Patent Publication Number: US-9423252-B2

Title: Using clustering techniques to improve magnetometer bias estimation

Description:
TECHNICAL FIELD 
     This subject matter is related to magnetometer bias estimation. 
     BACKGROUND 
     A mobile device such as a cellular phone or a smart phone, PDA, handheld computer, navigational device, gaming device, netbook, among others can be equipped with a magnetometer. Magnetic readings from the magnetometer can be used to provide a user with a direction, which may be a “heading” (typically given relative to the Earth&#39;s true North), and/or an arrow pointing to true North. The direction information may be provided for the user&#39;s own navigation knowledge, for example, to tell the user which way is north while the user is walking or driving in unfamiliar surroundings. The direction information can also be used by a navigation or map application that may be running on the mobile device. 
     The magnetometer obtains a measure of the magnetic field that is present in the immediate surroundings of the mobile device as a two or three-component vector in a Cartesian coordinate system using 2-axis or 3-axis magnetic sensors. The sensed magnetic field can contain a contribution of the Earth&#39;s magnetic field and a contribution by a local interference field (device co-located interference fields). The latter is a magnetic field that is created by components in the local environment of the mobile device. This may include contributions by one or more magnetic field sources that are near the magnetic sensors, such as the magnet of a loudspeaker that is built into the mobile device. The interference field may also have a contribution due to one or more magnetic objects found in the external environment close to the device, such as when the user is driving an automobile, riding in a train or bus, or riding on a bicycle or motorcycle. In most cases, the interference field is not negligible relative to the Earth&#39;s magnetic field. Therefore, a calibration procedure is needed to reduce the adverse impact of the interference field contribution from the sensors&#39; measurements to allow the magnetometer to calculate a more accurate direction. 
     There are several types of 3-axis calibration procedures. In one such technique, the user is instructed to rotate the mobile device (containing the magnetometer) according to a set of geometrically different orientations and azimuth angles, while measurements by the magnetometer and by an orientation sensor are collected and analyzed to isolate or quantify the interference field. The quantified interference field can then be subtracted from the measurement taken by the magnetic sensor to yield the Earth&#39;s geomagnetic field. The Earth&#39;s geomagnetic field can be further corrected to get the true north direction, such as correcting for magnetic variation (declination) due to the variation of the Earth&#39;s magnetic field based on geographic location. 
     In another 3-axis calibration technique, rather than instruct the user to deliberately rotate the mobile device in a predetermined manner, measurements are collected from the magnetometer, continuously over a period of time, while the mobile device is being used or carried by the user. This can lead to random (albeit sufficient) rotations of the mobile device, such that the magnetometer measurements define a desired, generally spherical measurement space. The sphere is offset from the origin of a coordinate system for the Earth&#39;s geomagnetic field vector by an unknown offset vector, which can represent a substantial part (if not all) of the interference field. Mathematical processing of the measurements can be performed to “re-center” the sphere by determining the offset vector. This technique is transparent to the user because the user is not required to go through a calibration procedure where the user deliberately rotates the device through a specified set of orientations. 
     The calibration techniques described above are effective but time consuming. As the user travels with the mobile device, the magnetometer will encounter different magnetic environments with varying local interference. These different magnetic environments can require a recalibration procedure and the calculation of a new offset vector. Even if the user returns to a previous location, a recalibration procedure may be required due to a change in the local interference field. 
     To avoid recalibrating the magnetometer for each use, a calibration database may be constructed with previously-calibrated readings. In these instances, raw magnetometer data is compared to a lookup table of the previously-calibrated readings, including thresholds, to determine matches with the raw magnetometer data. If a match is found, the bias offset for the matching calibrated reading may be applied to the raw magnetometer data to determine an estimated geomagnetic field. Though, the calibration data may include gaps such that no previously-calibrated readings match the raw calibration data. Also, the thresholds used to match the previously-calibrated readings are static. 
     SUMMARY 
     In some implementations, a computer-implemented method includes receiving a reading from a magnetometer of a mobile device. A cluster from a plurality of clusters of bias offsets generated from previously-calibrated readings is selected. The selected cluster has a representative bias offset, a mean of magnitudes in the selected cluster, and a magnitude threshold. An external magnetic field is estimated based on the reading and the representative bias offset for the selected cluster. Whether a magnitude of the estimated external field is within a magnitude range defined by the mean magnitude and the mean magnitude plus the magnitude threshold is determined. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an exemplary Cartesian coordinate system describing the Earth&#39;s geomagnetic field in accordance with some implementations. 
         FIG. 2  illustrates an exemplary 3-axis magnetometer in accordance with some implementations. 
         FIG. 3  illustrates an example system for evaluating readings against clustered data. 
         FIG. 4  is a three-dimensional graph illustrating clustering on bias vectors. 
         FIG. 5  is a flow chart illustrating an example method for comparing an estimated external field to clustered data. 
         FIG. 6  is a flow chart illustrating an example method for re-execution a clustering algorithm in response to a trigger event. 
         FIG. 7  is a block diagram of exemplary architecture of a mobile device employing the processes of  FIGS. 5 and 6  in accordance with some implementations. 
         FIG. 8  is a block diagram of exemplary network operating environment for the device of  FIG. 7 . 
     
