Name
Abstract | ||
|---|---|---|
Radio signals are sensitive to changes in the environment, which for example is reflected on the received signal strength (RSS) measurements of low-cost wireless devices. This information has been used effectively in the past years e.g. in device-free localization and tracking. Recent literature has also shown that the fading information of the wireless channel can be exploited to estimate the breathing rate of a person in a non-invasive manner; a research topic we address in this paper. To the best of our knowledge, we demonstrate for the first time that the respiration rate of a person can be accurately estimated using only a single IEEE 802.15.4 compliant TX-RX pair. We exploit channel diversity, low-jitter periodic communication, and oversampling to enhance the breathing estimates, and make use of a decimation filter to decrease the computational requirements of breathing estimation. In addition, we develop a hidden Markov model (HMM) to identify the time instances when breathing estimation is not possible, i.e., during times when other motion than breathing occurs. We experimentally validate the accuracy of the system and the results suggest that the respiration rate can be estimated with a mean error of 0.03 breaths per minute, the lowest breathing rate error reported to date using IEEE 802.15.4 compliant transceivers. We also demonstrate that the breathing of two people can be monitored simultaneously, a result not reported in earlier literature. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Wireless,Decimation,Oversampling,Computer science,Simulation,Fading,Communication channel,Respiratory rate,Breathing,Hidden Markov model |
DocType | Volume | Citations |
Journal | abs/1307.0084 | 3 |
PageRank | References | Authors |
0.50 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ossi Kaltiokallio | 1 | 221 | 14.41 |
| Hüseyin Yigitler | 2 | 74 | 10.20 |
| Riku Jäntti | 3 | 773 | 92.13 |
| Neal Patwari | 4 | 3805 | 241.58 |