A Battery-free Object Localization and Motion SensingPlatform

Yi ZhaoEE Dept.

University of Washingtonyizhao@uw.edu

Anthony LaMarcaIntel Research

anthony.lamarca@intel.com

Joshua R. SmithCSE&EE Dept.

University of Washingtonjrs@cs.washington.edu

ABSTRACTIndoor object localization can enable many ubicomp applica-tions, such as asset tracking and object-related activity recog-nition. Most location and tracking systems rely on eitherbattery-powered devices which create cost and maintenanceissues or cameras which have accuracy and privacy issues.This paper introduces a system that is able to detect the 3Dposition and motion of a battery-free RFID tag embeddedwith an ultrasound detector and an accelerometer. Combiningtags’ acceleration with location improves the system’s powermanagement and supports activity recognition. We charac-terize the system’s localization performance in open space aswell as in a cluttered wet lab environment. The system isused to track real-time location and motion of the tags as wellas recognize actions performed on the objects to which thetag is attached. The median localization accuracy is 7.6cm –(3.1, 5, 1.9)cm for each (x, y, z) axis – with max update ratesof 15 Sample/s using single RFID reader antenna.

Author KeywordsRFID; localization; Object tracking; Activity Recognition

ACM Classification KeywordsC.2.4 Distributed Systems: Distributed Applications

1.INTRODUCTIONAccurate 3D object location is one of the key context infor-mation enabling many smart home and smart office capabil-ities, such as activity inference, asset tracking, and human-robot interaction. Unfortunately, most localization systemsare based on either cameras [10] or battery-powered dis-tributed sensing devices [4, 6]. Camera-based systems cre-ate a host of privacy issues and have trouble localizing trans-parent, reflective and irregular-shape objects. The latter sys-tems typically have better accuracy and identification ability,but don’t scale well due to tag size, battery life and mone-tary and maintenance costs of the active sensors, especiallyin dense localization sensor network. RF wireless powertechnology [3, 5, 11] can power varied sensing devices byavailable RF infrastructure rather than battery, such as pas-sive RFID tags. Those RFID tags are small, cheap and have

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long life-time due to its battery-free, thereby having potentialas low-cost localization system. Besides, the RFID proto-cols are optimized for reading dense tag population, thereforeallowing good scaling. Current RFID localization systemscan provide at best sub-meter level coarse localization usingRF signal strength. However, researchers have to deal withthe trade-off between low accuracy due to RF multi-path andhigh-cost because of using large number of RFID referencenodes [2].

To address these issues, this paper introduces the first battery-free acoustic localization platform based on RFID infras-tructure. In previous work, we demonstrated that a passiveRFID tag augmented with an ultrasonic microphone can ac-curately estimate its range from an ultrasonic transmitter [11].In this work, both our ultrasonic transmitters and receiverswere built-into fully-programmable passive tags (Figure 1)named WISPs (Wireless Identification Battery-free SensingPlatform [9]), which were augmented with either ultrasoundbeacons or ultrasound microphones and accelerometers. Byhaving all of the ultrasonic sensing and actuation in the tag,we can make use of commercial RFID readers to power andcommunicate with the tags. In this paper, we show that thisbasic acoustic ranging capability can be extended to robust,accurate 3D localization in a lab environment. We do thisby having tags perform ultrasound time of flight (ToF) mea-surements to estimate its range to four ultrasonic transmittersin known locations. These ranging estimates infer the tag’s3D location relative to the transmitters by applying a translit-eration method that can mitigate the effect of indirect-pathand multi-path acoustic reflections. The result is a scalable,battery-free 3D localization system with the following fea-tures:

• 7.6cm accuracy overall with (3.1, 5, 1.9)cm accuracy foreach (x,y,z) axis for line of sight stationary tags.

• Average update rate of 15 3D location estimates per second(given 1W RF power and 1.8m test range).

• Maximum range of 3.5m (at best RF sensitive direction).

Figure 1. A battery-free ultrasonic localization tag that is powered andread by RF signals.

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Figure 2. Sensing and communication protocol: in the blue area, theSense WISP is awake; in the white region, it asleep.

• 252 µJ Energy consumption for positioned tag updatingone location measurement.

• Tolerance to ultrasound echo noise.

• Wireless powered acoustic transmitters that allow morefreedom for system set-up.

