Monitoring the sleep respiratory rate with microcontroller Wi-Fi

Sleepness is the most common repairing operation of human body. Since today people are expressing more on caring for their own health, spending money on a device that can inform their health during sleep are normal things nowadays. Sleep Apnea, a symptom where people stop breathing or breathe slowly at night, is one of the upcoming issues that can lead to many more serious diseases. Respiratory Rate (RR) during sleep is a very normal way to detect that sleepness disorder. Using a smart device for seeing our own RR at night helpessly comes with burdens like contact-needed, costly, not that easy to find.So, we look for a possible way of bringing an off-the-shelf way to check this undesirable sickness. In this paper, we used ESP32 which is a very affordable single board computer and exploited its Wi-Fi transmitting pattern, then paired to human sleep RR to create a mapping rule of those.After gathering Wi-Fi variation from ESP32, we used Hampel Filtering, Gaussian Filtering, Linear Interpolation and Butterworth Low Pass Filtering to filter out parts of the signal that is not related to RR factor. we tested with newly collected data from 4 volunteers with different body shape, age and gender. To evaluate the result, we compared predicted Sleep RR with annotated RR from the Vernier Belt. With a proper fine-tuned parameters, it resulted as that the model can significantly point out slow-breathing RR from others. So, this manner of working can help people who are in need of Sleep Apnea monitoring which is one of the most sleepness concerning nowadays.