w % an
Contents
1. 103 examples SIS ere erm Mg mM MM NM RM wm mM em Re RM KK KH Ke Ke ee j eae ee ee ee ee ee Ke Ke Ke Ke ImCalibrator LiveMove librarv Figure 1 Overview U S Patent Apr 20 2010 Sheet 2 of 6 US 7 702 608 B1 201 200 202 human performs motion motion filter ted compute distances to prototypes 205 any prototypes no within CD 206 KA 207 unknown label more than ng candidate 208 fl 209 piek best prototvpe 210 Figure 2 Classification U S Patent Apr 20 2010 Sheet 3 of 6 US 7 702 608 B1 301 300 labeled motion examples human selects examples to use calculate classification rates select best ones as prototypes build classifier classifiers Figure 3 Generating a Classifer U S Patent Apr 20 2010 Sheet 4 of 6 US 7 702 608 B1 401 start 400 402 NN classifiers 403 human provides new example 404 adaptive smoothing 405 new example accepted SS 406 classifier has spare capacity 407 add new prototvpe 408 classifiers Figure 4 Tuning a Classifier U S Patent Apr 20 2010 Sheet 5 of 6 US 7 702 608 B1 500 host computer 502 console development kit 503 505 X Figure 5 Typical Setup U S Patent Apr 20 2010 Sheet 6 of 6 US 7 702 608 B1 NNR Bae Bie i 603 y 605 save classifier l y Jill s classifier o a2. boosted if a classifier can be modified to include prototypes from the player whose motions are to be recognized Itis up to the game developer as to how they incorporate the tuning step into their game The only constraint is that the classifier be provided with new labeled examples of known motion classes A simple example of how the tuning step might be performed is to have the player follow instructions to perform a predetermined set of motions That way the clas sifier knows to which class of motions the supplied motion is mean to belong US 7 702 608 B1 11 Of course all motion signals are again adaptively smoothed in order to compress them and make them easier to compare and manage If the candidate tuning example is too dissimilar from the known prototypes it will typically be rejected and the player is expected to modify their behavior to more accurately per form the desired motion In this way the player is disallowed from generating de facto new recognizers In particular the ability to allow players to generate their own recognizers is only available for an additional licensing fee If the candidate tuning example is deemed suitable it will be used to augment or replace one of the classifier s existing set of prototypes Augmentation is preferable but if the clas sifier has reached its capacity for example due to memory constraints one of the existing prototypes must be discarded Additional details and advice o
3. say an uppercut or a jab This might be useful if there were circumstances in the game in which it was only necessary to determine the broad class of motion In such cases the additional work of determining more fine grained information about the motion could be avoided Methods of Operation FIG 3 shows the process 300 of generating a new classifier 307 from a set of labeled examples 302 In particular a human operator of ImMaker 303 selects which examples to use to build a classifier If necessary the US 7 702 608 B1 7 motion examples are smoothed and then the classification rates are calculated for each example to each other example 304 The examples with the best classification rates are selected as the prototypes 305 The selected prototypes are then used to create the classifiers 305 that are stored out to disk or some other persistent storage 307 for future use Those skilled in the art would recognize that it is straight forward to include the functionality of ImMaker in the run time library This would allow the game players to generate their own classifiers from scratch within the context of play ing the game The only challenge is from a game design point of view how to integrate the classifier generation process into the game One implementation by the inventors would be in the context of a Simon Says game One player performs some motions that are used as prototypes to generate a new classi fier And then another p
4. Live Move classifier maker and a motion recorder application called ImRecorder To use the invention game developers will insert calls to the libCon soleLM run time library API into their own code Then the developer will compile and link the libConsoleLM with their game code and any additional libraries they happen to be using In contrast a developer will only use ImMaker and ImRecorder at development time US 7 702 608 B1 9 Methods of Operation The steps that a game developer might typically follow to use LiveMove are listed below In practice any given set of developers may choose to skip some of the steps repeat a step until some criteria are met iterate over some subset of steps until some criteria are met or perform some steps in a differ ent order Motion Design Step As part of the game design process a game developer will typically decide upon a set of motions that they want the player to be able to perform in the game Motion Creation Step Using ImRecorder the Wii devel opment kit and the controller a game developer records a set of example raw motions for each motion that they want the player to be able to perform in the game Recording the motions simply involves using the controller to perform a motion and choosing which motions to save on the host PC disk The recorded motion signal is simply a sequence of numbers that represent the X Y Z accelerations of the Wii controller that has an associated label to s
