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

arXiv: https://arxiv.org/abs/2505.09393 · HTML: https://mlsanity.com/paper/2505.09393/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 21 of 21)

- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors](https://mlsanity.com/paper/2203.08528/) (2022)
- [AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing](https://mlsanity.com/paper/2207.13784/) (2022)
- [Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs](https://mlsanity.com/paper/1703.08014/) (2017)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation](https://mlsanity.com/paper/2203.15720/) (2022)
- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [Accurate 3D Body Shape Regression using Metric and Semantic Attributes](https://mlsanity.com/paper/2206.07036/) (2022)
- [EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors](https://mlsanity.com/paper/2305.01599/) (2023)
- [LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors](https://mlsanity.com/paper/2205.15410/) (2022)
- [Mocap Everyone Everywhere: Lightweight Motion Capture With Smartwatches and a Head-Mounted Camera](https://mlsanity.com/paper/2401.00847/) (2024)
- [Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors](https://mlsanity.com/paper/2312.02196/) (2023)
- [QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors](https://mlsanity.com/paper/2306.05666/) (2023)
- [HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations](https://mlsanity.com/paper/2403.03561/) (2024)
- [Physical Non-inertial Poser (PNP): Modeling Non-inertial Effects in Sparse-inertial Human Motion Capture](https://mlsanity.com/paper/2404.19619/) (2024)
- [SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data](https://mlsanity.com/paper/2311.02191/) (2023)
- [HybridCap: Inertia-aid Monocular Capture of Challenging Human Motions](https://mlsanity.com/paper/2203.09287/) (2022)
- [Fusing Monocular Images and Sparse IMU Signals for Real-time Human Motion Capture](https://mlsanity.com/paper/2309.00310/) (2023)
- [Ultra Inertial Poser: Scalable Motion Capture and Tracking from Sparse Inertial Sensors and Ultra-Wideband Ranging](https://mlsanity.com/paper/2404.19541/) (2024)

## Built on by (most cited first, 5 of 5)

- [Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors](https://mlsanity.com/paper/2603.16233/) (2026)
- [EgoExoMoCap: Distributed Ego-Exo Human Motion Capture](https://mlsanity.com/paper/2607.15868/) (2026)
- [Mocap Anywhere: Towards Pairwise-Distance based Motion Capture in the Wild (for the Wild)](https://mlsanity.com/paper/2601.19519/) (2026)
- [Ultra Diffusion Poser: Diffusion-Based Human Motion Tracking From Sparse Inertial Sensors and Ranging-Based Between-Sensor Distances](https://mlsanity.com/paper/2606.02153/) (2026)
- [Reliability-Gated Fusion of Consumer Head and Foot IMUs for Lower-Body 3D Pose](https://mlsanity.com/paper/2609.35764/) (2026)

## Abstract (arXiv)

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.