[IEEE 2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET) - Ajaccio, France (2013.06.24-2013.06.26)] 2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET) - Localizing a wheelchair indoors with magnetic sensors

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<ul><li><p>Localizing a Wheelchair Indoors with MagneticSensors</p><p>Ruolin Fan, Silas Lam, Oleksandr Artemenko, Emanuel Lin, You Lu, Mario GerlaDepartment of Computer Science</p><p>University of California, Los AngelesLos Angeles, CA 90095, USA</p><p>Email: {ruolinfan, silaslam, emanuel, youlu, gerla}@cs.ucla.edu Ilmenau University of Technology, 98693 Ilmenau, Germany</p><p>Email: oleksandr.artemenko@tu-ilmenau.de</p><p>AbstractIndoor localization with conventional GPS devicesis too difficult because the satellite signals are too weak. Severalalternate schemes have been proposed, from RF signal strength toradio signatures. In this paper we consider the indoor localizationof a wheelchair. This is important to guide the handicapped onpredefined wheelchair-friendly routes in a building, for example.For indoor wheelchair positioning we explore a new odometry-like approach based on a cyclometer. Namely, the system detectswheel rotations from uniformly distributed magnets around thecircumference of the two large wheels. Distance traveled andturning degrees can be computed by analyzing how much thewheels have rotated. This allows the tracking of the path. Thispaper reports the system implementation and preliminary results.</p><p>I. INTRODUCTION</p><p>Localization is the process of knowing ones location, whichis critical for anyone wishing to reach a specific destinationin a timely manner. Global positioning system (GPS) is oneof todays most common solutions for obtaining a positionalfix. Commercial GPS devices, typically used for driving,are readily available from companies such as TomTom andMagellan [1]. Most mobile phones also contain built-in GPSunits. The ubiquitous nature of GPS greatly helps solve thelocalization problem in most cases.</p><p>Unfortunally, GPS is not well suited for the indoors en-vironment for lack of satellite signals [2]. In our research,we consider a specialized problem, the indoor localizationof the physically handicapped, wheelchair bound individuals.Instead of relying on existing non-GPS solutions, such as radioreceivers, accelerometers and even cameras, we created a newsystem that could transform measured wheel rotations into bothdistance and angular displacement. The scheme is inspired bythe odometer. However, an odometer can only measure totaldistance travelled and not angular displacement. Specifically,our solution adds evenly spaced magnets on the two largerwheels of the wheelchair. A reed switch detects when a magnetpassed near it. By performing simple calculations on the rawdata, we can obtain an estimate of the wheelchairs location.</p><p>The main contribution of this work is introduce a newpositioning scheme that does not use previously investigatedsensors. The current system implementation can be summa-rized as follows. Raw data from the magnets is collectedby smartphone at regular intervals through a PHP webpagecombined with JavaScript and MySQL. The data is processed</p><p>and the webpage displays the wheelchairs current location.Error is estimated by comparing our results with physicaltape measurements. Results are very encouraging for straightforward motion at slow to normal speeds. However, we foundthat while turning angles are not accurately estimated, witherrors up to 10 degrees. These errors can be disastrous whenamplified by dead reckoning.</p><p>The rest of the paper is organized as follows. SectionII gives some background information on the problem ofindoor localization in general. This is followed by section IIIwhich gives a brief description on related work on localization.Section IV presents our design and how we translated physicalcharacteristics into formulas for calculation. Sections V and VIdeal with our implementation and evaluation, respectively. Weend with discussion and conclusion in sections VII and VIII,respectively.</p><p>II. BACKGROUND</p><p>As previously stated, GPS is good enough for outdoor usebut does not suit our purposes for the indoors. The medianhorizontal error of mobile-GPS units ranges from 5-8.5 meterswhen tested outdoors [3]. This degree of error is acceptablein an outdoor setting, as the system can still place the user onthe same street or block. In an indoor scenario, however, suchan error may place the user in the wrong room or wrong aislein the grocery store. In addition, this estimation of error inan indoor scenario is actually optimistic because GPSs tend tohave an even larger range of errors on average when operatedindoors, assuming that it can actually obtain a readable signal.</p><p>GPS is based on triangulation from satellites whose po-sition is known. When GPS fails, a possible solution is toredefine other landmarks. One obvious method is to use 3Gand 4G repeaters of known position as landmarks and usesignal strength to approximate distance. Similarly, indoor Wi-Fi access points can be used. Another method often used forpositioning relative to radio access point is radio signaturematching, exploiting the fact that the combination of signalstrength levels received from the station uniquely defines theposition. Other schemes yet use a combination of acousticsignals (say smart phone ring tones) and radio signals to obtaina more precise position fix. Generally, triangulation methodsare quite complex and require previous mapping of the site,acquisition of landmarks and/or sophisticated equipment.