5. are sometimes referred to herein as processed motions Raw motion signal Is the unprocessed motion signal Raw motion signals are sometimes referred to herein as motion signals Motion class A motion class is a set of motions recogniz able as distinct from other motion classes such as a cluster of motions generally distinguishable from other such clusters For example not intended to be limiting in any way there is aclass of motions that correspond to waving Any two waving motions could be quite different but there is some group family resemblance that means they are both examples of the class of waving motions Unknown class In any set of motion classes there is under stood to be the class of unknown or undetermined motions In these cases the unknown class is used herein to refer to all motions that are not examples of one of the set of said known classes Motion label A motion label includes a unique identifier for a motion class For example any motion that is deemed to be an example of the class of waving motions might be labeled waving Those skilled in the art would immediately recognize that some convenient synonym such as an integer or enum in a programming language could be used Labeled motion A labeled motion includes a raw or pro cessed motion signal that has been assigned a class label During the training phase in which a classifier is generated labels might be assigned by a human oper
6. device and further including determining if one of the motion recognizers does not satisfactorily recognize the motion signal 55 The method as recited in claim 54 wherein as the second handheld motion sensitive device is caused to move around the second motion signal results in a plurality of motions ranked according to respective classification rates 56 The method as recited in claim 37 wherein as the second handheld motion sensitive device is caused to move around the second motion signal results in a plurality of motions ranked according to respective classification rates 57 The method as recited in claim 37 including specifying a capacity for each of the generated motion recognizers US 7 702 608 B1 17 58 The method as recited in claim 37 including coupling an application wherein one or more end users of the appli cation are able to redefine one or more of the motion recog nizers in response to a new training set of motion signals or a modified existing training set of motion signals 59 The method as recited in claim 58 including generating the motion recognizers in response to one or more of the motion signals in the new training set or the modified existing training set created by the end user 1 60 The method as recited in claim 58 wherein Wherein the one or more end users are physically challenged 61 The method as recited in claim 37 including generating the motion recognizers in response to one
7. or 15 more of the motion signals in a training set representing one or more motions performed by teachers possessing some physical skills with the effect that one or more students can attempt to learn that same or similar physi cal skill by attempting to have their motions correctly classified by those motion recognizers 18 62 The method as recited in claim 37 including generating the motion recognizers in response to one or more of the motion signals in a training set representing one or more behaviors performed by animals 63 The method as recited in claim 62 including performing behavior modification on at least one of those animals in response to an output of one or more of the motion recognizers 64 The method as recited in claim 37 including generating the motion recognizers representing one or more behaviors performed by people wherein the behaviors are prescribed or proscribed 65 The method as recited in claim 64 including monitoring one or more persons for these behaviors that are prescribed or proscribed 66 The method as recited in claim 37 further comprising providing feedback that includes at least one measure of similarity or difference on how sets of motions compare to motions earlier used to generate or tune the motion recogniz 20 ers
8. the generality of this application TECHNICAL APPENDIX This application includes the following technical appen dix This document forms a part of this disclosure and is hereby incorporated by reference as if fully set forth herein The LiveMove user manual The user manual is written for game developers who want to use LiveMove in their game Among other things it explains how to use the development tools to generate motion classifiers and describes the libConsoleLM run time library API REFERENCES This application includes the following references Each of these documents forms a part of this disclosure and is hereby incorporated by reference as if fully set forth herein 1 E Keogh and M Pazzani Derivative Dynamic Time Warp ing in First SLAM International Conference on Data Min ing Chicago Ill 2001 2 Lawrence R Rabiner A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition Pro ceedings of the IEEE 77 2 p 257 286 February 1989 20 25 30 35 40 45 50 55 60 65 12 What is claimed is 1 A svstem for recognizing motions the svstem compris ing a computing unit configured to include one or more motion recognizer generators that generate a set of motion rec ognizers for use in video games in response to a first training set of first motion signals and at least one first handheld motion sensitive device gener ating the first training set of first m