    
    
     DETAILED DESCRIPTION 
     Raw Magnetic Field—Overview 
       FIG. 1  illustrates an exemplary Cartesian coordinate system for describing the Earth&#39;s geomagnetic field   in accordance with some implementations. The geomagnetic field vector   can be described, in device coordinates, by the orthogonal components E x  (toward top of a mobile device), E y  (toward right side of mobile device) and E z  (back side of mobile device, positive downwards); the magnitude | |; and inclination (or dip relative to a horizontal line) I. Similarly, the gravitational acceleration vector   can be described by the orthogonal components g x  (toward top of a mobile device), g y  (toward right side of mobile device) and g z  (back side of mobile device, positive downwards); and magnitude | |. The  ,  , and the horizontal line are coplanar, and the intersection of   and the horizontal line form a 90° angle. The magnitude | |, the magnitude | |, and the inclination angle I can be computed from the orthogonal components using the following equations: 
     
       
         
           
             
               
                 
                   
                     
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     The inclination angle I can be defined as the angle between the geomagnetic field   and the Earth&#39;s gravitational acceleration vector  . The most commonly used International System of Units (SI) unit of magnetic-field magnitude is the Tesla. 
     Overview of Magnetometers 
       FIG. 2  illustrates an exemplary 3-axis magnetometer in accordance with some implementations. In general, a magnetometer is an instrument that can sense the magnitude and direction of a magnetic field in its vicinity. Magnetometers can be 2-axis or 3-axis and the processes described here apply to both types of sensors. In the interest of brevity, only a 3-axis magnetometer is described. 
     In some implementations, 3-axis magnetometer sensor configuration  100  can be used to calculate a heading for a variety of applications, including applications running on a mobile device. For example, magnetometers may be used that require dead reckoning or headings, such as navigation applications for vehicles, aircraft, watercraft and mobile devices (e.g., smart phones). Sensor configuration  100  can include three magnetic field sensors  102 ,  104 ,  106  mounted orthogonally on a board, substrate or other mounting surface. Magnetic sensors  102 ,  104 ,  106  can be included in an integrated circuit (IC) package with or without other sensors, such as accelerometers and gyros. 
     Sensor configuration  100  can be deployed in a host system environment that contains interfering magnetic fields. Since the Earth&#39;s magnetic field is a weak field (˜0.5 Gauss), other magnetic fields can interfere with the accuracy of sensors  102 ,  104 ,  106 . For example, the sensors  102 ,  104 ,  106  may pick or otherwise detect magnetic fields generated by components of the mobile device, which may be referred to as bias offset. A calibration procedure can be deployed to isolate and remove the bias offset. One technique is to determine an offset vector which can be subtracted from sensor measurements to get accurate measurements of the Earth&#39;s magnetic field. 
     In one exemplary calibration procedure for a 3-axis magnetometer, each heading computation can be assumed to be made with a number of valid X, Y, and Z sensor readings which can be taken with a minimal delay between each reading. For this sensor configuration, sensors  102 ,  104 ,  106  are at right angles with respect to each other and lie level with respect to the Earth&#39;s surface. As discussed above, the positive end of the X-axis points to the top of the mobile device, the positive end of the Y-axis points to the right-side of the mobile device when facing the screen, and the positive end of the Z-axis points to backside of the mobile device. During calibration, two consecutive sensor readings may be made 180 degrees apart. These measurements can be represented by reading (R x1 , R y1 , R z1 ) and reading (R x2 , R y2 , R z2 ), which are measurements of the raw magnetic field including Earth&#39;s magnetic field plus bias offset. The Earth&#39;s magnetic field in any given direction as measured with no interfering field can be represented by values (E x , E y , E z ). Magnetic interference can be represented by values (B x , B y , B z ). Using these mathematical conventions, the two sensor readings can be represented by
 