</p><p>978-1-4799-1004-5/13/$31.00 2013 IEEE</p><p>2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET)</p><p>141</p></li><li><p>In our application we choose to rely on methods that donot use landmarks. These methods are often categorized asdead reckoning methods. Dead reckoning is the process ofcomputing ones current position based on a previously knownposition and incremental displacement. Because each positionestimate is relative to its previous estimate, errors will magnifyas they accumulate in the calculation. This means that if therewas a previous turning angle error of several degrees at aprevious position, the current estimate will have the turn errorfrom the current calculation added with the previous turn error.In order to correct for this error, dead reckoning works bestwhen alongside a system such as GPS. This would be akin tousing the GPS for positioning whenever a signal is present.</p><p>Inertial navigation systems use computers and sensors tocalculate position via dead reckoning [4]. They are commonlyused in vehicles such as ships, aircraft, submarines, missiles,and spacecraft. Unfortunately, they are inconvenient to use fora single person because it would require a person to carrya device specifically for the purpose of navigation. We wouldlike to avoid this and use whatever a person may already have,such as a wheelchair and smartphone.</p><p>III. RELATED WORK</p><p>Localization is the core of any navigation system. Much ef-fort has been put into the indoor localization problem. Almostall the approaches are based on dead reckoning and utilize asmartphone device for data capture and processing. They canbe grouped into three major classifications: smartphone-basedindoor navigation systems, wardriving, and optical flow.</p><p>Almost all smartphones (especially of the Android orApple variants) contain built-in measurement devices such asaccelerometer, gyroscope, and compass. The measurements ofany combination of these instruments can be utilized to com-pute a position through dead reckoning [5][7]. Gyroscopesmeasure angular momentum, which can be used to obtain abearing. Compasses can also obtain a bearing but by detectingmagnetic fields. Accelerometers measure acceleration, whichcan be used to compute speed. Unfortunately, using only a sin-gle type of measurement is error-prone because of the noise inthe raw data from the sensor. Each of the types of sensors haveits own disadvantages with respect to our scenario. Gyroscopesrequire frequent calibration because their error increases overtime [8]. Compass measurements are difficult to use becauseof the magnetic inference of metallic objects, such as stairwayrails or electronics, which are widespread within buildings.This causes wildly varying readings in bearing that cannotbe relied upon [9]. Accelerometer measurements depend onthe orientation of the phone. This orientation easily changesinside a persons pocket, causing a skew in the data [10]. Inour research, we did not integrate these sensors into our systembecause we wished to analyze the accuracy of our proposedsolution in isolation.</p><p>Wardriving positions an entity according to stationary Wi-Fi hotspots [11]. By pinging surrounding hotspots and mea-suring the signal strength, you can triangulate your location.An inconvenience of wardriving is the inherent requirementfor massive amounts of infrastructure. Large Wi-Fi coverageof a building is not possible without significant cost andmany access points. Many wardriving techniques also require</p><p>calibration of the hotspots, such as a known location of thehotspot (like a latitude/longitude tuple). Even the accuracy ofwardriving is in question, as it also must deal with the problemof signal degradation and interference.</p><p>The third kind of localization is optical flow. It works bycalculating the distance between two images taken at differenttimes. This distance can be analyzed to compute an estimatedspeed of the viewer. The calculations tend to be too CPU-intensive for smartphones, making the program too slow forreal-time applications [12]. One group has succeeded withFlowpath, a system that analyzes H.263 video motion vectorsin real-time [6]. However, the system requires a map, eitherpublically available or user-created, of the building to function.This requirement is not practical for people visiting a buildingfor the first time.</p><p>Our work falls most closely with the smartphone-basedindoor navigation systems. Although we do not utilize any ofthe sensors in the smartphone, we do use its web browsingcapabilities in order to access our server to send/display thedata. Our major contribution is using the physical attributes ofa wheelchair to our advantage in creating an indoor localizationsystem.