Run Zhao

PRMS: Phase and RSSI based Localization System for Tagged Objects on Multilayer with a Single Antenna

In the future, libraries and warehouses will gain benefits from the spatial location of books and merchandises attached with RFID tags. Existing localization algorithms, however, usually focus on improving positioning accuracy or the ordering one for RFID tags on the same layer. Nevertheless, books or merchandises are placed on the multilayer in reality and the layer of RFID tagged object is also an important position indication. To this end, we design PRMS, an RFID based localization system which utilizes both phase and RSSI values of the backscattered signal provided by a single antenna to estimate the spatial position for RFID tags. Our basic idea is to gain initial estimated locations of RFID tags through a basic model which extracts the phase differences between received signals to locate tags. Then an advanced model is proposed to improve the positioning accuracy combined with RF hologram based on basic model. We further change traditional deployment of a single antenna to distinguish the features of RFID tags on multilayer and adopt a machine learning algorithm to get the layer information of tagged objects. The experiment results show that the average accuracy of layer detection and sorting at low tag spacing (

TTBA: An RFID-based Tracking System for Two Basic Actions in Free-Weight Exercises

2\sim8

A novel accurate synthetic aperture RFID localization method with high radial accuracy

cm) are about 93% and 84% respectively.

RFlow-ID: Unobtrusive Workflow Recognition with COTS RFID

People have been spending much time on free-weight exercises that can strengthen the muscles, connective tissues and tendons. To decrease the risk of injury and reap the benefit of free-weight exercises, a monitoring system which helps users exercise scientifically is necessary. Wearable sensors or changes of Radio Frequency (RF) signal have been exploited for activity sensing in prior work. However, wearable sensors methods are intrusive and may bother users, while the RF-based methods require training data and fail to extract fine-grained features of each action. Therefore, our goal is to design a system which is non-intrusive, privacy-insensitive and non-training. Tracking the free-weight equipment may meet the challenge of large accuracy loss, only by extending existing RF-based 2D tracking methods to 3D space. Instead, regarding the 3D moving as 1D moving simplifies the problem. We find that most actions in free-weight exercises can be divided into two kinds, circular and vertical motions. Therefore, this paper proposes a RFID-based system, TTBA, to track free-weight equipment instrumented with passive RFID tags. We implement a low-cost prototype of TTBA to recognize and track the two basic actions. Extensive experiments show that TTBA achieves high tracking accuracy for both motions, even mm-level accuracy for the vertical motion. And the potential TTBA has to achieve an assessment system is also showed in the practical evaluation.

TagController: A Universal Wireless and Battery-free Remote Controller using Passive RFID Tags

Internet of Things (IoT) is rather prevalent in many manufacturing and smart city applications, while localization is a premise for many other processes, varying from ordering objects in manufacturing lines to locating books on bookshelves. Radio Frequency Identification (RFID) based localization is of great interest in many IoT applications. Synthetic aperture RFID, due to its anti-noise capability and robustness against multipath distortion, is becoming a rising star in the field of localization. Existing systems achieve finer lateral resolution, whereas their radial accuracy is limited by the narrow bandwidth of RFID signal. In this paper, we present a novel synthetic aperture RFID localization method which combines RFID phase based ranging with synthetic aperture technology, to achieve a higher radial accuracy than the existing systems. With only one reader antenna and one 1-dimensional (1D) trajectory, a synthetic array is constructed to get an accurate localization result both in lateral and radial direction. Its core idea is to make full use of the coherence of all multi-frequency phase data and merge them into a unique ranging based likelihood function. To improve the accuracy, the relative phase is leveraged to eliminate phase offsets caused by the reader antenna, and the phase deviation from the angle-of-arrival response is calibrated by pre-processing. Then a weighted enhancement is fully exploited to further improve the localization performance. We evaluate its performance with commercial-off-the-shelf (COTS) RFID devices and the results show that it achieves median accuracy of 3cm in both lateral and radial direction. This novel promising method is suitable for locating tags placed densely in many IoT applications, such as test tubes in hospitals.

Passive radio frequency identification sorting for dense objects on high-speed conveyor belts

Workflow recognition is a key technique in the field of activity recognition with benefits of monitoring the step being performed in the workflow, detecting the missing step, and providing assistance to the performer of the workflow, among others. In this paper, we present an unobtrusive workflow recognition system called RFlow-ID, which is the first device-free, battery-free and privacy-preserving workflow recognition system based on RFID technique. RFlow-ID perceives the use and movement of associated objects in the workflow using fine-grained phase information extracted from low-level RF signal, and infers the most likely sequence of workflow activities via a VQ-HMM model. We implement RFlow-ID on COTS RFID devices and evaluate it through a common biomedical experiment. The results validate the high recognition accuracy and robustness of our system.

An advanced method for tag ordering and localization in mobile RFID systems

Innovative Human Machine Interface technologies are fundamentally reshaping the way people live, entertain and work. Passive RFID tags, benefiting from its wireless, inexpensive and battery-free sensing ability, are gradually being applied in new-style interaction interfaces, ranging from virtual touch screen to 3D mouse. This paper presents TagController, a universal wireless and battery-free remote controller with two types of interactive actions. The key insight is that the fine-grained phase information extracted from RF signals is capable of perceiving various actions. TagController can recognize 10 actions without any training or prestored profiles by executing a sequence of functional components, i.e. preprocessor, action detector and action recognizer. We have implemented TagController with COTS RFID devices and conducted substantial experiments in different scenarios. The results demonstrate that TagController can achieve an average recognition accuracy of 95.8% and 94.3% in the scenarios of one and two remote controllers, respectively, which promises its feasibility and robustness.

SGRS: A sequential gesture recognition system using COTS RFID

In many logistics applications, densely placed objects on high-speed conveyor belts are attached with passive tags for automatic identification and sorting to work more efficiently. Traditional localization or sorting methods cannot effectively work in such scenarios due to complex environments. In this article, we propose a new passive radio frequency identification sorting system for dense mobile tags on high-speed conveyor belts by utilizing the output phase. At first, a relative motion model is utilized to get the zero point time when the object passes the radio frequency identification portal. Then, the coarse-grained time and the corresponding speed of the conveyor belt are obtained by the phase curve fitting. Finally, the inverse synthetic aperture radio frequency identification is used to get the fine-grained zero point time, which is realized based on the holographic image reconstruction. And the particle filter is utilized to get a significant reduction of computational burden. The proposed method is implemented with commercial-off-the-shelf devices, and the evaluation results in various scenarios show our system can achieve an average accuracy of 97% with the tag density of 10/m and at a speed of 4 m/s.