用4个传感器实现轮椅用户实时姿态估计，精度超现有方法3倍。
WheelPoser: Sparse-IMU Based Body Pose Estimation for Wheelchair Users
- 仅用4个分置的惯性传感器，替代摄像头和密集阵列。
- 关节角度误差14.30度，位置误差6.74厘米，性能超同类系统3倍以上。
- 专为轮椅动作设计，适合医疗监测与无障碍交互应用。
尽管已有大量研究关注动态姿态追踪，但多数工作未考虑轮椅使用者，导致性能不佳。轮椅用户可从中受益于伤害预防、健康监测、环境无障碍识别及游戏与虚拟现实交互。本文提出WheelPoser，一种专为轮椅用户设计的实时姿态估计系统。该系统仅需用户身体与轮椅上四个精心布置的惯性测量单元（IMU），比依赖摄像头或密集IMU阵列的系统更实用。实验表明，其平均关节角度误差为14.30度，平均关节位置误差为6.74厘米，优于现有稀疏IMU系统三倍以上。为训练系统，我们构建了全新的WheelPoser-IMU数据集，包含167分钟的轮椅用户同步IMU与动作捕捉数据，涵盖推轮、减压等特有动作。最后，我们探讨了系统潜在应用场景并展望未来方向。开源代码、模型与数据集见：https://github.com/axle-lab/WheelPoser。
原文摘要 · Abstract (English)
Despite researchers having extensively studied various ways to track body pose on-the-go, most prior work does not take into account wheelchair users, leading to poor tracking performance. Wheelchair users could greatly benefit from this pose information to prevent injuries, monitor their health, identify environmental accessibility barriers, and interact with gaming and VR experiences. In this work, we present WheelPoser, a real-time pose estimation system specifically designed for wheelchair users. Our system uses only four strategically placed IMUs on the user's body and wheelchair, making it far more practical than prior systems using cameras and dense IMU arrays. WheelPoser is able to track a wheelchair user's pose with a mean joint angle error of 14.30 degrees and a mean joint position error of 6.74 cm, more than three times better than similar systems using sparse IMUs. To train our system, we collect a novel WheelPoser-IMU dataset, consisting of 167 minutes of paired IMU sensor and motion capture data of people in wheelchairs, including wheelchair-specific motions such as propulsion and pressure relief. Finally, we explore the potential application space enabled by our system and discuss future opportunities. Open-source code, models, and dataset can be found here: https://github.com/axle-lab/WheelPoser.
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