9. which class the supplied motion belongs to Conflicts are typically resolved by majority vote or some measure based upon the distance If the supplied motion is not within the classification distance of any prototype the supplied motion s class is said to be undetermined That is the supplied motion is deemed to not be an example of any known class The invention extends the known techniques described in 1 by inventing an incremental version In particular the incre mental version can return the most likely classification before it has seen the entire motion signal When only a small amount of the signal has been seen there maybe several likely candidates but the inventors have discovered that it is often the case that well before the end of the motion signal there is only one likely remaining candidate This is an important enabling invention for games where the latency in known approaches could result in annoying pauses In the preferred embodiment there is a recommended tun ing step a new player can perform before beginning to play the game in earnest It is also recommended that the player repeat the tuning step whenever the recognition rates decline For example because the player is performing motions differ ently due to practice tiredness etc Whether the tuning step is undertaken is ultimately in the control of the game developer and the player But the inven tors have discovered that recognition rates are significantly
10. 608 B1 Page 2 6 467 085 6 477 553 6 561 811 6 636 860 6 640 231 6 892 349 7 054 928 2002 0165839 2003 004 1040 2003 0084015 2004 0010505 2006 000 1545 2006 0036398 U S PATENT DOCUMENTS Al Al 10 2002 11 2002 5 2003 10 2003 10 2003 5 2005 5 2006 11 2002 2 2003 5 2003 1 2004 1 2006 2 2006 Larsson Druck Rapoza et al Vishnubhotla Andersen et al Shizuka et al Segan et al Taylor et al Bertrand et al Beams et al Vishnubhotla WOE eisein h Funge et al 340 573 1 FOREIGN PATENT DOCUMENTS WO WO 2006 015234 A2 2 2006 OTHER PUBLICATIONS Welch Greg and Eric Foxlin Motion Tracking No Silver Bullet but a Respectable Arsenal Computer Graphics and Applications IEEE vol 22 Issue 6 Nov Dec 2002 pp 24 38 Statsoft Neural Networks Feb 13 1998 Verified by Wayback Machine Teknomo Kardi K Nearest Neighbors Tutorial Strengths and Weakness Numerical Example How KNN works Oct 16 2005 Verified by wayback machine EHow How to Play Simon Says Verified by wayback machine to Jun 29 2004 Kwon Doo Young and Markus Gross Combining Body Sensors and Visual Sensors for Motion Training ACM International Confer ence Proceeding Series vol 265 Proceedings of the 2005 ACM SIGCHI International Conference on Computer entertainment tech nology Jun 15 17 2005 cited by examiner U S Patent Apr 20 2010 Sheet 1 of 6 US 7 702 608 B1 100
11. 8 The method as recited in claim 37 wherein a time warp distance is calculated from the second motion signal to one ore more motion prototypes stored in one or more of the motion recognizers to determine a subset of the prototypes when the time warp distance is below a predefined distance threshold and wherein motion recognition of the second handheld motion sensitive device is responsive to the subset of the prototypes 39 The method as recited in claim 37 wherein the com puting unit is configured to determine for at least one of the motion recognizers a set of motion prototypes that are rep resentative of the first motion signals associated with the at least one of the motion recognizers 40 The method as recited in claim 37 wherein at least one of the motion recognizers generates a recognition signal in response to a prefix of the second motion signal when a time warp distance calculated from the second motion signal to one or more motion prototypes stored in a motion recognizer is compared to a modified distance threshold leading to an effect of providing relatively low latency incremental motion recognition 41 The method as recited in claim 37 wherein the first training set of first motion signals are a set of raw motion signals each describing one or more motions of the first handheld motion sensitive device 42 The method as recited in claim 41 further comprising processing the raw motion signals from motion sensors en
12. The system as recited in claim 1 wherein at least one of the motion recognizers generated in response to the first train ing set of motion signals includes a generalized version of one or more of the first motion signals is a prototype to which classification will be responsive 5 The system as recited in claim 3 wherein the second motion signal or one or more of the second set of motion signals is used to tune one or more motions from the training set into a generalized version of the one or more motions from the training set 6 The system as recited in claim 1 wherein at least one of the motion recognizers generates a recognition signal in response to a prefix of the second motion signal when a time warp distance calculated from the prefix of the second motion signal to one or more motion prototypes stored in a classifier is compared to a modified distance threshold leading to an effect of providing relatively low latency incremental motion recognition 7 The system as recited in claim 1 2 3 or 6 wherein one or more of the first motion signals in the training set represent one or more motions performed by one or more people involved in game development the second motion signal or one of the second set of motion signals represents one or more motions performed by one or more game players one or more of the motion recognizers are coupled to a game and are used to classify the second motion one or more resulting classificatio
