TY - JOUR
T1 - Linked adaptive neuro-fuzzy inference system for biosignal distortion detection system
AU - Park, Jun Yong
AU - Kim, Dong W.
AU - Kang, Tae Koo
AU - Lim, Myo Taeg
N1 - Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (grants NRF-2016R1D1A1B01016071 and NRF-2017R1D1A1B03031467).
PY - 2019
Y1 - 2019
N2 - This paper proposes a biosignal distortion detection algorithm for a driver healthcare system based on a contact biosensor and a linked adaptive neuro-fuzzy inference system (ANFIS), and demonstrate its superiority using actual vehicle experiments. Contact biosensors are highly sensitive to vehicle vibration and turning. Although vehicle suspension contributes significantly to ride quality, vibration transfers to the driver and contact between the driver and biosensor can become unstable when executing a turn, causing the driver's biosignal to not be measured well. This study estimated the driver's biosignal state using acceleration, angular velocity, and slip ratio measurements obtained from sensor fusion. When the measurement exceeded a defined threshold, the driver healthcare system removed unreliable biosignal data. We adopted ANFIS to improve the proposed sensor fusion algorithm estimate accuracy for the driver's biosignal state and improved the healthcare system robustness to road conditions. The effectiveness of the proposed algorithm was demonstrated experimentally by comparing the system using sensor fusion and linked ANFIS.
AB - This paper proposes a biosignal distortion detection algorithm for a driver healthcare system based on a contact biosensor and a linked adaptive neuro-fuzzy inference system (ANFIS), and demonstrate its superiority using actual vehicle experiments. Contact biosensors are highly sensitive to vehicle vibration and turning. Although vehicle suspension contributes significantly to ride quality, vibration transfers to the driver and contact between the driver and biosensor can become unstable when executing a turn, causing the driver's biosignal to not be measured well. This study estimated the driver's biosignal state using acceleration, angular velocity, and slip ratio measurements obtained from sensor fusion. When the measurement exceeded a defined threshold, the driver healthcare system removed unreliable biosignal data. We adopted ANFIS to improve the proposed sensor fusion algorithm estimate accuracy for the driver's biosignal state and improved the healthcare system robustness to road conditions. The effectiveness of the proposed algorithm was demonstrated experimentally by comparing the system using sensor fusion and linked ANFIS.
KW - Linked adaptive neuro fuzzy inference system
KW - biosignal distortion detection
KW - driver healthcare system
KW - sensor fusion
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U2 - 10.3233/JIFS-182532
DO - 10.3233/JIFS-182532
M3 - Article
AN - SCOPUS:85077469025
VL - 37
SP - 7725
EP - 7735
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
SN - 1064-1246
IS - 6
ER -