Abstract | ||
---|---|---|
The respiration rate of a person provides critical information about their well-being. Conventionally, contact sensing is used for breathing monitoring; however, it is expensive, uncomfortable, and immobile. In-home non-contact breathing monitoring is now possible via Doppler radar and motion capture video sensors, yet these technologies are limited in mobility, among other limitations. When monitoring a patient who is free to move around his or her home, it is dificult to scale current non-contact sensors to cover the large area. Our RUBreathing sensor system uses RF received signal strength (RSS) in a network to estimate breathing rate in real-time with high accuracy over a wide area. In this demonstration, we show the sensor continuously estimating a patient's respiration rate from non-contact RSS measurements between wireless devices. |
Year | DOI | Venue |
---|---|---|
2015 | 10.1145/2737095.2737133 | IPSN |
Field | DocType | Citations |
Doppler radar,Motion capture,Respiratory rate monitoring,Wireless,Simulation,Computer science,Real-time computing,Respiratory rate,Sensor system,Breathing,RSS | Conference | 2 |
PageRank | References | Authors |
0.36 | 2 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Anh Luong | 1 | 24 | 6.05 |
Spencer Madsen | 2 | 2 | 0.36 |
Michael Empey | 3 | 2 | 0.36 |
Neal Patwari | 4 | 3805 | 241.58 |