用手机手表的UWB和惯性数据，无须训练就能精准估测手臂姿势。
SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU Data
- 融合手机与手表的UWB绝对距离和IMU相对数据，互补防漂移。
- 手腕和肘关节定位中位误差仅11.0厘米，无需用户训练数据。
- 适合健身、康复、增强现实等场景，隐私友好且硬件通用。
准确追踪用户手臂姿态在健身、康复、增强现实输入、生活记录和上下文感知助手等场景中具有重要价值，但现有技术要么依赖摄像头（引发隐私问题），要么需要多个可穿戴惯性传感器或标记点。本文提出一种基于市售智能手机与智能手表的纯软件方案，利用设备内置的超宽带（UWB）功能获取两设备间的绝对距离，有效补充惯性测量单元（IMU）数据的相对性和漂移缺陷。通过实测验证，该方法可在不依赖用户训练数据的条件下，实现手腕和肘关节位置估计，中位误差仅为11.0厘米，具备实用性与可扩展性。
原文摘要 · Abstract (English)
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 \hl{median positional error of 11.0~cm}, without the user having to provide training data.
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