DRER: Deep Learning-Based Driver's Real Emotion Recognizer.

In intelligent vehicles, it is essential to monitor the driver's condition; however, recognizing the driver's emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver's emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver's real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers' real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver's real emotional state. Hence, we categorized the driver's emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver's induced emotion while driving situation.

Adhesive lift method for patterning arbitrary-shaped thin ion-selective films in micro/nanofluidic device.

Micro/nanofluidic platforms with nanoporous films have been utilized as research tools for studying electrokinetic phenomena occurring not only in macro-scale systems such as electro-desalination but also in micro-scale systems such as bio-molecular preconcentrators. However, due to the limitations of fabrication techniques, studies with nanoporous films are mainly limited to vary the physicochemical properties of the films such as surface charge and pore size, despite the enormous effect of the membrane morphology on the phenomena that is to be expected. Therefore, we propose an economic and feasible nanofabrication method called the "adhesive lift method" for patterning thin arbitrarily-shaped nanoporous film to integrate it into micro/nanofluidic platforms. The conformal patterning of the nanoporous films (Nafion or poly(3,4-ethylenedioxythiophene)polystyrene sulfonate (PEDOT:PSS) in this work) was accomplished with spin coating, oxygen plasma treatment and the "adhesive lift technique". Using the fabricated platforms, the initiation of ion concentration polarization along the film with various shapes was demonstrated. In particular, various electrokinetic characteristics of overlimiting conductance depending on the length scale of the microchannels were successfully demonstrated. Therefore, the presented adhesive lift method would provide platforms which can nearly mimic practical macro-scale fluidic systems so that the method would be very useful for studying various electrokinetic phenomena inside it.