• Accelerometer data which accompanies location estimates.This enables both motion and activity estimation as well asimproves tag’s power management

2 SYSTEM ARCHITECTUREOur system is designed to allow an customized RFID tag todetect the time of arrival (ToA) of ultrasound sending fromfour ultrasound transmitters, and to measure its 3-axis accel-eration data. The tag will then send back these results back toone RFID reader in range using its Electronic Product Code(EPC) (tag’s ID). Finally, the RFID reader will report all tagreadings to a client to compute the tag-to-transmitter distanceranges, and from that estimate the tag’s final 3D location andmotion state. To simplify the explanation, we use a basic twotags system to demonstrate how to localize a target tag us-ing single RFID tag, antenna and reader. The basic systemconsists of three hardware parts as follows and the design ofhardware can be found in previous work [11]:

2.1 Sense WISPThe Sense WISP is the target tag to be positioned, which isa battery-free programmable RFID tag (Figure 1) augmentedwith an ultrasound detector and 3D accelerometer. This smalltag is compatible with RFID UHF EPC Gen2 protocol. Weare intended to use commercial RFID reader and another ref-erence RFID tag(called Spy WISP) to estimates the 3D lo-cation of the Sense WISP. The Spy WISP sent ultrasoundfrom its four ultrasonic beacons. All RFID tag mentionedin this work is wireless powered by 1W RF signals sent fromsingle nearby RFID reader antenna. During the step of theRFID protocol called the ”Inventory Round”, the Sense WISPstarts a timer in anticipation of the first ultrasound pulse be-ing simultaneously sent from the Spy WISP and then mea-sures the Time of arrival (ToA) of all four sent ultrasound

signals. Time-division multiplexing is used to schedule 4 ul-trasound transmission and detection within one RFID inven-tory round. To avoid interference between ultrasound signalsfrom different emitters in different RFID reading rounds, theSpy WISP confines all of its ultrasound signals to a 10 mswindow. This is the minimum “inventory period” neededby the RFID reader to update one Sense WISP EPC read-ing. The intervals between ultrasound pulses are chosen tobe long enough that ultrasound collisions cannot occur. Afterultrasound measurement, the Sense WISP samples its 3D ac-celeration and send all those data back to the reader. The databeing sent encodes the RFID readings along with tag’s uniqueID (that is the Electronic Product Code (EPC))(Figure 2). Theaccelerometer in the Sense WISP has built-in activity and in-activity detection circuitry and can measure up to ±2G with16mG sensitivity.

2.2 Spy WISPThe Spy WISP RFID tag is in many ways like the SenseWISP tag: It is also a passive RFID tag and can understandand follow the EPC protocol. Unlike the Sense WISP, how-ever, the Spy WISP tag does not acknowledge any radio re-sponses to the RFID reader, nor does perform any sensing. In-stead, the Spy WISP is equipped with 4 25kHZ narrow bandultrasound beacons at known location. The Spy WISP’s func-tion is to transmit an ultrasonic pulse from each beacons at atime known to the Sense WISP (Figure 2). The Spy WISPsynchronizes the Sense WISP by listening a hard-coded in-formation in RFID command sent from reader to Sense WISPin each tag inventory round. The details of synchronizationmethod can be found in previous paper [11]. In a brief, theToA measurements are achieved using the EPC protocol it-self: Both the Sense WISP and Spy WISP listen for the startof the ”Inventory Round” of the EPC protocol. On observ-ing the signal, the Spy WISP emits a sequence of ultrasoundpulses at known intervals. The Sense WISP using this sig-nal to start a time and being listening for the ultrasonic chips.Since the EPC protocol signals are sent via radio, both tagswill receive and recognize the signal at the nearly the sametime and from the perspective of acoustic wave speeds, anydifference is negligible. Since the Spy WISP does not sendany radio replies, it does not interfere with any tags replyingto the reader’s read requests. As it turns out, the transmissionof ultrasound makes the Spy WISP more power hungry thanSense WISP, it thus it has to be placed within 1 m of the RFIDantenna. The result, however, is an extremely flexible systemwith mobile, battery-free tags along with a small number ofbattery and wire-free reference tags to be placed in the envi-ronment, independent from the reader.