 R   x1   =E   x   +B   x ;
 
 R   y1   =E   y   +B   y ;
 
 R   z1   =E   z   +B   z ;
 
 R   x2   =−E   x   +B   x ;
 
 R   y2   =−E   y   +B   y ; and
 
 R   z2   =−E   z   +B   z .  [4]
 
     Assuming the magnetometer is fixed with respect to the host system (e.g., a magnetometer installed in a mobile phone), the readings (R x1 , R y1 , R z1 ) and (R x2 , R y2 , R z2 ) taken during calibration may both contain substantially the same interference values (B x , B y , B z ). Since the magnetometer readings taken during calibration are 180 degrees apart the readings of the Earth&#39;s magnetic field (E x , E y , E z ) are equal but opposite in sign (−E x , −E y , −E z ). Solving the equations above for B x , B y , and B z  yields:
 
 B   x =( R   x1   +R   x2 )/2,
 
 B   y =( R   y1   +R   y2 )/2, and
 
 B   z =( R   z1   +R   z2 )/2.  [5]
 
     Using the determined bias offsets for each component, the Earth&#39;s magnetic field (E x , E y , E z ) may be determined by the following equations:
 
 E   x   =R   x   −B   x ;
 
 E   y   =R   y1   −B   y ; and
 
 E   z   =R   z1   −B   z .  [6]
 
     At any given position on Earth, a magnitude | | is substantially constant, regardless of magnetometer orientation. Also, at any given position on Earth, the inclination angle I between the gravitational vector   and the Earth&#39;s magnetic field   is substantially constant because the orientation of the gravitational acceleration vector   and the orientation of geomagnetic field vector   are substantially constant. 
     A heading ψ of the mobile device may be calculated from the determined   and   using equations [7]-[11] as followed: 
     
       
         
           
             
               
                 
