Driver drowsiness detection system had been developed as mobile device application such as Percentage of Eye Closure (PERCLOS) measured by using mobile device camera. Nevertheless, the mobile device has the potential risk of distracting the driver's attention, causing accidents. Thus, a wearable-type drowsiness detection system is proposed to overcome such issue. The proposed system used self-designed wristband consisted of photoplethysmogram sensor and galvanic skin response sensor. The sensors data are sent to the mobile device which served as a main analyzing processing unit. Those data are analyzed along with the motion sensors, which are the mobile device built-in accelerometer and gyroscope sensors. Five features are extracted accordingly based on the received raw sensors data, including heart rate, pulse rate variability, respiratory rate, stress level, and adjustment counter. Those features are further served as computation parameters to a support vector machine to derive the driver drowsiness state. The testing results indicated that the accuracy of the system with SVM model reached up to 98.3%. In addition, driver will be alerted using graphical and vibration alarm generated by the mobile device. In fact, the integration of driver physical behavior and physiological signals is proven to be an outstanding solution to detect driver drowsiness in a safer, more flexible and portable used.