BaroPoser: Real-time Human Motion Tracking from IMUs and Barometers in Everyday Devices
This addresses the limitation of existing motion tracking methods for applications like fitness or AR that require accurate pose estimation on non-flat terrain, representing an incremental improvement by integrating barometric data.
The paper tackled the problem of inaccurate human motion tracking from IMUs on uneven terrain by combining IMU and barometric data from smartphones and smartwatches, resulting in a method that outperforms state-of-the-art IMU-only approaches in pose estimation and global translation.
In recent years, tracking human motion using IMUs from everyday devices such as smartphones and smartwatches has gained increasing popularity. However, due to the sparsity of sensor measurements and the lack of datasets capturing human motion over uneven terrain, existing methods often struggle with pose estimation accuracy and are typically limited to recovering movements on flat terrain only. To this end, we present BaroPoser, the first method that combines IMU and barometric data recorded by a smartphone and a smartwatch to estimate human pose and global translation in real time. By leveraging barometric readings, we estimate sensor height changes, which provide valuable cues for both improving the accuracy of human pose estimation and predicting global translation on non-flat terrain. Furthermore, we propose a local thigh coordinate frame to disentangle local and global motion input for better pose representation learning. We evaluate our method on both public benchmark datasets and real-world recordings. Quantitative and qualitative results demonstrate that our approach outperforms the state-of-the-art (SOTA) methods that use IMUs only with the same hardware configuration.