Object sensing and tracking using electric and magnetic fields allow intelligent interaction, automation, and adaptation in cyber-physical systems. Our approach, called iSense, uses a software-defined collaborative sensing technique for the detection of the type of object when placed on a large surface and tracking its mobility. iSense is cost effective, low power, and scalable, which allows its use over large surfaces. First, we introduce a dual-coil magnetic resonant sensing architecture based on nested coils, i.e., passive (outer) and active (inner) coils, for low-power contactless sensing. Second, a data-driven support vector machine-based approach helps to classify different types of objects using the voltage readings obtained at the passive coil. iSense combines sensed voltage information from multiple different coils spread over the surface with a group-based interference mitigation mechanism between coils for collaborative sensing. We validate our system with real-time prototype and experimental evaluations. We demonstrate the detection of seven different types of objects over three different materials, and real-time detection and tracking of mobile objects including a robot car. Experimental results show that each sensing coil only consumes few milliwatts, i.e., 18× less than inductive sensing and 15× less than classical magnetic resonance sensing, extend sensing depth to 3 cm, and enable tracking on the large surface sensing with more than 90% accuracy for velocity estimation.

中文翻译：

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iSense：通过网络耦合磁共振线圈进行智能物体传感和机器人跟踪


使用电场和磁场进行物体感测和跟踪可实现网络物理系统中的智能交互、自动化和适应。我们的方法称为 iSense，使用软件定义的协作传感技术来检测放置在大表面上的物体类型并跟踪其移动性。 iSense 具有成本效益、低功耗和可扩展性，因此可以在大表面上使用。首先，我们介绍一种基于嵌套线圈的双线圈磁共振传感架构，即无源（外部）和有源（内部）线圈，用于低功耗非接触式传感。其次，基于数据驱动的支持向量机的方法有助于使用在无源线圈处获得的电压读数对不同类型的物体进行分类。 iSense 将来自分布在表面上的多个不同线圈的感测电压信息与线圈之间基于组的干扰减轻机制相结合，以实现协作感测。我们通过实时原型和实验评估来验证我们的系统。我们演示了通过三种不同材料检测七种不同类型的物体，以及包括机器人汽车在内的移动物体的实时检测和跟踪。实验结果表明，每个传感线圈仅消耗几毫瓦，即比电感传感低18倍，比经典磁共振传感低15倍，将传感深度扩展至3厘米，并可实现90%以上的大表面传感跟踪速度估计的准确性。