                   
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     The heading may be calibrated using other implementations. For example, the heading may also be calibrated based on the orientation of the device obtained from an accelerometer, inclination, GPS, and other types of corrections or calibrations. 
     If a magnetometer is included in a mobile device, such as a mobile phone, the bias offset may change. For example, if the user docks his mobile device (containing the magnetometer) in his car, magnetic objects in the car could change the local interference, which could result in the calibration offsets becoming invalid. If the offsets are invalid, then the magnetometer can perform a recalibration procedure to generate new offsets. This recalibration procedure can be a tedious process for the user if performed often and may require the user to manipulate the mobile device through a number of angles. 
     Example Cluster Evaluation of Magnetometer Data 
       FIG. 3  illustrates an example system  300  for cluster evaluation of magnetometer data. For example, the system  300  may store calibrated magnetometer data including magnitudes | | of the geomagnetic field, inclination angle I, and associated bias offsets (B x , B y , B z ). As illustrated, the system  100  includes a magnetometer  302  for detecting magnetic fields, a magnetometer calibration module  304  for calibrating magnetometer data, magnetometer database  306  for storing calibrated magnetometer data, a clustering module  308  for determining clusters of the calibrated magnetometer data, clustered magnetometer database  310  for storing clustered data, and a cluster matching module  312  for determining whether magnetometer data matches any clusters in the clustered magnetometer database  310 . In particular, the magnetometer  302  may be 2-axis or 3-axis magnetometer as discussed with respect to  FIG. 2  that measures different components of the raw magnetic field (R x , R y , R z ) in, for example, device coordinates. 
     In response to a recalibration trigger event, the magnetometer  302  may pass magnetometer data to the magnetometer calibration module  304 . A recalibration trigger event can be any event that triggers a recalibration procedure on the mobile device. The trigger event can be based on time, location, mobile device activity, an application request, magnetometer data, expiration of a time period, or other events. In connection with recalibration, the magnetometer calibration module  304  may determine a bias offsets (B x , B y , B z ) based on raw magnetometer reading (R x , R y , R z ) and determine the Earth&#39;s magnetic field vector   based on the bias offset (B x , B y , B z ) and the raw magnetometer reading (R x , R y , R z ) as discussed with respect to  FIG. 2 . In addition, the magnetometer calibration module  304  may determine the magnitude | | of the geomagnetic field and determine the inclination angle I based on the determined geomagnetic field vector   and the gravitational vector  . The magnetometer calibration module  304  may receive the gravitational vector   from a location processor, accelerometer readings, or other sources. 
     Each time a calibration procedure is performed, the magnetometer calibration module  304  may store, in the magnetometer database  306 , the magnitude | |, the inclination angle I, and associated bias offsets (B x , B y , B z ). As previously mentioned, the magnitude | | and the inclination angle I for each location should be theoretically constant regardless of the position of the magnetometer on the Earth or its orientation. If these parameters are not constant then the bias offset may have changed. The magnetometer database  306  may store other parameters such as, for example, temperature, calibration level, a timestamp. The calibration level can be used to determine the accuracy or quality of a set of calibration data (e.g., offset values), so that an accurate set of calibration data are not overwritten with a less accurate set of calibration data. The timestamp can be used to manage entries in the magnetometer database  306 . For example, the timestamp can be used with an “aging” algorithm for overwriting entries in the magnetometer database  306 , so that magnetometer database  306  does not grow too large. In some implementations, entries with the oldest timestamp can be overwritten first. In some implementations, a count is kept for each entry in the magnetometer database  306  and may be incremented each time an entry is used to restore calibration offsets. The entries with the lowest count may be overwritten first. In some implementations, both timestamps and counts can be used for managing the magnetometer database  306 . This constant property makes these Earth magnetic field parameters useful for determining the confidence of a match, as described in reference to  FIG. 4 . 
     The clustering module  308  can include any software, hardware, firmware, or combination thereof configured to execute a clustering algorithm on the bias offsets (B x , B y , B z ) stored in the magnetometer database  306  to form clusters. For example, the clustering module  308  may apply the well-known clustering algorithm known as quality threshold clustering algorithm to entries in the magnetometer database  306  to create clusters of bias offsets. Other clustering algorithm may be used such as connectivity based clustering, centroid-based clustering, distribution-based clustering, density-based clustering, or others. In general, cluster analysis or clustering assigns a set of objects into groups, i.e., clusters, so that the objects in the same cluster are more similar to each other based on one or more metrics than to objects in other clusters. In some implementations, the clusters may be based on the Euclidean distance between bias offset points. Further details of operations of clustering module  308  are described below in reference to  FIG. 4 . 
     The clustering module  308  stores the determined clusters in the clustered magnetometer database  310 . For each cluster, the clustering module  308  may determine a mean magnitude C m  of the Earth&#39;s geomagnetic field   as follows: 
                     C   m     =         ∑     i   =   1     N     ⁢            E   ⇀     l            N             [   12   ]               
where N is the number of geomagnetic field vectors   in the cluster. Also, the clustering module  308  may determine a mean inclination angle C a  of the Earth&#39;s geomagnetic field   as follows:
 
                     C   a     =         ∑     i   =   1     N     ⁢     I   i       N             [   13   ]               
where N is the number of geomagnetic fields   in the cluster. In addition, the clustering module  308  may determine a magnitude threshold and an angle threshold. For example, the magnitude threshold may be based on the standard deviation of the magnitudes of the geomagnetic fields | | in the cluster, and the angle threshold may be based on the standard deviation of the inclination angles in the cluster. In addition, the clustering module  308  may determine, for each cluster, a representative bias offset. For example, the clustering module  308  may determine, for each cluster, the geometric center of the cluster as the representative bias offset for the cluster. In some instances, the clustering module  308  determines, for each cluster, the mean of the biases as the center of the cluster. The clustering module  308  may use other statistical measures for determining the center of the cluster such as determining a median of the bias offsets of the cluster.
 
