Han Ding

FEMO: A Platform for Free-weight Exercise Monitoring with RFIDs

Regular free-weight exercise helps to strengthen the bodys natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes (e.g., WiFi and Blue tooth) for activity sensing, recognition and countingetc.. However, none of them have incorporate three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system responds to these demands, providing an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1): since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other and serves as a reliable signature for each activity. 2): the Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of each performed activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities and users, and provide valuable feedbacks for activity alignment.

CBID: A Customer Behavior Identification System Using Passive Tags

Different from online shopping, in-store shopping has few ways to collect the customer behaviors before purchase. In this paper, we present the design and implementation of an on-site Customer Behavior Identification system based on passive RFID tags, named CBID. By collecting and analyzing wireless signal features, CBID can detect and track tag movements and further infer corresponding customer behaviors. We model three main objectives of behavior identification by concrete problems and solve them using novel protocols and algorithms. The design innovations of this work include a Doppler effect based protocol to detect tag movements, an accurate Doppler frequency estimation algorithm, a multi-RSS based tag localization protocol, and a tag clustering algorithm using cosine similarity. We have implemented a prototype of CBID in which all components are built by off-the-shelf devices. We have deployed CBID in real environments and conducted extensive experiments to demonstrate the accuracy and efficiency of CBID in customer behavior identification.

Human object estimation via backscattered radio frequency signal

In this paper, we propose a system called R# to estimate the number of human objects using passive RFID tags but without attaching anything to human objects. The idea is based on our observation that the more human objects are present, the higher the variance in the RSS values of the tag backscattered RF signal. Thus, based on the received RF signal, the reader can estimate the number of human objects. R# includes an RFID reader and some (say 20) passive tags, which are deployed in the region that we want to monitor the number of human objects, such as the region in front of a painting. The RFID reader periodically emits RF signal to identify all tags and the tags simply respond with their IDs via C1G2 standard protocols. We implemented R# using commercial Impinj H47 passive RFID tags and Impinj reader model R420. We conducted experiments in a simulated picking aisle area of the supermarket environment. The experimental results show that R# can achieve high estimation accuracy (more than 90%).

Device-free detection of approach and departure behaviors using backscatter communication

Smart environments and security systems require automatic detection of human behaviors including approaching to or departing from an object. Existing human motion detection systems usually require human beings to carry special devices, which limits their applications. In this paper, we present a system called APID to detect arm reaching by analyzing backscatter communication signals from a passive RFID tag on the object. APID does not require human beings to carry any device. The idea is based on the influence of human movements to the vibration of backscattered tag signals. APID is compatible with commodity off-the-shelf devices and the EPCglobal Class-1 Generation-2 protocol. In APID an commercial RFID reader continuously queries tags through emitting RF signals and tags simply respond with their IDs. A USRP monitor passively analyzes the communication signals and reports the approach and departure behaviors. We have implemented the APID system for both single-object and multi-object scenarios in both horizontal and vertical deployment modes. The experimental results show that APID can achieve high detection accuracy.

VADS: Visual attention detection with a smartphone

Identifying the object that attracts human visual attention is an essential function for automatic services in smart environments. However, existing solutions can compute the gaze direction without providing the distance to the target. In addition, most of them rely on special devices or infrastructure support. This paper explores the possibility of using a smartphone to detect the visual attention of a user. By applying the proposed VADS system, acquiring the location of the intended object only requires one simple action: gazing at the intended object and holding up the smartphone so that the object as well as users face can be simultaneously captured by the front and rear cameras. We extend the current advances of computer vision to develop efficient algorithms to obtain the distance between the camera and user, the users gaze direction, and the objects direction from camera. The objects location can then be computed by solving a trigonometric problem. VADS has been prototyped on commercial off-the-shelf (COTS) devices. Extensive evaluation results show that VADS achieves low error (about 1.5° in angle and 0.15m in distance for objects within 12m) as well as short latency. We believe that VADS enables a large variety of applications in smart environments.

Verifiable smart packaging with passive RFID

Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes.

MISS: Multi-dimensional Information Sensing Surveillance for Cold Chain Logistics

Cold chain logistics is of great importance for transporting temperature and vibration sensitive products. However, fine-grained surveillance remains challenging in cold chain logistics, due to the lack of multi-dimensional information that reflects the status of monitored objects. In this paper, we propose a multi-dimensional information sensing surveillance framework, named MISS, to timely detect abnormal events that occur in cold chain logistics. The sensed information, including temperature and acceleration etc., can be integrated to provide accurate detection on the abnormal events. By adopting minimum entropy and AVC algorithms, we can classify various status in cold chain logistics. We further perform real implementations and evaluations on a prototype, and examine the effectiveness of MISS.

Secure and Efficient Control Transfer for IoT Devices

The prevalence of Internet of Things (IoT) requires flexible and fine-grained controls over the IoT devices. Existing works rely on specific controllers or programs to remotely control IoT devices, which is inefficient to support intelligent control in IoT environments. In contrast, utilizing a common portal device, for example, smart phone, to control variant IoT devices is a promising solution. However, it is challenging to guarantee the security when transferring the control of IoT devices. In this paper, we design a lightweight protocol to enable secure control transfer among the IoT devices, portal devices, and backend server. We demonstrate the effectiveness of our protocol in defending against mainstream attacks. Experimental results show the efficiency of our protocol in the authentication and key-updating during the control transfer.

HMRL: Relative Localization of RFID Tags with Static Devices

Passive Radio Frequency Identification (RFID) tags have been widely applied in many applications, such as logistics, retailing, and warehousing. In many situations the relative locations of objects are more important than their absolute locations. However, state-of-art relative localization methods need continuing movement of tags and readers, which limit the application domain and scalability. In this paper, we propose a relative localization approach for passive tags that requires no device movement. Instead, our method utilizes signal changes caused by arbitrary movement of human beings around tags, who carry no device. Hence our method is called Human Movement based Relative Localization (HMRL). The basic idea of HMRL is that when people pass between reader antenna and tags, the received signal strength will change. By observing the time-series RSS changes of tags, HMRL can obtain the order of tags along a specific horizontal direction. HMRL can also get the order of tags in a vertical direction using hyperbolic positioning. We implement HMRL with commodity off-the-shelf RFID devices. The experimental results show that HMRL achieves high accuracy for relative localization of passive tags.

RFIPad: Enabling Cost-Efficient and Device-Free In-air Handwriting Using Passive Tags

An important function of smart environments is the ubiquitous access of computing devices. In public areas such as hospitals, libraries, and airports, people may want to interact with nearby computing systems to get information, such as directions to a hospital room, locations of books, and flight departure/arrival information. Touch screen based displays and kiosks, which are commonly used today, may incur extra hardware cost or even possible germ and bacteria infection. This work provides a new solution: users can make queries and inputs by performing in-air handwriting to an array of passive RFID tags, named RFIPad. This input method does not require human hands to carry any device and hence is convenient for applications in public areas. Besides the mobile and contactless property, this system is a cost-efficient extension to current RFID systems: an existing reader can monitor multiple RFIPads while performing its regular applications such as identification and tracking. We implement a prototype of RFIPad using commercial off-the-shelf UHF RFID devices. Experimental results show that RFIPad achieves >91% accuracy in recognizing basic touch-screen operations and English letters.