2.3 RFID Reader and Client ApplicationWe use an Impinj EPC Gen2 UHF reader to power and com-municate with the Sense WISP by issuing inventory com-mands. By sending valid inventory commands to the SenseWISP, the reader helps to triggers the acoustic transmissionfrom transmitters in the Spy WISP and detection of the ToA inSense WISP. In response, the Sense WISP will passively re-flect back to reader its EPC information (include Sense WISP

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ID, 4 ToA measurement results, 3 axis acceleration data, mo-tion activity state from previous inventory round) (Figure 2),which is used for inferring tag’s location and motion state.

SOFTWARE DESIGN

3.1 3D Location CalibrationIn general, the time of arrival of ultrasound signals in a givenenvironment is linearly proportional to the distance it prop-agated. However, the hardware and sensor introduces somesynchronization error. Therefore, we use a first order poly-nomial line fit model to matching Sense WISP-to-transmitterdistance dTi and its ToA measurement result TRi . The line fitmodel can be expressed as dTi = ai × TRi + bi(i = 1, 2, 3).Using this model, the small RF synchronization offset, SenseWISP detection clock offset can be modeled in bi, and thespeed of sound variance can be modeled in ai. After that, wecan calculate the position of Sense WISP with these calcu-lated distances dTi and transmitter position(xi, yi, zi) using atrilateration algorithm [7]. Besides, a time and tag’s motionweighted Kalman filter is used to filter out measurement andenvironmental noise. A python based RFID localization ap-plication GUI is developed to visualize real time tag trackingresult. (The GUI uses a python reader client packages from[8])

3.2 Power OptimizationThe firmware and hardware of the Sense and Spy WISP areoptimized for low power and duty cycled during RFID com-munication and ultrasound detection (Figure 2). The SenseWISP only consumes about 80 µW during a wake up cycleand 10 µW for a sleep cycle. As a result, the system is ableto more accurately track static or slow-moving tags than onesmoving quickly. As a further optimization, stationary tagsdon’t need to perform their location estimation continuallysince they are not moving. We use the accelerometer data onSense WISP to implement this power-savings. Once the tagis detected to be stationary, a Sense WISP will increase itssleep cycle length, once motion is resumed, will increase thelocalization update rate to track its new position.

4 EXPERIMENT AND PERFORMANCEWe test the system with single Spy WISP and single SenseWISP to be located in a 1.2m ∗ 1.2m ∗ 1.8m open space andtest with more Sense WISPs in a Wet Lab later. The SpyWISP’s four ultrasound transmitters are placed at the 4 ver-tices of 15cm-long square with known location which are inline of sight for the Sense WISP. We use only one reader pro-duces 1W power with single antenna (6dBi gain), which canpower up Sense WISP up to 3.5m away in best sensitive direc-tion. The total energy used for Sense WISP updating one 3Dlocation is below 252µJ . The experimental results are shownin Figures 3 and 4. In theory, using the RFID synchronizationand acoustic TDMA method [11], the system can support lo-calizing up to 65535 Sense WISPs, however, the localizationupdate rate for each tag will relatively decrease if the systemscales up.

Accuracy: Because of the geometry setup of the ultrasoundstransmitters, the system can obtain better accuracy in z axis,

Figure 3. Free space localization performance: CDF of localization errordistribution, in percentage and absolute units.

which is perpendicular to the planes of transmitters. 3D ge-ometry setup of those transmitters can improve the accuracyfor other axis, however, the ground truth measurement forthat is challenging for experiment. Figure 4 shows that ac-curacy decreases as the Sense WISP-to-transmitters distanceor ultrasound angle of arrival increase. Because the attenu-ation of ultrasound and directional ultrasound sensor intro-duces non-liner noise which can not be modeled well usingline-fitting model at those circumstance. The average accu-racy is (3.1, 5, 1.9)cm for each (x, y, z) direction within test-ing range.

Range: The system maximum localization range is 3.4m(when x = 0, y = 0, z = 3.4m). The range of the systemis limited by the amount of available power for Sense WISP,that means when the Sense WISP is out of the range, it is notable to powered up or complete one detection.

Update Rate: The update rate for this system is limited bythe available RF power for Sense WISP, its energy harvest-ing efficiency and RFID protocol (maximum 45 S/s for SenseWISP), and is 5S/s 31S/s for this experiment. It will de-crease as the population of Sense WISP increase. The averageupdate rate is 15 S/s and can be adjusted by changing the sleeptime of either Spy WISP and Sense WISP. Spreading high-gain omni-directional antenna can offer better power densityin the space, thereby improve the update rate if needed.

