Inertial measurement unit (IMU) sensors are widely used in motion tracking for various applications, e.g., virtual physical therapy and fitness training. Traditional IMU-based motion tracking systems use 9-axis IMU sensors that include an accelerometer, gyroscope, and magnetometer. The magnetometer is essential to correct the yaw drift in orientation estimation. However, its magnetic field measurement is often disturbed by the ferromagnetic materials in the environment and requires frequent calibration. Moreover, most IMU-based systems require multiple IMU sensors to track the body motion and are not convenient for use. In this paper, we propose a novel approach that uses a single 6-axis IMU sensor of a consumer smartwatch without any magnetometer to track the user's 3D arm motion in real time. We use a recurrent neural network (RNN) model to estimate the 3D positions of both the wrist and the elbow from the noisy IMU data. Compared with the state-of-the-art approaches that use either the 9-axis IMU sensor or the combination of a 6-axis IMU and an extra device, our proposed approach significantly improves the usability and potential for pervasiveness by not requiring a magnetometer or any extra device, while achieving comparable results.

Playing is an essential way for preschoolers to learn. There are three types of games for preschoolers: functional play, constructive play, and symbolic play. However, existing works/games can only work for one specific type of children's play. Therefore, we designed and implemented an extensible children's game framework based on Augmented Reality building blocks. We implement an AR prototype from scratch that achieves up to 8 blocks with 81% detection rate and overhead of 21ms (46 FPS) on average.