     The cluster matching module  312  can include any software, hardware, firmware, or combination thereof for determining, for each of the clusters in the clustered magnetometer database  310 , whether magnetometer data from the magnetometer  302  satisfies the mean magnitude and magnitude threshold and the mean inclination angle and angle threshold. In particular, the cluster matching module  312  may identify the representative bias offset for each of the clusters and determine an estimated external field using the equations [6]. In other words, the cluster matching module  312  may, for each cluster, estimate the geomagnetic field   using the following equation:
 
 = −   [14]
 
where   is the raw magnetic field vector determined by the magnetometer  302  and   is the representative bias offset for the cluster. Once determined, the cluster matching module  312  may determine the magnitude | | and the inclination angle I for the estimated geomagnetic field   using the equations [1]-[3].
 
     After the magnitude and inclination angle are determined for the estimated geomagnetic field   for a cluster, the cluster matching module  312  may determine whether the estimated magnitude is within the range of the mean of the Earth&#39;s geomagnetic field to the mean plus the threshold for the cluster. In addition, the cluster matching module  312  may determine whether the estimated inclination angle I is within the range of the mean of the inclination angle to the mean plus the threshold for the cluster. If a match is not found, the cluster matching module  312  iteratively executes these calculations to determine if the estimated geomagnetic field matches any of the clusters in the clustered magnetometer database  310 . If a match with a cluster is found, an estimated heading ψ can be computed using the equations [7]-[11], the estimated geomagnetic field   determined from the representative bias offset of the matching cluster, and the gravitational vector  . 
     Clustering Overview 
       FIG. 4  is a three-dimensional graph  400  illustrating exemplary clustering techniques of bias offsets. In particular, the diagram  400  is a three-dimensional space based on the bias offset components (B x , B y , B z ). The clustering module  308  (as described in reference to  FIG. 3 ) can apply quality threshold techniques to create exemplary clusters of bias offsets C1, C2, and C3. As illustrated, the graph  400  includes different clusters C1, C2, and C3 are illustrated in different shades of gray and clusters that include only a single point are illustrated in the same shade of gray. 
     The clustering module  308  can analyze the magnetometer database  306  as described above in reference to  FIG. 3 . The clustering module  208  can identify a first class of bias offsets having a first label (e.g., those labeled as “positive”) and bias offsets having a second label (e.g., those labeled as “negative”). The clustering module  308  can identify a specified distance (e.g., a minimum distance) between a first class bias-offset point (e.g., “positive” bias-offset point  402 ) and a second class motion feature (e.g., “negative” bias-offset point  404 ). The clustering module  308  can designate the specified distance as a quality threshold (QT). 
     The clustering module  308  can select the first bias-offset point  402  to add to the first cluster C1. The clustering module  308  can then identify a second bias-offset point  404  whose distance to the first bias-offset point  402  is less than the quality threshold and, in response to satisfying the threshold, add the second bias-offset point  404  to the first cluster C1. The clustering module  308  can iteratively add bias-offset points to the first cluster C1 until all bias-offset points whose distances to the first bias-offset point  402  are each less than the quality threshold has been added to the first cluster C1. 
     The clustering module  308  can remove the bias-offset points in C1 from further clustering operations and select another bias-offset point (e.g., bias-offset points  406 ) to add to a second cluster C2. The clustering module  308  can iteratively add bias-offset points to the second cluster C2 until all bias-offset points whose distances to the bias-offset point  406  are each less than the quality threshold have been added to the second cluster C2. The clustering module  308  can repeat the operations to create clusters C3, C4, and so on until all bias-offset points features are clustered. 
     The clustering module  308  can generate representative bias offsets for each cluster. In some implementations, the clustering module  308  can designate as the representative bias offsets the geometric center (e.g., mean of the bias offsets in the cluster) of the cluster such as the center  508  for cluster C1. The clustering module  308  may use other techniques for designating a bias-offset point as the representative bias offsets. For example, the clustering module  308  may identify an example that is closest to other samples. In these instances, the clustering module  308  can calculate distances between pairs of bias-offset points in cluster C1 and determine a reference distance for each bias-offset point. The reference distance for a bias-offset point can be a maximum distance between the bias-offset point and another bias-offset point in the cluster. The clustering module  308  can identify a bias-offset point in cluster C1 that has the minimum reference distance and designate the bias-offset point as the bias offsets for cluster C1. 
