Tracking free-form activity using wifi signals
Tracking free-form activity using wifi signals
复制标题
DOI：
10.1145/3447993.3482857
复制
发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
Yili Ren;Zi Wang;Sheng Tan;Yingying Chen;Jie Yang
中科院分区：
文献类型：
--
作者：
Yili Ren;Zi Wang;Sheng Tan;Yingying Chen;Jie Yang
WiFi人体感测在实现新兴的人机交互应用方面变得越来越有吸引力。相应的技术已经逐渐从多个活动类型的分类发展到更细粒度的3D人体姿态跟踪。然而，现有的基于WiFi的3D人体姿态跟踪仅限于一组预定义的活动。在这项工作中，我们提出了Winect，一个3D人体姿态跟踪系统的自由形式的活动，使用商品WiFi设备。我们的系统通过估计由人体的一组关节组成的3D骨架姿势来跟踪自由形式的活动。特别是，Winect首先通过利用人体反射的信号来识别移动的肢体，并分离每个肢体的纠缠信号。然后，我们的系统跟踪每个肢体，并通过建模肢体和相应关节的运动之间的内在关系来构建身体的3D骨架。我们的评估结果表明，Winect在各种环境下实现了厘米级的自由形式活动跟踪精度。
WiFi human sensing has become increasingly attractive in enabling emerging human-computer interaction applications. The corresponding technique has gradually evolved from the classification of multiple activity types to more fine-grained tracking of 3D human poses. However, existing WiFi-based 3D human pose tracking is limited to a set of predefined activities. In this work, we present Winect, a 3D human pose tracking system for free-form activity using commodity WiFi devices. Our system tracks free-form activity by estimating a 3D skeleton pose that consists of a set of joints of the human body. In particular, Winect first identifies the moving limbs by leveraging the signals reflected off the human body and separates the entangled signals for each limb. Then, our system tracks each limb and constructs a 3D skeleton of the body by modeling the inherent relationship between the movements of the limb and the corresponding joints. Our evaluation results show that Winect achieves centimeter-level accuracy for free-form activity tracking under various environments.