelet and a smartphone with a sleep monitoring system when taking measurements. The volunteer turns on the sleep monitoring function on the bracelet and smartphone at the same time during sleep. The bracelet is worn on the wrist and the smartphone Placed next to the pillow, the system automatically measures the sleep state, and compares the accuracy of the system's measurement by comparing the two.
[0065] The sleep state test is divided into two levels of comparison, the first is the comparison of the time measurement of different sleep stages, and the second is the comparison of the total sleep duration measurement time. Randomly select one day's sleep data for each volunteer from the measurement data set, and get the following Image 6 Comparison chart of sleep duration shown.
[0066] From the experimental results, it can be seen that the average error of the total sleep duration measured by the Jawbone UP bracelet and the smart phone is within 50 minutes, accounting for 12.3% of the average sleep duration, that is, the average monitoring accuracy compared with the bracelet is 87.7%. The experimental results show that this system has high accuracy compared with bracelet products in monitoring total sleep time.
[0067] Sleep monitoring is not only about recording the total length of sleep, but also the time distribution of different sleep stages. Therefore, by analyzing and comparing the experimental data sets, we get Figure 7 The results of the test are based on the sleep state distribution measured by the bracelet as a reference standard.
[0068] By comparing 20 sets of sleep data, it can be obtained that the judgment accuracy rate of REM reaches 75.3%, the judgment accuracy rate of light sleep stage reaches 81.2%, and the judgment accuracy rate of deep sleep reaches 78.8%. It can be seen that the judgment accuracy rate of light sleep is the highest, and the judgment accuracy rate of rapid eye movement sleep (REM) is the lowest. This is because the brain neuron activity in the REM phase and the waking phase is similar, and the movement of the limbs and the breathing rate cannot be used for this. Effective screening in two stages can easily lead to misjudgment.


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