Motion Tracklet Oriented 6-DoF Inertial Tracking Using Commodity Smartphones


Motion tracklets are the basic fragments of the track followed by a moving object and constitute various everyday motion behavior. An accurate estimation of motion tracklets in 3-D space can enable a wide range of applications, ranging from human computer interaction to medical rehabilitation. This paper presents a novel dataset for accurate 6-DoF motion tracklet estimation with the inertial sensors on commodity smartphones. The dataset consists of around 100 minutes of handheld motion with 3 predominant types of motion track-lets and accurate ground truth using the Vicon systems. With the presented dataset, we further benchmarked the trajectory estimation using a lightweight neural odometry model, showcasing how the dataset can be used while providing quantitative performance for downstream tasks. Our dataset, toolkit and source code available at

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems
Peize Li
Peize Li
PhD Student

PhD student at University of Edinburgh