Integrating Mobile Sensing and Machine Learning for Early Detection and Personalized Management of Chronic Diseases

Obesity, heart disease, diabetes, and depression have all been linked to insufficient sleep. Analysis of one's sleep habits using one's smartphone has gained popularity as a possible solution. In this paper, we provide a framework for analyzing sleep habits using mobile devices. Because of their widespread availability and extensive sensor suite, cellphones are being use in sleep research. Motion and orientation sensors and audio recorders are used in sleep analysis. Both head and body motions are tracked by the gyroscope and accelerometer, respectively. The microphone is sensitive enough to pick up noises like snoring. Approaches to sleep analysis on smartphones range widely. Sensor data may be analyzed using machine learning algorithms. These systems can distinguish between light, deep, and REM sleep by analyzing data from motion and sound sensors. Gather information about your sleep using surveys or questionnaires like the Pittsburgh Sleep Quality Index (PSQI) or the Epworth Sleepiness Scale (ESS). Analysis of sleep patterns with a mobile device has several benefits. It's a simple and non-invasive way to study how you sleep. Second, it offers continuous monitoring of sleep, which might lead to the identification and resolution of sleep disorders. Third, it may provide objective sleep data for the diagnosis of sleep disorders and the development of individualized treatment plans. Analysis of one's slumber using a smartphone might shed light on our sleeping habits and encourage healthy ones. Machine learning algorithms and questionnaires are two methods for analyzing sleep quality on a smartphone. Smartphone-based sleep studies are easy to implement and conduct, and they provide reliable, objective information on one's nightly slumber.