Two main approaches have been tried to exploit the complementary properties of visual and this explanation inertial sensors, namely the loosely coupled approach and the tightly coupled approach [13]. In the loosely coupled approach [14�C16], the vision-based tracking system and the INS exchange information each other, while the sensor data processing takes place in separate modules. The information delivered by the IMU can be used to speed up the tracking task of the features by predicting their locations within the next frame; in turn, data from the visual sensor allows updating the calibration parameters of inertial sensors. Conversely, in the tightly coupled approach all measurements, either visual or inertial, are combined and processed using a statistical filtering framework. In particular, Kalman filter-based methods are the preferred tool to perform sensor fusion [2,17,18].In this paper the problem of estimating the ego-motion of a hand-held IMU-camera system is addressed. The presented development stems from our ongoing research on tracking position and orientation of human body segments for applications in telerehabilitation. While orientation tracking can be successfully performed using EKF-based sensor fusion methods based on inertial/magnetic measurements [10,19,20], position tracking requires some form of aiding [21].A tightly coupled approach was adopted to the design of a system in which pose estimates were derived from observations of fiducials. Two EKF-based sensor fusion methods were developed that built somewhat upon the approaches investigated in [2,18], respectively. They were called DLT-based EKF (DLT: Direct Linear Transformation) and error-driven EKF. Their names were intended to denote the different use made of visual information available from fiducials: the visually estimated pose produced by the DLT method was directly delivered to the DLT-based EKF, while in the error-driven EKF the visual measurements were the difference between the measured and predicted location of the fiducials in the image plane. In each filter 2D frame-to-frame correspondences were established by a process of model-based visual feature tracking: a feature was searched within a size-variable window around its predicted location, based on 3D known coordinates of fiducials and the a priori state estimate delivered by the EKF. Moreover, the visual measurement equations were stacked to the measurement equations for the IMU sensors (accelerometer and magnetic sensor), and paired to the state transition equation, where the state vector included quaternion of rotation, position and velocity of the body frame relative to the navigation frame.