MotionTrace: IMU-based Trajectory Prediction for Smartphone AR Interactions

Smartphone powered mobile Augmented Reality (AR) applications require precise and accurate 6DoF localization of the phone within a 3D space. This typically requires camera-based localization systems that are power intensive and prone to motion blurs, thereby limiting their usage. In such cases, IMU-based methods are a promising alternative for low power localization sensing. In this paper, we present MotionTrace, a method for predicting a user's handheld phone trajectory using a smartphone's inertial sensor. We evaluated MotionTrace over future hand positions at 50, 100, 200, 400, and 800ms time horizons using the large motion capture (AMASS) and smartphone-based full-body pose estimation (Pose-on-the-Go) datasets. Our results show that MotionTrace can estimate the future phone position of the user with an average MSE between 0.11 – 143.62 mm across different time horizons.