</p><p>IV. DESIGN</p><p>With a wheelchair, we use features unique to this deviceto help us achieve our goal. Assuming that the wheelchairwas outside at some point in time, we record the movementsof the wheelchair and use them in combination with the lastknown GPS coordinates to find out the actual position ofthe wheelchair. Therefore, from a high level perspective, welocalize the wheelchair using a known initial position and addon vectors to find its new positions. The bulk of the algorithmlies in recording these movements.</p><p>To track the wheelchairs movements, we take advantageof its mode of travel. Since wheelchairs travel by rotationof its wheels, we can then track all movements by countingthe rotations of the wheels and analyzing them. For example,we can immediately infer straight forward movement if bothwheels rotate at the same rate in the same direction. Tocalculate the magnitude of movement, we simply record theamount of rotations and multiply it by the circumference of thewheels. In this way, we develop a simple mathematical modelof the wheelchairs movement, consisting of simple straightforward movement and turns. To make the distances morefine-grained than entire circumference of the wheel, we placemarkers on the wheels equally spread apart. We then are ableto increment the travelling distance by the arc lengths betweenthese marks rather than entire wheel circumferences, yieldingmore accurate results.</p><p>For straight forward movement, we utilize the followingequation:</p><p>dtravelled =cwheeln</p><p> ticks, (1)where cwheel is the circumference of the wheel, n is thenumber of marks we place on the wheel, and ticks is thenumber of times each mark on the wheel passes by a point ofreference to indicate motion.</p><p>Turns are a little more complex to think about. We placethem into two different categories, one easy case which we call</p><p>2013 12th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET)</p><p>142</p></li><li><p>Fig. 1. An illustration of a generic turn with the two wheels of a wheelchair</p><p>a sharp turn, and a more difficult case which is the generic turn.The sharp turn is where one of the wheels is moving, whilethe other one stays completely still. The generic turn, on theother hand, has both wheels moving at the same time, but oneof the wheels rotates faster than the other one, thus creating aturn. For sharp turns, we utilize the following equations:</p><p>cchairTurn = 2wchair, (2)</p><p>turn =dtravelledcchairTurn</p><p> 360, (3)</p><p>where cchairTurn is the circumference when the chair turns afull circle by keeping one wheel still. This is calculated withthe standard circumference formula, using the width of thechair, wchair, as the circle radius. dtravelled used in (3) is thedistance travelled by the rotating wheel during the turn, whichin this case is the arc of the turning circle.</p><p>For a generic turn, we use the definition of radians to derivethe turning model. When the wheelchair makes a generic turn,we assume that the two wheels follow the curvature of twocircles, in the fashion as illustrated in Figure 1. In this scenario,the known variables are r, which is just the distance betweenthe two wheels, and d1, and d2, the arc distance travelled byeach wheel during the turn. By definition of radians, is</p><p>rturn =d1r1</p><p>=d2r2</p><p>. (4)</p><p>Substituting in r to get r2 = r1 + r and rearrangingthe terms, we end up with</p><p>r1 =d1r</p><p>d2 d1 . (5)</p><p>Therefore,</p><p>rturn =d1r1</p><p>= d1(d2 d1d1r</p><p>) =d2 d1r</p><p>, (6)</p><p>and the equation for calculating the turning angle, indegrees, is</p><p>Fig. 2. Placement of magnets and reed switch on one of the wheels</p><p>turn =d2 d1r</p><p> 180</p><p>. (7)</p><p>With these equations in place to model the overallwheelchair movement, we know what distance and in whichdirection the wheelchair has moved from its initial positionand thus find its location inside the building.</p><p>V. IMPLEMENTATION</p><p>Our implementation of the wheelchair localization systemconsists of both a hardware part and software part. The radiusof the wheel is 0.29m, and the width of the chair from wheelto wheel is 0.53m. Thus, the circumference for each wheel isabout 1.8m, and the circumference of a turn for the wheelchairis 3.3m, as seen in (2).</p><p>To keep track of the ticks for calculating the distance awheel has travelled, we placed a magnet on each of the eightspokes of each wheel, and ran two reed switches down eachside of the wheel chair to detect as the magnets pass by it(see Figure 2). The reed switches are then connected to aBluetooth mouse on the wheelchair where the left reed switchis connected to the left click and the right one to the rightclick. In this way, we record the rotation for each wheel basedon the left and right clicks of the mouse.</p><p>We differentiate between straight forward movement andturns by comparing the number of left clicks LC with thenumber of right clicks RC. If they are equal, we infer that thewheelchair is moving forward and can thus use (1) to calculatethe new position of the wheelchair. On the other hand, wecan infer a simple turn if the click count for either left orright was 0. In this case, the wheelchair is turning tow...</p></li></ul>

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