An Attitude Compensation Technique for a MEMS Motion Sensor Based Digital Writing Instrument

A MAG-muIMU which is based on MEMS gyroscopes, accelerometers, and magnetometers is developed for real-time estimation of human hand motions. Appropriate filtering, transformation and sensor fusion techniques are combined in the ubiquitous digital writing instrument to record handwriting on any surface. In this paper, we discuss the design of an extended Kalman filter based on MAG-muIMU (micro inertial measurement unit with magnetometers) for real-time attitude tracking. The filter utilizes the gyroscope propagation for transient updates and correction by reference field sensors, such as gravity sensors, magnetometers or star trackers. A process model is derived to separate sensor bias and to minimize wideband noise. The attitude calculation is based on quaternion which, when compared to Euler angles, has no singularity problem. Testing with synthetic data and actual sensor data proved the filter will converge and accurately track the attitude of a rigid body. Our goal is to implement this algorithm for motion recognition of a 3D ubiquitous digital pen.