TY - GEN
T1 - Driver's drowsiness estimation by combining EEG signal analysis and ICA-based fuzzy neural networks
AU - Lin, Chin Teng
AU - Liang, Sheng Fu
AU - Chen, Yu Chieh
AU - Hsu, Yung Chi
AU - Ko, Li-Wei
PY - 2006/12/1
Y1 - 2006/12/1
N2 - The public security has become an important issue in recent years, especially, the safe manipulation and control of vehicles in preventing the growing number of traffic accident fatalities. Accidents caused by drivers' drowsiness have a high fatality rate due to the decline of drivers' abilities in perception, recognition, and vehicle control abilities while sleepy. Preventing such an accident requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism to maintain the driver's maximum performance of driving. The ICAFNN is a fuzzy neural network (FNN) capable of parameter self-adapting and structure selfconstructing to acquire a small number of fuzzy rules for interpreting the embedded knowledge of a system from the given training data set. Our experiments show that the ICAFNN can achieve significant improvements in the accuracy of drowsiness estimation compared with our previous works.
AB - The public security has become an important issue in recent years, especially, the safe manipulation and control of vehicles in preventing the growing number of traffic accident fatalities. Accidents caused by drivers' drowsiness have a high fatality rate due to the decline of drivers' abilities in perception, recognition, and vehicle control abilities while sleepy. Preventing such an accident requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism to maintain the driver's maximum performance of driving. The ICAFNN is a fuzzy neural network (FNN) capable of parameter self-adapting and structure selfconstructing to acquire a small number of fuzzy rules for interpreting the embedded knowledge of a system from the given training data set. Our experiments show that the ICAFNN can achieve significant improvements in the accuracy of drowsiness estimation compared with our previous works.
UR - http://www.scopus.com/inward/record.url?scp=34547296519&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2006.1693037
DO - 10.1109/ISCAS.2006.1693037
M3 - Conference contribution
AN - SCOPUS:34547296519
SN - 0780393902
SN - 9780780393905
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 2125
EP - 2128
BT - ISCAS 2006
T2 - ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems
Y2 - 21 May 2006 through 24 May 2006
ER -