13. US007702608B1 a2 United States Patent 10 Patent No US 7 702 608 B1 Bererton et al 45 Date of Patent Apr 20 2010 54 GENERATING MOTION RECOGNIZERS FOR 6 425 582 BL 7 2002 Rosi ARBITRARY MOTIONS FOR VIDEO GAMES AND TUNING THE MOTION RECOGNIZERS TO THE END USER Continued 75 Inventors Curt Bererton Burlingame CA US FOREIGN PATENT DOCUMENTS Daniel Dobson Atherton CA US WO WO 2006 014560 A2 2 2006 John Funge Sunnyvale CA US Charles Musick Belmont CA US Stuart Reynolds Palo Alto CA US Xiaoyuan Tu Sunnyvale CA US Ian Wright Mountain View CA US Wei OTHER PUBLICATIONS Yen Los Altos Hills CA US Continued Kjeldson Rick and John Kender Toward the Use of Gesture in l l ok Traditional User Interfaces Proceedings of the Second International 73 Assignee AiLive Inc Mountain View CA US Conference on Automatic Face and Gesture Recognition Oct 14 16 A s x 1996 pp 151 156 Notice Subject to any disclaimer the term of this PP patent is extended or adjusted under 35 Continued U S C 154 b by 911 days Primary Examiner David R Vincent 21 Appl No 11 486 997 Assistant Examiner Ben M Rifkin 74 Attorney Agent or Firm Joe Zheng 22 Filed Jul 14 2006 57 ABSTRACT 51 Int Cl GO6F 17 00 2006 01 A GOGN 5 02 2006 01 Generating motion recognizers from example motions with A63F 9 24 2006 01 out substantial programming wi
14. ace assets include sound files texture maps 3D models etc Those skilled in the art would immediately recognize this as standard practice for shipping games that depend on various assets Game Playing Step When the player starts playing the game that they have purchased or otherwise acquired the game will execute the sequence of steps it has been pro grammed to execute in response to the player s actions When the player starts the game or reaches some otherwise conve nient point in the game such as a new level the game will load in one of the previously generated classifiers As the player plays the game and performs motions with the Wii controller the game supplies the motions to the lib ConsoleLM run time library through the preprogrammed calls to the libConsoleLM run time library The libCon soleLM runtime library is also called by the game code to ask which motion the player has performed and the libCon soleLM run time library will return in real time or close to real time a label indicating which motion if any the player s input data corresponds to To make the determination the libConsoleLM runtime library uses its own internal logic and one of the classifiers it has access to In particular time warping is used to compare the distance between the supplied motion and one of the stored prototypes If a proto type is within its classification distance to the sup plied motion then that prototype is used to determine
15. articular type of customer such as expert tennis players versus small children The invention also obviously allows for some motions to be locked out or to be released by the player achieving some skill level in the game System Elements LiveMove Nintendo will soon release a new games console called the Wii One of the novel and interesting features of the Wii is the controller In particular the controller contains among other things accelerometers that can be used to record accelera tions over time in three dimensions as a player moves the controller through space Game developers imagine many exciting new uses and games for the Wii and the associated controller Many of those ideas revolve around being able to recognize which motions a player is performing However writing code to interpret the accelerometer data being relayed form the Wii controller is difficult The problem is difficult because the same motion can be quite different when performed by different people or even by the same person at different times In addition the motion recording device might introduce measurement errors or noise that can make it harder to recognize a motion Game developers using known techniques have therefore struggled to bring their game ideas to market The invention solves this problem by allowing game developers to create motion recognizers by simply providing examples of the motion to be recognized In a preferred embodiment not
16. assification might additionally assign probabilities possibly in response to additional factors that an unlabelled example is an example of each possible class in which case the assigned label is the class with greatest like lihood Motion prototype A motion prototype is a raw or pro cessed motion signal that has been chosen to be amember of the set of representative motions for some class of motion signals The number of prototypes that a motion recognizer or classifier can store is called the capacity of the motion recog nizer or classifier Adaptive smoothing Adaptive smoothing includes motion filtering techniques applied to a raw motion signal to generate acompressed representation referred to herein as a processed motion signal In a preferred embodiment the raw motion is split into segments and each segment is represented by the average value of the signal in that segment The length of the segment is determined adaptively according to the magnitude of the underlying raw motion signal In some embodiments the length of the segment is proportional the signal magnitude so that the higher the magnitude the shorter the segment higher magnitude signals intuitively indicate more informa tion content and hence the need for a higher sampling rate Motion recognizer software instructions capable of being interpreted by a computing device to recognize classes of motions Gesture A meaningful or expressive change in the posi
