Embedded IoT Data Collection for Snore Analysis

Abstract

Internet of Things (IoT)-based devices are in demand for capturing different data types to produce essential information for the receiver. Human sleep behavior is one area open for research, particularly on human snoring. The ESP32 microcontroller was used to modify the processes of capturing human snoring during sleep. This embedded IoT-based device monitors and captures the snoring activity of the human being while sleeping. A prototype of the modified device with its new algorithm was developed, and different test experiments were conducted to test its system performance. Experiment results showcased the accuracy of capturing snoring frequencies beyond established norms and measuring decibel levels within specific parameters. Technical challenges were encountered, such as static interferences and data storage errors, but all were systematically addressed, highlighting the system's robustness. Pilot experiments on EXP1 and EXP2 provided insights into the system's adaptability to different environmental conditions. It is recommended to incorporate upgraded machine learning algorithms into a more powerful microcontroller to improve noise differentiation and computational capabilities and to collaborate with sleep experts to enable diagnostic capabilities. The research emphasizes the system's potential for real-world application in advancing healthcare solutions while highlighting the need for continuous evolution