无需校准，仅用智能手表即可实时估测手臂姿态。
ArmPoser: Real-Time, Calibration-Free Arm Pose Estimation from Smartwatch IMU
- 直接在手表原生坐标系训练模型，避免坐标转换和校准步骤。
- 在10人30项活动中表现媲美甚至超越需校准的基线方法。
- 支持不同佩戴方向和手臂形态，适合日常真实场景使用。
手臂姿态估计可应用于健身、扩展现实输入、康复和生活记录。以往基于智能手表的方法依赖校准姿态和预处理流程，将原始惯性测量单元（IMU）数据转换为标准化训练格式，这限制了日常部署，并因校准不准确和传感器漂移引入误差。本文提出ArmPoser，一种仅使用单个智能手表IMU的免校准手臂姿态估计系统。核心贡献在于在消费级手表的原生参考坐标系中直接训练模型，使学习过程与设备实际产生的数据对齐。通过在设备原生轴上运行，ArmPoser消除了坐标变换、显式对齐和骨骼偏移校准的需求。我们还通过物理合理的手表佩戴位置和臂部形态变化增强训练，以应对用户个体差异。系统包含一个穿戴配置模块，可推断前臂的前后朝向及表冠方向。我们在公开基准和一项包含10名参与者、30项活动的实验中评估，结果表明，在watchOS和Android手表上，无需任何用户校准，其性能达到或超过已校准基线。
原文摘要 · Abstract (English)
Arm pose estimation enables applications in fitness, extended reality input, rehabilitation, and life logging. Prior smartwatch-based approaches rely on calibration poses and preprocessing pipelines that transform raw IMU measurements into standardized training formats. These steps hinder deployment in everyday settings and introduce errors due to imperfect calibration and sensor drift. We present ArmPoser, a calibration-free arm pose estimation system using a single smartwatch IMU. Our central contribution is training models directly in the reference frame native to consumer smartwatches, aligning learning with how IMU data is produced by deployed devices. By operating on device-native axes, ArmPoser removes the need for coordinate transformations, explicit alignment, and bone-offset calibration used in prior work. We further augment training with physically grounded variations in watch placement and arm morphology to account for user-specific variability. ArmPoser also includes a wear-configuration module that infers anterior or posterior forearm placement and crown orientation. We evaluate pose estimation on public benchmarks and on a 10-participant, 30-activity study using watchOS and Android smartwatches, where ArmPoser matches or exceeds calibrated baselines without any user calibration.
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