17. ator or other inter 0 20 25 35 40 45 50 60 4 face with domain knowledge of the motion signals Labels can also be implicit in the sense that a set of motions grouped together in some way can sometimes be assumed to all examples of some motion That is they are implicitly labeled as positive examples of some motion that may or may not have some additional way of describing it Training set A set of raw or processed motion signals used to generate a motion recognizer There are a wide variety of possible forms a training set can take and many structures that a training set can have For example not intended to be limiting in any way a collection of sets of motion classes or a set of labeled motions or a collection of unlabeled motions implicitly assumed to be positive examples of some motion class Classification rate A measure of motion recognizer per formance responsive to a set of statistical measures such as for example a number of false positives and false negatives Classification distance If a set of motions is arranged in ascending order of distance to some particular motion a classification distance for the particular motion is the distance to the first false positive in that set Classification Includes assigning a class label to an unla belled motion signal or prototype including the possibility that the assigned class label might be unknown undeter mined and the like Cl
18. cial case of rec ognizing motions What makes it a special case is that the set of motion classes is known in advance and all the motions are known ahead of time to be performed in a two dimensional plane For example in English there are 26 lowercase letters of the alphabet that are written on a flat writing surface Real world HWR recognition systems may include support for uppercase letters punctuation numerals and other gestures such as cut and paste At least some machine learning approaches to HWR are known and widely used but they do not solve the more general problem of generating motion recognizers in response to example motions At least some techniques for gesture recognition of limited symbols in computer games are also known For example various spell casting games allow players to perform gestures that are recognized as invocations for particular spells How ever the set of gestures is fixed in advance by using a pre programmed recognizer Moreover a movement is usually restricted to movement in a plane SUMMARY OF THE INVENTION The invention provides a way for developers and users to generate motion recognizers from example motions without substantial programming The invention is not limited to recognizing a fixed set of well known gestures as developers and users can define their own particular motions For example developers and users could choose to give example motions for their own made up alphabet that is unl
19. closed in the first handheld motion sensitive device being manipulated by a trainer 20 25 30 35 40 45 50 55 60 65 16 43 The method as recited in claim 42 wherein said pro cessing of the raw motion signals includes adaptively sampling the raw motion signals with an effect of making the raw motion signals easier to be stored or recognized 44 The method as recited in claim 38 further comprising tuning one of the previously generated motion recognizers in response to an additional training set 45 The method as recited in claim 44 wherein the turning of the previously generated motion recognizer is performed by a motion recognizer tuner that tunes one or more of the motion recognizers to make them more responsive to one or more motions included in the additional training set resulting in an effect of improving recognition performance on motion signals subsequently generated from a substantially similar source as the additional training set 46 The method as recited in claim 44 wherein said tuning of the previously generated motion recognizer includes selecting one or more of the motion signals from the addi tional training set as representative prototypes in place of orin addition to one or more of the prototypes in one or more of the motion recognizers being tuned 47 The method as recited in claim 44 further comprising capturing motions responsive to feedback from an entity involved in collecti
20. device such as a cell phone that can be used for playing games Console development kit or development kit A con sole development kit is a version of one or more game con soles used by game developers to develop their games that is either a version of a single game console or a version capable of emulating different game consoles It is ostensibly the same as the final console that the game will run on but typically has additional features to help game development such as file input and output hookup to an integrated devel opment environment hosted on another computer and the like Host PC or host computer During game development on consoles it is customary to have a console development kit attached to a host PC For example the compiler might run on a PC running a version of Microsoft Windows to generate an executable The executable then gets run on the console by transferring it across some connection such as a USB cable to the console Output from the console then appears ona TV screen with the option to have printed messages for debug ging purposes sent back to the host PC for display Development time The time during which the game is developed that is before it ships to end users However development may even continue after shipping with the effect that upgrades and bug fixes might be released as patches Game time The time when the game is being run that is played by an end user The scope and spiri
