UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units

TL;DR

UMotion introduces an uncertainty-aware, real-time framework that fuses inertial and ultra-wideband sensor data to improve 3D human motion estimation, addressing pose ambiguity and sensor drift issues.

Contribution

The paper presents a novel uncertainty-driven fusion framework using an Unscented Kalman Filter to combine IMU and UWB data for enhanced human motion estimation.

Findings

Improves pose accuracy over existing methods.

Effectively stabilizes sensor data in real-time.

Demonstrates robustness on synthetic and real datasets.

Abstract

Sparse wearable inertial measurement units (IMUs) have gained popularity for estimating 3D human motion. However, challenges such as pose ambiguity, data drift, and limited adaptability to diverse bodies persist. To address these issues, we propose UMotion, an uncertainty-driven, online fusing-all state estimation framework for 3D human shape and pose estimation, supported by six integrated, body-worn ultra-wideband (UWB) distance sensors with IMUs. UWB sensors measure inter-node distances to infer spatial relationships, aiding in resolving pose ambiguities and body shape variations when combined with anthropometric data. Unfortunately, IMUs are prone to drift, and UWB sensors are affected by body occlusions. Consequently, we develop a tightly coupled Unscented Kalman Filter (UKF) framework that fuses uncertainties from sensor data and estimated human motion based on individual body shape. The UKF iteratively refines IMU and UWB measurements by aligning them with uncertain human motion constraints in real-time, producing optimal estimates for each. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of UMotion in stabilizing sensor data and the improvement over state of the art in pose accuracy.