WheelPoser：面向轮椅使用者的基于稀疏IMU的人体姿态估计
WheelPoser: Sparse-IMU Based Body Pose Estimation for Wheelchair Users
AI总结：
针对现有姿态追踪方法不适用于轮椅使用者的问题，提出仅用4个IMU的WheelPoser实时姿态估计系统，构建专用数据集训练，精度达同类稀疏IMU系统三倍以上，兼具实用性与应用潜力。
摘要：
尽管研究人员已广泛研究了多种移动中人体姿态追踪方法，但大多数先前工作未考虑轮椅使用者，导致追踪性能不佳。轮椅使用者可从这类姿态信息中大幅获益，用于预防损伤、监测健康状况、识别环境无障碍障碍，以及与游戏和VR体验交互。在本研究中，我们提出WheelPoser，这是一款专门为轮椅使用者设计的实时姿态估计系统。我们的系统仅在使用者身体和轮椅上策略性地放置4个IMU（惯性测量单元），相比使用摄像头和密集IMU阵列的先前系统实用性大幅提升。WheelPoser追踪轮椅使用者姿态的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，性能是同类稀疏IMU系统的三倍以上。为训练我们的系统，我们收集了全新的WheelPoser-IMU数据集，包含167分钟的轮椅使用者IMU传感器与动作捕捉配对数据，涵盖推进、减压等轮椅特有动作。最后，我们探讨了该系统可实现的潜在应用场景，并讨论了未来研究方向。开源代码、模型和数据集可在以下地址获取：https://github.com/axle-lab/WheelPoser。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