     Example Process for Managing Clustered Data 
       FIGS. 5 and 6  are flow charts illustrating example methods  500  and  600  for managing clustered data in accordance with some implementations of the present disclosure. Methods  500  and  600  are described with respect to the system  300  of  FIG. 3 . Though, the associated system may use or implement any suitable technique for performing these and other tasks. These methods are for illustration purposes only and that the described or similar techniques may be performed at any appropriate time, including concurrently, individually, or in combination. In addition, many of the steps in these flowcharts may take place simultaneously and/or in different orders than as shown. Moreover, the associated system may use methods with additional steps, fewer steps, and/or different steps, so long as the methods remain appropriate. 
     Referring to  FIG. 5 , method  500  begins at step  502  where magnetometer data for the raw magnetic field is detected. For example, the magnetometer  302  in  FIG. 3  may detect magnetic fields in device coordinates and pass the readings to the cluster matching module  312 . At step  504 , a plurality of clusters is identified. As for the example, the cluster matching module  312  may retrieve or otherwise identify clusters stored in the clustered magnetometer database  310 . Next, at step  506 , a representative bias offset is identified for an initial cluster. In some implementations, method  500  is iterated through all clusters such that the order may be determined based on any parameter such as timestamp, size, assigned indices, or others. An estimate of the external field is determined by the representative bias offset from the raw magnetic field at step  508 . At step  510 , the magnitude of the estimated external field is determined. Next, at step  512 , a gravitational vector is identified. The inclination angle is determined, at step  514 , based on an estimated external field vector and the gravitational vector. If the magnitude does not match the mean magnitude and threshold of the cluster or the inclination angle for the external field does not match the mean angle and threshold of the cluster at decisional step  516 , then execution proceeds to decisional step  518 . If another cluster is available for evaluation, then, at step  520 , the representative bias offset for the next cluster is identified. If another cluster is not available to evaluate, the novel bias is stored in the database at step  522 . Returning to decisional step  516 , if both the magnitude of the estimated external field satisfy the magnitude threshold and the inclination angle of the estimated external field satisfy the angle threshold, a heading of the mobile device may be determined based on the estimated external field at step  524 . 
     Referring to  FIG. 6 , method  600  begins at step  602  where the offset bias for magnetometer data is determined during calibration. For example, the magnetometer  302  may detect multiple points, and the magnetometer calibration module  304  may directly determine the bias offset using the multiple points. At step  604 , the determined bias offset is added to the bias table. As for the example, the magnetometer calibration module  304  may store the bias offset in a lookup table stored in the magnetometer database  306 . If the determined bias offset is novel at decisional step  606 , then execution proceeds to decisional step  608 . For example, the magnetometer calibration module  304  may determine that the bias offset does not fall within the existing clusters. If the total number of bias offsets exceed a specified threshold (e.g., 2, 5, 10), then, at step  610 , the clustering algorithm applied to the bias offset data stored in the magnetometer database  310  to determine new clusters. At step  612 , the determined clusters are stored in a cluster table. As for the example, the clustering module  308  may apply the clustering algorithm on the magnetometer database  306 , generate new clusters, and store the new clusters in a table of clustered magnetometer database  310 . If either the bias offset falls within a cluster or the number of novel bias offsets have not exceed a threshold, then execution ends. 
     Example Mobile Device Architecture 
       FIG. 7  is a block diagram of exemplary architecture  700  of a mobile device including an electronic magnetometer. The mobile device  700  can include memory interface  702 , one or more data processors, image processors and/or central processing units  704 , and peripherals interface  706 . Memory interface  702 , one or more processors  704  and/or peripherals interface  706  can be separate components or can be integrated in one or more integrated circuits. Various components in mobile device architecture  700  can be coupled together by one or more communication buses or signal lines. 
