This study proposes a method for real-time identification of a driver model. The proposed method requires only the yaw rate sensor, the steering angle sensor, and velocity sensors that are usually installed in the production car. The identification algorithm involves the division of the recorded data, prefiltering of the divided data, estimation of the driver's desired response, and identification. The prefilter extracts the driver's involuntary response that can be modelled in a simple form. The ideal car response that the driver attempts to track is estimated from the recorded data, and this response is provided to the identification algorithm of the feedback driver model for error tracking. These newly developed methods enable real-time identification under actual driving conditions. The driving simulator experiments and the actual driving tests were performed, and the proposed method was validated. The results show that the time history of the variation in the driver's characteristics can be realized in real time using the proposed method.
Microwave reflectometric measurement is applied to diagnose vital signal of a human. The reflectometer signal is processed by a quadrature phase detector in order to obtain both phase and amplitude components of the signal. The phase component is analyzed by using fast-Fourier transform and wavelet transform to evaluate the frequency spectrum of the heartbeat and respiration. Various applications of the microwave measurement are discussed.
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