Hernan Paz Penagos, Esteban Morales Mahecha, Adriana Melo Camargo, Edison Sanchez Jimenez, Diego Arturo Coy Sarmiento, Sara Valentina Hernández Salazar
{"title":"Detection, recognition and transmission of snoring signals by ESP32","authors":"Hernan Paz Penagos, Esteban Morales Mahecha, Adriana Melo Camargo, Edison Sanchez Jimenez, Diego Arturo Coy Sarmiento, Sara Valentina Hernández Salazar","doi":"10.1016/j.measen.2024.101397","DOIUrl":null,"url":null,"abstract":"<div><div>This study focuses on the monitoring, transmission, recognition and detection of snoring signals and their relationship with obstructive sleep apnea. To achieve this purpose, the ESP32 microcontroller and a MEMS technology microphone were used to capture and measure characteristic parameters of snoring signals, such as their intensity, frequency and duration. In addition, the WiFi radio interface was used to send the signals to a server where the information was processed, the snoring was detected, linked to a chatbot in Nodred to show the user in a graphical interface his diagnosis of the snoring level. This comprehensive approach allows real-time, wireless monitoring of snoring, leading to a less invasive diagnosis of obstructive sleep apnea.</div></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"36 ","pages":"Article 101397"},"PeriodicalIF":0.0000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement Sensors","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665917424003738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
This study focuses on the monitoring, transmission, recognition and detection of snoring signals and their relationship with obstructive sleep apnea. To achieve this purpose, the ESP32 microcontroller and a MEMS technology microphone were used to capture and measure characteristic parameters of snoring signals, such as their intensity, frequency and duration. In addition, the WiFi radio interface was used to send the signals to a server where the information was processed, the snoring was detected, linked to a chatbot in Nodred to show the user in a graphical interface his diagnosis of the snoring level. This comprehensive approach allows real-time, wireless monitoring of snoring, leading to a less invasive diagnosis of obstructive sleep apnea.