     Sensors, devices, and subsystems can be coupled to peripherals interface  706  to facilitate multiple functionalities. For example, motion sensor  710 , light sensor  712 , and proximity sensor  714  can be coupled to peripherals interface  706  to facilitate orientation, lighting, and proximity functions of the mobile device. Location processor  715  (e.g., GPS receiver) can be connected to peripherals interface  706  to provide geopositioning. Electronic magnetometer  716  (e.g., an integrated circuit chip) can also be connected to peripherals interface  706  to provide data that can be used to determine the direction of magnetic North. 
     Camera subsystem  720  and Optical sensor  722 , e.g., a charged coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) optical sensor, can be utilized to facilitate camera functions, such as recording photographs and video clips. 
     Communication functions can be facilitated through one or more wireless communication subsystems  724 , which can include radio frequency receivers and transmitters and/or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of communication subsystem  724  can depend on the communication network(s) over which the mobile device is intended to operate. For example, the mobile device may include communication subsystems  724  designed to operate over a GSM network, a GPRS network, an EDGE network, a Wi-Fi or WiMax network, and a Bluetooth™ network. In particular, wireless communication subsystems  724  may include hosting protocols such that the mobile device may be configured as a base station for other wireless devices. 
     Audio subsystem  726  can be coupled to speaker  728  and microphone  730  to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and telephony functions. Note that speaker  728  could introduce magnetic interference to the magnetometer, as described in reference to  FIGS. 1-2 . 
     I/O subsystem  740  can include touch surface controller  742  and/or other input controller(s)  744 . Touch surface controller  742  can be coupled to touch surface  746 . Touch surface  746  and touch surface controller  742  can, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch surface  746 . 
     Other input controller(s)  744  can be coupled to other input/control devices  748 , such as one or more buttons, rocker switches, thumb-wheel, infrared port, USB port, docking station and/or a pointer device such as a stylus. The one or more buttons (not shown) can include an up/down button for volume control of speaker  728  and/or microphone  730 . 
     In one implementation, a pressing of the button for a first duration may disengage a lock of touch surface  746 ; and a pressing of the button for a second duration that is longer than the first duration may turn power to the mobile device on or off. The user may be able to customize a functionality of one or more of the buttons. Touch surface  746  can, for example, also be used to implement virtual or soft buttons and/or a keyboard. 
     In some implementations, the mobile device can present recorded audio and/or video files, such as MP3, AAC, and MPEG files. In some implementations, the mobile device can include the functionality of an MP3 player, such as an iPod Touch™. 
     Memory interface  702  can be coupled to memory  750 . Memory  750  can include high-speed random access memory and/or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices, and/or flash memory (e.g., NAND, NOR). Memory  750  can store operating system  752 , such as Darwin, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks. Operating system  752  may include instructions for handling basic system services and for performing hardware dependent tasks. In some implementations, operating system  752  can be a kernel (e.g., UNIX kernel). 
     Memory  750  may also store communication instructions  754  to facilitate communicating with one or more additional devices, one or more computers and/or one or more servers. Memory  750  may include graphical user interface instructions  756  to facilitate graphic user interface processing; sensor processing instructions  758  to facilitate sensor-related processing and functions; phone instructions  760  to facilitate phone-related processes and functions; electronic messaging instructions  762  to facilitate electronic-messaging related processes and functions; web browsing instructions  764  to facilitate web browsing-related processes and functions; media processing instructions  766  to facilitate media processing-related processes and functions; GPS/Navigation instructions  768  to facilitate GPS and navigation-related processes and instructions; camera instructions  770  to facilitate camera-related processes and functions; magnetometer data  772  and calibration instructions  774  to facilitate magnetometer calibration, as described in reference to  FIG. 2 . In some implementations, GUI instructions  756  and/or media processing instructions  766  implement the features and operations described in reference to  FIGS. 1-6 . 
     Memory  750  may also store other software instructions (not shown), such as web video instructions to facilitate web video-related processes and functions; and/or web shopping instructions to facilitate web shopping-related processes and functions. In some implementations, media processing instructions  766  are divided into audio processing instructions and video processing instructions to facilitate audio processing-related processes and functions and video processing-related processes and functions, respectively. An activation record and International Mobile Equipment Identity (IMEI) or similar hardware identifier can also be stored in memory  750 . 
