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The posture of a user is one of the contextual information that can be used for mobile applications and the treatment of idiopathic scoliosis. This paper describes a method for monitoring the posture of a user during operation of a mobile device in three activities such as sitting, standing, and walking. The user posture monitoring system (UPMS) proposed in this paper is based on two major technologies. The first involves a tilt-angle measurement algorithm (TAMA) using an accelerometer. Unlike most previous studies, it is based on a relative computation using the dot product from the time-series acceleration data. Because TAMA does not require a physical calibration by a user, it is more robust and accurate compared to other methods that rely on absolute computations. The second technology is an effective signal-processing method that eliminates the motion acceleration component of the accelerometer signal using a second-order Butterworth low-pass filter (SLPF). Because the posture of a user is only related to the gravity acceleration component, the motion acceleration components should be removed. The TAMA and UPMS are implemented on a personal digital assistant (PDA). They are evaluated to verify the possibility of application to a mobile device. Additionally, a posture-based intelligent control interface in context-aware computing that reacts to the posture of a PDA user is implemented on the PDA to complement the poor user interface (UI) of the mobile device, and its results are presented.