21. fect of tuning the motion recognizers accordingly 19 The system as recited in claim 14 wherein the motion recognizer tuner is capable of performing at least one of the following causing the motion recognizers to be responsive to the additional training set removing the motion recogniz ers and merging the motion recognizers 20 The system as recited in claim 1 wherein one or more of the motion recognizers generate motion recognition sig nals for one or more new motion signals in response to an incremental distance comparison to potential representative prototypes 20 25 30 35 40 45 50 55 60 65 14 21 The system as recited in claim 1 further comprising memory or mass storage coupled to the computing unit wherein the memory or mass storage is accessible by another computing device for use of the generated motion recognizers therein 22 The system as recited in claim 1 wherein one or more of the motion recognizer generators are sent over a network for use on another computing device 23 The system as recited in claim 22 wherein the motion recognition signal includes a class label to identify a particu lar motion generated from the second handheld motion sen sitive device 24 The system as recited in claim 23 wherein the motion recognition signal includes a list of class labels ranked in response to a measure of relative likelihood that one of the second set of motion signals is a membe
22. ffect or purpose as the first reasons or structures or techniques After reading this application those skilled in the art would see the generality of this description DEFINITIONS The general meaning of each of these following terms is intended to be illustrative and not in any way limiting Motion The action or process of changing position This includes intentional and meaningful motions such as twist ing ones wrist to simulate using a screwdriver as well as unintentional motions such as wobbling some people might exhibit when drunk Motion signal A motion signal is information such as time series data that describes some motion over a predefined time The data can take many forms For example not intended to be limiting in any way positions of an object over time orientations ofan object over time accelerations experienced by an object over time forces experienced by an object over time data expressed in a frequency domain data expressed in a parameterized domain suchas R or R and the like Motion signals are sometimes referred to as motions As used herein a motion signal might refer herein to a processed motion signal or a raw motion signal Processed motion signal A processed motion signal is a motion signal that has been filtered or transformed in some way For example adaptively smoothing the signal or trans forming the signal into a frequency domain using a Fourier or other transform Processed motion signals
23. he motion recognizers are used to classify one or more motions performed by the one or more players and one or more resulting classifications are coupled to events in the game 31 The system as recited in claim 30 wherein the motion recognizers generated in response to the first training set of motion signals represent motions of a first set of players which are used to recognize motions generated by a different set of players with an effect that the game is responsive to one or more of the different sets of players mimicking one or more of the motions of the first set of players 32 The system as recited in claim 29 wherein one or more of the second set of motion signals represent one or more motions performed by one or more disabled people one or more of the motion recognizers are coupled to an application to learn a correspondence between motions a disabled person is able to perform and a meaning asso ciated with those motions and one or more of the motion recognizers are used to classify one or more of the motions performed by the disabled users of the application US 7 702 608 B1 15 33 The system as recited in claim 1 wherein one or more of the generated motion recognizers are coupled to a training program designed to guide or help or teach people to learn a physical skill 34 The system as recited in claim 1 wherein one or more of the generated motion recognizers are coupled to an animal behavior monitoring app
24. ibrator 107 and ImRecorder 106 To create motion examples 103 the game developer runs ImRecorder 106 Then as the developer or someone hired bv the developer performs motions with the controller the motions are recorded and saved to a disk or some other suitable media as motion examples 103 ImRecorder 106 can also provide feedback on the motions generated to help the user of the motion input device obtain the examples being desired Thus only when a desired motion has been performed is it saved It shall be noted that ImRecorder 106 can alternatively be compiled into a developer s game 108 or some other suitable application as a library so that the collection of raw motions can be performed within the context of the game if the devel oper so desires Another application called ImMaker runs on the host com puter The example motions 103 can be read in by ImMaker 102 running on the host PC 101 to create classifiers 104 In particular the developer uses ImMaker 102 to select motions and assign corresponding labels to the classifiers In addition ImMaker provides additional summary information on the motions For example which orientation the motion device was being held etc Once the classifiers 104 have been generated they can then be read straight back in to ImMaker 102 for immediate test ing This allows for a very fast prototyping to maximize game developer creativity The classifiers 104 can also be loaded by console a
