# SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU Data

arXiv: https://arxiv.org/abs/2509.03451 · HTML: https://mlsanity.com/paper/2509.03451/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 64 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [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)
- [mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs](https://mlsanity.com/paper/1911.09592/) (2019)
- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time](https://mlsanity.com/paper/1810.04703/) (2018)
- [TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors](https://mlsanity.com/paper/2105.04605/) (2021)
- [Upper Body Pose Estimation Using Wearable Inertial Sensors and Multiplicative Kalman Filter](https://mlsanity.com/paper/1909.10376/) (2019)

## Built on by (most cited first, 4 of 4)

- [Group Inertial Poser: Multi-Person Pose and Global Translation from Sparse Inertial Sensors and Ultra-Wideband Ranging](https://mlsanity.com/paper/2510.21654/) (2025)
- [Human Motion Estimation with Everyday Wearables](https://mlsanity.com/paper/2512.21209/) (2025)
- [Garment Inertial Denoiser (GID): Endowing Accurate Motion Capture via Loose IMU Denoiser](https://mlsanity.com/paper/2601.01360/) (2026)
- [ArmPoser: Real-Time, Calibration-Free Arm Pose Estimation from Smartwatch IMU](https://mlsanity.com/paper/2609.08806/) (2026)

## Abstract (arXiv)

The ability to track a user's arm pose could be valuable in a wide range of applications, including fitness, rehabilitation, augmented reality input, life logging, and context-aware assistants. Unfortunately, this capability is not readily available to consumers. Systems either require cameras, which carry privacy issues, or utilize multiple worn IMUs or markers. In this work, we describe how an off-the-shelf smartphone and smartwatch can work together to accurately estimate arm pose. Moving beyond prior work, we take advantage of more recent ultra-wideband (UWB) functionality on these devices to capture absolute distance between the two devices. This measurement is the perfect complement to inertial data, which is relative and suffers from drift. We quantify the performance of our software-only approach using off-the-shelf devices, showing it can estimate the wrist and elbow joints with a hlmedian positional error of 11.0~cm, without the user having to provide training data.