Abstract
In this study, EEG (EEG-MPF, EEG-α/β), heart rate variability (RRV3), tracking error and subjective rating of fatigue (drowsiness) while performing a simulated driving task were measured. The relation between these measurements and drowsiness was analyzed. As a result, EEG-MPF tended to decrease with the increase of drowsiness. It tended that EEG-α/β, RRV3 and tracking error increased with the increase of drowsiness. Then, a method for predicting drowsiness by applying Bayesian estimation to physiological measurements was proposed. Bayesian estimation carries out a statistical inference using some kind of evidences or observations and calculating the probability that a hypothesis is true. An attempt was made to predicting drowsiness by applying Bayesian estimation to psychological parameters such as EEG-MPF, EEG-α/β, RRV3. As a result, it was suggested that the proposed method can predict the symptom of decreased consciousness (drowsiness).
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the SICE Annual Conference |
| Pages | 58-63 |
| Number of pages | 6 |
| Publication status | Published - 2011 |
| Event | 50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo, Japan Duration: Sep 13 2011 → Sep 18 2011 |
Other
| Other | 50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 |
|---|---|
| Country | Japan |
| City | Tokyo |
| Period | 9/13/11 → 9/18/11 |
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Keywords
- Bayesian estimation
- drowsiness
- EEG
- HRV
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Computer Science Applications
Cite this
An attempt to predict drowsiness by Bayesian estimation. / Murata, Atsuo; Matsuda, Yusuke; Moriwaka, Makoto; Hayami, Takehito.
Proceedings of the SICE Annual Conference. 2011. p. 58-63 6060576.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An attempt to predict drowsiness by Bayesian estimation
AU - Murata, Atsuo
AU - Matsuda, Yusuke
AU - Moriwaka, Makoto
AU - Hayami, Takehito
PY - 2011
Y1 - 2011
N2 - In this study, EEG (EEG-MPF, EEG-α/β), heart rate variability (RRV3), tracking error and subjective rating of fatigue (drowsiness) while performing a simulated driving task were measured. The relation between these measurements and drowsiness was analyzed. As a result, EEG-MPF tended to decrease with the increase of drowsiness. It tended that EEG-α/β, RRV3 and tracking error increased with the increase of drowsiness. Then, a method for predicting drowsiness by applying Bayesian estimation to physiological measurements was proposed. Bayesian estimation carries out a statistical inference using some kind of evidences or observations and calculating the probability that a hypothesis is true. An attempt was made to predicting drowsiness by applying Bayesian estimation to psychological parameters such as EEG-MPF, EEG-α/β, RRV3. As a result, it was suggested that the proposed method can predict the symptom of decreased consciousness (drowsiness).
AB - In this study, EEG (EEG-MPF, EEG-α/β), heart rate variability (RRV3), tracking error and subjective rating of fatigue (drowsiness) while performing a simulated driving task were measured. The relation between these measurements and drowsiness was analyzed. As a result, EEG-MPF tended to decrease with the increase of drowsiness. It tended that EEG-α/β, RRV3 and tracking error increased with the increase of drowsiness. Then, a method for predicting drowsiness by applying Bayesian estimation to physiological measurements was proposed. Bayesian estimation carries out a statistical inference using some kind of evidences or observations and calculating the probability that a hypothesis is true. An attempt was made to predicting drowsiness by applying Bayesian estimation to psychological parameters such as EEG-MPF, EEG-α/β, RRV3. As a result, it was suggested that the proposed method can predict the symptom of decreased consciousness (drowsiness).
KW - Bayesian estimation
KW - drowsiness
KW - EEG
KW - HRV
UR - http://www.scopus.com/inward/record.url?scp=81255167364&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=81255167364&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9784907764395
SP - 58
EP - 63
BT - Proceedings of the SICE Annual Conference
ER -