Adaptive Recalibration Algorithm for Removing Sensor Errors and Its Applications in Motion Tracking

Measurement data generated by sensors on mobile devices, such as smartphones, wearables, and smart controllers, are affected by integration and real-world usage errors that can collectively be modeled as bias, linear/quadratic drift, and random noise. Motion tracking based on sensors, such as accelerometer, gyroscope, and magnetometer, is used in the field of mobile gaming control, wearable handwriting tracking, virtual reality controller, mobile phone motion tracking to control remote camera, and so on. Controller applications on mobile phone require motion sensor post processing algorithms that can work on infinite motion possibilities with instant response times and high accuracy. In this paper, we introduce a training-based algorithm that uses adaptive threshold recalibration for correcting these sensor errors identifying valid motion areas. We apply the adaptive error correction algorithm in mobile devices having accelerometer for generating linear motion coordinates. We also apply the algorithm to track rotational motion when accelerometer is fused with gyroscope and magnetometer sensors. The algorithm proposed in this paper removes motion sensor bias and drifts in 100% of the cases. The algorithm successfully identifies the duration of active motion on the motion sensor(s) data in 100% of the cases. We also compare the outputs of the proposed linear and rotational motion tracking algorithms with existing algorithms to show superior performance in terms of stability, noise tolerance, and motion tracking accuracy.