Affiliations: [a]
Department of Electrical Engineering, Korea University, Seoul, South Korea
| [b]
Department of Digital Electronics, Inha Technical College, Nam-Gu, Incheon, South Korea
| [c]
Department of Human Intelligence and Robot Engineering, Sang Myung University, Chungnam, South Korea
Correspondence:
[*]
Corresponding author. Dong W. Kim, Department of Digital Electronics, Inha Technical College, 100 inha-ro, Nam-Gu, Incheon, South Korea. E-mail: [email protected] and Myo Taeg Lim, Department of Electrical Engineering, Korea University, Seoul, South Korea. E-mail: [email protected].
Abstract: 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.