     Each of the above identified instructions and applications can correspond to a set of instructions for performing one or more functions described above. These instructions need not be implemented as separate software programs, procedures, or modules. Memory  750  can include additional instructions or fewer instructions. Furthermore, various functions of the mobile device may be implemented in hardware and/or in software, including in one or more signal processing and/or application specific integrated circuits. 
     The disclosed and other embodiments and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. The disclosed and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer-readable medium for execution by, or to control the operation of, data processing apparatus. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, or a combination of one or more them. The term “data processing apparatus” means all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. 
     A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. 
     The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). 
     Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. 
     To provide for interaction with a user, the disclosed embodiments can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. 
     The disclosed embodiments can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of what is disclosed here, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet. 
     While this specification contains many specifics, these should not be construed as limitations on the scope of what being claims or of what may be claimed, but rather as descriptions of features specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understand as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 
     Exemplary Operating Environment 
       FIG. 8  is a block diagram of exemplary network operating environment  800  for the mobile devices implementing motion pattern classification and gesture recognition techniques. Mobile devices  802   a  and  802   b  can, for example, communicate over one or more wired and/or wireless networks  810  in data communication. For example, a wireless network  812 , e.g., a cellular network, can communicate with a wide area network (WAN)  814 , such as the Internet, by use of a gateway  816 . Likewise, an access device  818 , such as an 802.11g wireless access device, can provide communication access to the wide area network  814 . 
     In some implementations, both voice and data communications can be established over wireless network  812  and the access device  818 . For example, mobile device  802   a  can place and receive phone calls (e.g., using voice over Internet Protocol (VoIP) protocols), send and receive e-mail messages (e.g., using Post Office Protocol 3 (POP3)), and retrieve electronic documents and/or streams, such as web pages, photographs, and videos, over wireless network  812 , gateway  816 , and wide area network  814  (e.g., using Transmission Control Protocol/Internet Protocol (TCP/IP) or User Datagram Protocol (UDP)). Likewise, in some implementations, the mobile device  802   b  can place and receive phone calls, send and receive e-mail messages, and retrieve electronic documents over the access device  818  and the wide area network  814 . In some implementations, mobile device  802   a  or  802   b  can be physically connected to the access device  818  using one or more cables and the access device  818  can be a personal computer. In this configuration, mobile device  802   a  or  802   b  can be referred to as a “tethered” device. 
     Mobile devices  802   a  and  802   b  can also establish communications by other means. For example, wireless mobile device  802   a  can communicate with other wireless devices, e.g., other mobile devices  802   a  or  802   b , cell phones, etc., over the wireless network  812 . Likewise, mobile devices  802   a  and  802   b  can establish peer-to-peer communications  820 , e.g., a personal area network, by use of one or more communication subsystems, such as the Bluetooth™ communication devices. Other communication protocols and topologies can also be implemented. 
     The mobile device  802   a  or  802   b  can, for example, communicate with one or more services  830  and  840  over the one or more wired and/or wireless networks. For example, one or more motion training services  830  can be used to determine one or more motion patterns. Motion pattern service  840  can provide the one or more one or more motion patterns to mobile devices  802   a  and  802   b  for recognizing gestures. 
     Mobile device  802   a  or  802   b  can also access other data and content over the one or more wired and/or wireless networks. For example, content publishers, such as news sites, Rally Simple Syndication (RSS) feeds, web sites, blogs, social networking sites, developer networks, etc., can be accessed by mobile device  802   a  or  802   b . Such access can be provided by invocation of a web browsing function or application (e.g., a browser) in response to a user touching, for example, a Web object. 
     Particular embodiments of the subject matter described in this specification have been described. Other embodiments are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. As one example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous.