| Summary: | How a person sleeps can tell a lot about the diseases that they might have. Most of these diseases go undetected because signs of these diseases e.g. snoring, teeth grinding etc. occur when they are still sleeping, and the wake- time symptoms of lack of sleep can be associated with other non-sleep related conditions. Early detection of such conditions can aid in their treatment. The purpose of this research is to develop a technique to unobtrusively monitor the user’s sleep so that conditions related to snoring can be detected using a smartphone.
An android application is developed to record the sleep audio and movement of the user while they are sleeping, using the sensors the smartphone comes equipped with. This data is then transferred to a server after pre-processing, and classified into different types of snores and noises which might have disturbed the user’s sleep. The user can then playback all the sound files that were detected as events during sleep and know when they occurred. In this way, the user will know if their snoring is problematic and they can consult a doctor for further analysis.
Visualisation of different sound events differ even though they recorded using the microphone of a smartphone. Therefore, they differ sufficiently to be classified by a trained classifying method. This shows how smartphones can be used to improve early diagnosis and hence improve healthcare.
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