Acceleration detection: the system is able to detect the move-ment of Sense WISP tagged object. By grasping the taggedobjects from table, the system have 90% confidence of de-tecting the movement for 15 test samples within 1m tag-to-reader range. In order to capture those fast movement of tag,like being grasped, the reader have to read Sense Tag with arelatively higher rate than the movement, which is limited byRFID protocol and available RF power near the Sense WISP.For more static tags whose orientation is known to the sys-tem, our system can detect the change of its orientation evenat a lower Sense WISP update rate up to 1.8m range usingsingle antenna. More antennas in test space will also help.Therefore the platform have distinguished motion detectionability varied by available RF power in the space.

APPLICATION AND IMPLEMENTATIONOne high-value application of context-awareness (and one ofthe largest challenges facing science) is the documentationand replication of scientific lab experiments.A 2012 study,for example, showed that 47 of 53 key cancer biology results

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Figure 4. Localization accuracy in 3D open space: sub-figures 1− 3 show results at z = 94cm, z = 134, z = 174cm. The 4 ultrasound sources in areplaced at the vertices of the 16cm × 14cm cyan squre in the center of the chess plane (20cm/grid, origin at the center). The yellow ball is the groundtruth location with sensor’s acutal size (radius= 8mm). The blue ball shows the localization result; size represents standard deviation on each axis.

Figure 5. Wetlab. We are exploring the use of the localization tags foractivity inference in a wetlab scenario.

Figure 6. Wetlab localization performance: CDF of localization errordistribution, in percentage and absolute units. The performance is notsignificantly different than the freespace case.

could not be replicated, in some cases even with the help ofthe original researchers [1]. Part of what makes documenta-tion and replication difficult is the large number and subtletythe many steps performed during a lab experiment. Anotherfactor is that often documentation is performed after the factsince both hands are needed to perform experiment, and labtechnicians often wear protective gloves that make writing ortyping difficult.

Accordingly, we are using a self-documenting smart wet labas a guiding application of our location system, using a clut-tered lab bench as our deployment environment (Figure 5).We are RFID-tagging key objects in the lab (e.g.: bottles ofreagents and glass containners) and tracking their locationand motion as experiments progress. Using accurate, high-update rate location readings from our tags, we aim to notonly infer which specific objects are present on the bench, but

when, where and how they are used. With this detailed objectlocation and motion data, coupled with representations of theexperimental protocols we hope to be able to infer and docu-ment the objects being used, actions performed on/with themand recognize the experiment being performed.

We implement this system in an (0.8m × 0.5m × 1.2m) labbench (one table and one shelf above) in the smart wet lab(Figure 5) and attach Sense WISP tag to several bottles ofreagents, the system can then infer the current state and ac-tivity of those bottles by measuring its location and move-ment(such as it is on the shelf or in media preparing area) andinfer their activity and state. An experiment is designed toreport the current static location of 1 object, 2 objects or threeobjects in 14 different locations. The average accuracy canbe seen from Figure 6.

Another experiment is conducted to try to recognize a pouringevent, when the Sense WISP tagged bottle is pouring into an-other container in order to alarm people if the reagents in thetagged bottle is pouring into the wrong container. Two iden-tical container with the same distance to the tagged bottle areused as the destination of the pouring. During the test, theresearcher grasps the tagged bottle from the table and pour-ing its liquid into one of the two containers, which location isknown, once any movement activity of the bottle is detectedand the bottles orientation is turning to horizontal, the systemcomputes the current location of the bottle while it is pouring.The system then can estimate which container the tagged bot-tle is pouring into by find which container it is mostly close to.Placing the pair of container in 5 different places, the systemcan detect the correct pouring destination container with 90%confidence for 8 times the pouring event for each location.

CONCLUSION AND FUTURE WORKUsing simple RFID infrastructure setting for wireless powerand communication, our system is able to estimate the 3Dposition of a battery-free ultrasound sensor tag with 8cm ac-curacy in an acoustically complex indoor environment. Thesame tags also provide acceleration sensing. The systemis designed to support activity recognition and tracking, byjointly interpreting the data from these sensors. Future workwill improve localization performance, scale up the numberof sensors with multiple antennas, and test the system’s effec-tiveness in activity recognition applications.

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ACKNOWLEDGMENTThis work is funded by Intel Science and Technology Centerfor Pervasive Computing (ISTC-PC)and NSF award numberCNS 1305072.

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