Real-Time Snoring Monitoring and Intervention with IoT and Long Short-Term Memory Network

Millions of people across the globe suffer from snoring, a widespread sleep condition that disrupts their everyday lives and may cause a host of health problems. A promising area for snoring treatment in recent years has been the combination of machine learning methods with Internet of Things (IoT) sensors. An innovative method for monitoring and intervening in snoring in real-time using IoT and Long Short-Term Memory (LSTM) networks is presented in this research. The wearable sensor that tracks snoring patterns while you sleep is an integral part of the proposed system using IoT capabilities. Before being sent to a central processing unit for analysis, the sensor gathers raw data, including audio recordings and movement signals. An LSTM network quickly and accurately detects instances of snoring by processing the collected data. The user receives immediate feedback via a mobile app when they snore, allowing them to take action to reduce the issue. Extensive testing and verification show that the proposed approach works in real-time snoring incident detection. Additionally, there is encouraging evidence that combining intervention measures, such as postural changes or mild vibrations, might improve sleep quality and decrease snoring episodes. The device is a great tool for those who want to manage their snoring issues successfully since it is non-invasive, has real-time capabilities and has intervention mechanisms.