Abstract
A blink is one of physiological signals that indicates a consciousness level or a drowsiness. Using a Doppler sensor is one solution to realize non- contact blink detection without cameras. However, it is difficult to detect blinks using a Doppler sensor because of low signal to noise power ratio (SNR) of reflected signal from an eyelid. We previously proposed the blink detection method based on machine learning and show that the method provides high probability of detection of blinks. However, it is necessary for the method to do a prior learning process for each person or environment. In this paper, we propose a high accuracy blink detection method without prior learning process. By applying constant false alarm rate (CFAR) processing to the blink signal detection, the method can be robust against the fluctuation of a body or noise, and the probability of detection can be improved. Moreover, we apply two kinds of classification based on the characteristic of the signal waveform of the blink. Thereby, a subtle body movement that is easy to be wrongly detected as a blink is excluded, and false positive in detection can be reduced. We conducted three experiments to evaluate the detection accuracy. As a result, we show that our proposal achieved high detection accuracy of around 99 %.
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509013289 |
| DOIs | |
| Publication status | Published - 2017 Feb 2 |
| Event | 59th IEEE Global Communications Conference, GLOBECOM 2016 - Washington, United States Duration: 2016 Dec 4 → 2016 Dec 8 |
Other
| Other | 59th IEEE Global Communications Conference, GLOBECOM 2016 |
|---|---|
| Country | United States |
| City | Washington |
| Period | 16/12/4 → 16/12/8 |
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ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Networks and Communications
- Hardware and Architecture
- Safety, Risk, Reliability and Quality
Cite this
Improvement of blink detection using a doppler sensor based on CFAR processing. / Tamba, Chihiro; Hayashi, Hirotaka; Ohtsuki, Tomoaki.
2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 7841887.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Improvement of blink detection using a doppler sensor based on CFAR processing
AU - Tamba, Chihiro
AU - Hayashi, Hirotaka
AU - Ohtsuki, Tomoaki
PY - 2017/2/2
Y1 - 2017/2/2
N2 - A blink is one of physiological signals that indicates a consciousness level or a drowsiness. Using a Doppler sensor is one solution to realize non- contact blink detection without cameras. However, it is difficult to detect blinks using a Doppler sensor because of low signal to noise power ratio (SNR) of reflected signal from an eyelid. We previously proposed the blink detection method based on machine learning and show that the method provides high probability of detection of blinks. However, it is necessary for the method to do a prior learning process for each person or environment. In this paper, we propose a high accuracy blink detection method without prior learning process. By applying constant false alarm rate (CFAR) processing to the blink signal detection, the method can be robust against the fluctuation of a body or noise, and the probability of detection can be improved. Moreover, we apply two kinds of classification based on the characteristic of the signal waveform of the blink. Thereby, a subtle body movement that is easy to be wrongly detected as a blink is excluded, and false positive in detection can be reduced. We conducted three experiments to evaluate the detection accuracy. As a result, we show that our proposal achieved high detection accuracy of around 99 %.
AB - A blink is one of physiological signals that indicates a consciousness level or a drowsiness. Using a Doppler sensor is one solution to realize non- contact blink detection without cameras. However, it is difficult to detect blinks using a Doppler sensor because of low signal to noise power ratio (SNR) of reflected signal from an eyelid. We previously proposed the blink detection method based on machine learning and show that the method provides high probability of detection of blinks. However, it is necessary for the method to do a prior learning process for each person or environment. In this paper, we propose a high accuracy blink detection method without prior learning process. By applying constant false alarm rate (CFAR) processing to the blink signal detection, the method can be robust against the fluctuation of a body or noise, and the probability of detection can be improved. Moreover, we apply two kinds of classification based on the characteristic of the signal waveform of the blink. Thereby, a subtle body movement that is easy to be wrongly detected as a blink is excluded, and false positive in detection can be reduced. We conducted three experiments to evaluate the detection accuracy. As a result, we show that our proposal achieved high detection accuracy of around 99 %.
UR - http://www.scopus.com/inward/record.url?scp=85015369395&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015369395&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2016.7841887
DO - 10.1109/GLOCOM.2016.7841887
M3 - Conference contribution
AN - SCOPUS:85015369395
BT - 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -