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Exploring The Implementation of AI in a Cost-effective Device for Predicting Sleep Quality

This research presents the development and effectiveness of an Arduino-based sleep tracking device that can accurately measure various parameters of sleep, including movement, temperature, sound, light intensity, and humidity. The device was designed to be low-cost and easy to use, while not compromising on its ability to accurately measure sleep activity. The effectiveness of the device was evaluated by collecting data from test subjects and comparing it to the data collected by other sleep tracking devices. The collected data was then processed and used to train Artificial Intelligence (AI) models such as Backward Propagation Neural Network, Linear Regression Model, and Grey Relation Analysis, to predict the sleep quality rating from 0% to 100% and to identify the main cause of poor sleep. The results of the study demonstrated that the Arduino-based sleep tracking device is an effective and cost-efficient tool for measuring various parameters of sleep. However, the pressure sensor may sometimes result in inaccurate readings, which can be addressed through data cleaning and filtering. Furthermore, the use of AI models was able to predict the sleep quality rating and identify the main causes of poor sleep with high accuracy. Further research is needed to evaluate the device's performance over a longer period of time and in a larger sample of participants.
Keywords: Arduino, Sleep Quality, Neural Network, Linear Regression, Relation Analysis

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