Wireless field intensity breath detecting method and device and breath detector

Abstract

The invention provides a wireless field intensity breath detecting method and device and a breath detector. Channel state information CSI is extracted from existing 802.11WiFi wireless signals and is utilized for acquiring the weak changes, caused by breath during sleep of people, of the wireless signals, and therefore the aim of tracking and detecting the human body breath condition is achieved. By means of a CSI extracting step, a data correction step and a breath rate detecting step, an existing 802.11a/g/n WiFi network and a mobile terminal are utilized, no professional wearable sensor is needed, the wireless field intensity breath detector is customized, besides, the device can be installed in a mobile terminal of a user as software, and detection can be achieved by upgrading existing wearable equipment. Therefore, the device can be popularized to home environments in a large scale, serves as an applied product for people to carry out long-term human body health monitoring every day and has very high application and business value.

Description

technical field [0001] The invention relates to the technical field of breath detection, in particular to a wireless field strength breath detection method, device and breath detector. Background technique [0002] Human respiratory conditions can indicate the basic physical function status of a person, and are an important means of assessing a person's health, and can determine various potential health problems. For example, the breathing rate of a person during sleep will definitely help in the diagnosis and treatment of conditions such as respiratory arrest and asthma. [0003] Existing technologies for detecting respiratory rate can be divided into four groups: professional-based sensors, smartphone-equipped wearable sensors, non-touch sensors, and radio-frequency signal-based methods. [0004] 1. The method based on professional sensors, taking polysomnography as an example, is to let the patient wear multiple sensors to sense the patient's respiratory rate. This appr...

AI Technical Summary

Problems solved by technology

Solutions using Doppler radar, ultra-wideband technology, and FMCW all need to rely on special hardware such as FMCW radar or Doppler radar, which leads to high system costs and high complexity, so large-scale deployment is impractical

However, to detect respiration rate through the measured value of received signal strength (RSS) extracted from wireless sensor nodes, additional wireless network infrastructure and high-density sensor nodes are required, and rough channel state information cannot be accurately calculated. Respiratory rate value

[0008] To sum up, the traditional method of detecting human breathing during sleep requires patients to be admitted to the hospital and wear special sensor equipment, which is very inconvenient and expensive

Furthermore, due to the patient's sleep in the unfamiliar environment of the hospital, the monitoring results may also be biased, and the hospital also needs to provide medical equipment and venues for sleep detection for a long time, which is not small for the hospital. cost of

Images