ArmPoser：基于智能手表IMU的实时、免校准手臂姿态估计

中文摘要 AI 辅助

手臂姿态估计可实现健身、扩展现实输入、康复和生活记录等应用。先前基于智能手表的方法依赖校准姿势和预处理流程，将原始IMU测量转换为标准化训练格式。这些步骤阻碍了在日常环境中的部署，并因校准不完善和传感器漂移而引入误差。我们提出ArmPoser，一种使用单个智能手表IMU的免校准手臂姿态估计系统。我们的核心贡献是直接在消费级智能手表固有的参考系中训练模型，使学习与部署设备产生IMU数据的方式保持一致。通过操作设备原生轴，ArmPoser消除了先前工作中所需的坐标变换、显式对齐和骨骼偏移校准。我们进一步通过手表佩戴位置和手臂形态的物理合理变化来增强训练，以考虑用户特定变异性。ArmPoser还包含一个佩戴配置模块，可推断前臂或后臂放置位置以及表冠方向。我们在公共基准测试和一项使用watchOS和Android智能手表的10名参与者、30项活动研究中评估姿态估计，其中ArmPoser在无需任何用户校准的情况下达到或超过校准基线。

英文摘要

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.

发表机构

• Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
• University of Chicago（芝加哥大学）
• Northwestern University（西北大学）

机构由 AI 辅助整理，请以论文原文为准。