Along with the development of localization and navigation technologies, the Global Positioning System (GPS) plays an important role in our daily life, but it is confined in outdoor environments. The technology of human localization has been developed in recent years. This technology utilizes sensors to determine the movement of human and measure the distance of walking, which is not only used to solve the problem of GPS out-of-lock, but also used for the indoor localization. This thesis describes a human step-length recognition and real-time localization base on an embedded system. The goal of this system is to develop a gait pattern classification and pedestrian dead reckoning (PDR) method for human localization. Through the information of an Inertial Measurement Unit (IMU) and two force sensors mounted on a shoe, the wireless transmission module is used to send data of sensors to an embedded platform. Then the functions of step detection, step length estimation and gait pattern recognition can be achieved. According to coordinate transformation and the ZUPT algorithm, the accumulated error of velocity can be corrected. The dead reckoning method is used to obtain the information of location. Finally, the information of human location and gait patterns is sent to the Android system for remote monitoring.