Dataset for the Construction of an Artificial Intelligence Learning Model for Sleep Analysis
With the increasing interest in sleep, many devices and applications that can assist sleep have been developed to improve the quality of sleep. Using such devices and applications, elements to analyze quality of sleep, such as tossing and turning and snoring, can be measured using sensors and the surrounding temperature, humidity, noise and brightness can be measured to analyze the appropriateness of the sleep environment. However, not only does analyzing the sleep data measured by simply using an algorithm not accurately measure the quality of sleep, it doesn’t fully utilize the correlation between data characteristics and the data. This is a study on a dataset to construct a learning model to analyze the relationship between quality of sleep and sleep environment. To do so, data was analyzed using different sensors, and the characteristics that can be used to construct a learning model were investigated. Also, elements that can decide quality of sleep were collected along with analyzed data characteristics to define the dataset, which can be used to construct the learning model. By analyzing sleep through this dataset, an environment for optimum sleep can be expected.
Keywords - AI, Machine learning, Sleep Analysis, Healthcare