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A Model-Based RF Hand Motion Detection System for Shadowing Scenarios
IEEE Access ( IF 3.9 ) Pub Date : 2020-01-01 , DOI: 10.1109/access.2020.3004513
Zehua Dong , Fangmin Li , Julang Ying , Kaveh Pahlavan

With the explosive growth of mobile computing, new modes of human-computer interaction (HCI) are emerging and becoming feasible. Compared to vision-based systems that require lighting, radio frequency (RF)-based hand motion detection systems are becoming more popular in HCI applications. In real RF hand motion detection scenarios, the line-of-sight between the transmitter (Tx) and receiver (Rx) is usually blocked. Hence, shadowing significantly affects the detection accuracy. To design better RF hand motion detection systems, we propose a simple diffraction and interference model (DIM) to interpret the received signal strength (RSS) variation caused by hand motions in the shadowing scenario. Based on theories of knife-edge diffraction and mutual radio interference, DIM provides a simple theoretical foundation for analyzing the RSS variation with hand size, signal frequency, and Tx-Rx distance. Furthermore, a model-based RF hand motion detection system benefiting from DIM is presented. Unlike existing systems that require a large number of motion features to train a motion classifier, the model-based system achieves training-free motion classification, which has potential for hand motion detection on a real-time basis. Empirical data collected from a vector network analyzer validate our system as well as demonstrate a simple diffraction model can help hand motion detection processing for commonly growing HCI applications.

中文翻译:

用于阴影场景的基于模型的射频手部运动检测系统

随着移动计算的爆炸式增长,新的人机交互(HCI)模式不断涌现并变得可行。与需要照明的基于视觉的系统相比,基于射频 (RF) 的手部运动检测系统在 HCI 应用中变得越来越流行。在真实的 RF 手部运动检测场景中,发射器 (Tx) 和接收器 (Rx) 之间的视线通常会被阻挡。因此,阴影显着影响检测精度。为了设计更好的射频手部运动检测系统,我们提出了一个简单的衍射和干扰模型 (DIM) 来解释阴影场景中手部运动引起的接收信号强度 (RSS) 变化。基于刀口衍射和相互无线电干扰的理论,DIM 为分析 RSS 随手大小、信号频率和 Tx-Rx 距离的变化提供了简单的理论基础。此外,提出了一种受益于 DIM 的基于模型的 RF 手部运动检测系统。与需要大量运动特征来训练运动分类器的现有系统不同,基于模型的系统实现了免训练运动分类,具有实时检测手部运动的潜力。从矢量网络分析仪收集的经验数据验证了我们的系统,并证明了一个简单的衍射模型可以帮助常见的 HCI 应用程序的手部运动检测处理。与需要大量运动特征来训练运动分类器的现有系统不同,基于模型的系统实现了免训练运动分类,具有实时检测手部运动的潜力。从矢量网络分析仪收集的经验数据验证了我们的系统,并证明了一个简单的衍射模型可以帮助常见的 HCI 应用程序的手部运动检测处理。与需要大量运动特征来训练运动分类器的现有系统不同,基于模型的系统实现了免训练运动分类,具有实时检测手部运动的潜力。从矢量网络分析仪收集的经验数据验证了我们的系统,并证明了一个简单的衍射模型可以帮助常见的 HCI 应用程序的手部运动检测处理。
更新日期:2020-01-01
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