TY - GEN
T1 - Mobile EEG Based Drowsiness Detection using K-Nearest Neighbor
AU - Purnamasari, Prima Dewi
AU - Yustiana, Pratiwi
AU - Putri Ratna, Anak Agung
AU - Sudiana, Dodi
PY - 2019/10
Y1 - 2019/10
N2 - In this research, a drowsiness detection system, named Drowsiver, was developed for a mobile electroencephalograph (EEG) and a mobile phone. The system is expected to minimize the causes of accidents caused by drowsy drivers. By using Electroencephalogram (EEG), the condition of drowsiness is detected by recording the electrical activity that occurs in the human brain and is represented as a frequency signal. The signal is transmitted to the Android mobile application via Bluetooth and will give an alarm notification if the drowsiness is detected. The brainwave from the mobile EEG is processed using Fast Fourier Transform (FFT) to extract its features. These features are classified using K-Nearest Neighbor (KNN) classifier. The system produces the best performance with the highest accuracy of 95.24% using the value of k=3 and four brain waves as features, namely Delta, Theta, Alpha, and Beta waves.
AB - In this research, a drowsiness detection system, named Drowsiver, was developed for a mobile electroencephalograph (EEG) and a mobile phone. The system is expected to minimize the causes of accidents caused by drowsy drivers. By using Electroencephalogram (EEG), the condition of drowsiness is detected by recording the electrical activity that occurs in the human brain and is represented as a frequency signal. The signal is transmitted to the Android mobile application via Bluetooth and will give an alarm notification if the drowsiness is detected. The brainwave from the mobile EEG is processed using Fast Fourier Transform (FFT) to extract its features. These features are classified using K-Nearest Neighbor (KNN) classifier. The system produces the best performance with the highest accuracy of 95.24% using the value of k=3 and four brain waves as features, namely Delta, Theta, Alpha, and Beta waves.
KW - Android Application
KW - Brain Wave
KW - Drowsiness
KW - Electroencephalogram (EEG)
UR - http://www.scopus.com/inward/record.url?scp=85077771473&partnerID=8YFLogxK
U2 - 10.1109/ICAwST.2019.8923161
DO - 10.1109/ICAwST.2019.8923161
M3 - Conference contribution
T3 - 2019 IEEE 10th International Conference on Awareness Science and Technology, iCAST 2019 - Proceedings
BT - 2019 IEEE 10th International Conference on Awareness Science and Technology, iCAST 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Awareness Science and Technology, iCAST 2019
Y2 - 23 October 2019 through 25 October 2019
ER -