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, we combine signal separation and joint movement modeling to achieve free-form activity tracking. Our system first identifies the moving limbs by leveraging the two-dimensional angle of arrival of the signals reflected off the human body and separates the entangled signals for each limb. Then, it 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 is environment-independent and achieves centimeter-level accuracy for free-form activity tracking under various challenging environments including the none-line-of-sight (NLoS) scenarios.
翻译:WiFi 人类感知在促成新兴的人类-计算机互动应用方面越来越具有吸引力。相应的技术已经逐渐从对多种活动类型的分类和联合移动模型进行更精细的跟踪,逐渐从3D人造形体的3D人造形体的现有WiFi 3D人造形体跟踪系统限于一系列预先界定的活动。在这项工作中,我们展示了3D人造形跟踪系统Winect,该系统用于利用3WiFi设备进行自由成形活动。我们的系统通过估计由一组人体连接组成的3D骨骼结构来跟踪自由成形活动。特别是,我们把信号分离和联合移动模型结合起来,以实现自由成形活动的跟踪。我们的系统首先通过利用从两维角度对人体反射出的信号的到达进行定位,将每个肢体的缠绕信号分离出来。然后,通过模拟肢体运动运动运动与相应连接之间的内在关系来跟踪身体的3D骨架体形体。我们的评估结果显示,Winect是环境依赖环境的,并且实现了在各种富有挑战的环境下(包括无线-视野环境)进行自由成形活动的跟踪活动时达到厘米的精确度。