25. ike any known alphabet and the invention will generate a motion recognizer for that unique alphabet The invention is also not limited to motions that occur sub stantially in a plane or are substantially predefined in scope The invention allows a developer to generate motion rec ognizers by providing one or more example motions for each class of motions that must be recognized Machine learning techniques are then used to automatically generate one or more motion recognizers from the example motions Those motion recognizers can be incorporated into an end user 20 25 30 35 40 45 50 55 60 65 2 application with the effect that when a user of the application supplies a motion those motion recognizers will recognize the motion as an example of one of the known classes of motion In the case that the motion is not an example of a known class of motion those motion recognizers can collec tively recognize that fact by responding that the motion is unknown In another use of the invention the ability to tune a motion recognizer can be incorporated into an end user application In this case not just the application developers but also any users of the end user application can add their own new example motions The recognizer can then be tuned to improve recognition rates for subsequent motions from those users In another use of the invention the ability to generate or alter a motion recognizer can be inc
26. intended to be limiting in any way the invention is embodied in a commercially avail able product called LiveMove LiveMove provides a video game with the ability to recognize any player s motions per formed using the accelerometers in Nintendo s Wii remote controllers LiveMove Components libConsoleLM run time library Is a run time library that is designed to be linked into the developer s game Those skilled in the art would immediately recognize this as stan dard practice for using third party libraries libConsoleLM header files Define the LiveMove API that the developer can use to insert calls to the libConsoleLM run time library into their game source code Those skilled in the art would immediately recognize this as standard practice for using third party libraries ImRecorder application Is an application that runs on the Wii development kit that records data from the Wii controllers onto the hard drive of a standard PC the host PC that is connected to the development kit Those skilled in the art would immediately recognize this as a standard approach to saving out data created on the Wii development kit ImMaker Live Move classifier maker application Is an application that runs on a standard PC the host PC which is used to create motion prototypes and motion classifiers One embodiment of the invention includes the LiveMove run time library called libConsoleLM a classifier generation application called ImMaker
27. is too far from any stored prototype 405 it will simply reject the new example and the human will have to provide an alternative If the prototype is acceptable and the classifier has enough capacity 406 to store the new example then the example may be stored in the classifier as a new proto type 407 The new classifier can then be saved out to a disk 408 or any other suitable storage media available locally or over the network Tuning could occur at development time to tweak an exist ing classifier But at development time the developer could just add the new motion prototypes to the previous set of prototypes and re generate the classifier as in FIG 2 So the intended use of modifying a classifier is by the player after the game has been shipped In particular players who have pur chased the game can add some of their own motion prototypes to the classifier The inventors have discovered that this ability significantly boosts subsequent classification rates More generally there is a chain of distribution between the developer and the end user and it might be desirable for one or more people in that chain including say to make modi fications For example not intended to be limiting in any way these could include parents with a security code a value added reseller a consultant hired to tailor the game to a 0 a 5 20 25 30 40 45 55 65 8 particular end user a retailer tailoring the game to a p
28. it configured to execute an end user application 13 The system as recited in claim 12 wherein one of the input devices is a controller handheld and moved around manually 14 The system as recited in claim 4 wherein the comput ing unit is configured to include a motion recognizer tuner that modifies a previously generated motion recognizer in response to an additional training set 15 The system as recited in claim 14 wherein the motion recognizer tuner tunes one or more of the motion recognizers to make them more responsive to one or more motions included in the additional training set with an effect of improving recognition performance on the second motion signals generated from a substantially similar source as the additional training set 16 The system as recited in claim 15 wherein the addi tional training set includes one or more motions that represent motions of a celebrity and the resulting tuned recognizer corresponds to a motion recognizer henceforth associated with the celebrity 17 The system as recited in claim 15 wherein the motion recognizer tuner tunes one or more of the first motion signals and provides representative prototypes in place of or in addi tion to one or more of the prototypes corresponding to one or more of the motion recognizers being tuned 18 The system as recited in claim 14 wherein the addi tional training set is responsive to feedback from at least one earlier collected motion with an ef
29. l ra S U a classifier 2 Ben s classifter LiveMove library game data player tuned procter eee eee Figure 6 Tuning setup US 7 702 608 B1 1 GENERATING MOTION RECOGNIZERS FOR ARBITRARY MOTIONS FOR VIDEO GAMES AND TUNING THE MOTION RECOGNIZERS TO THE END USER BACKGROUND OF THE INVENTION 1 Field of the Invention The invention relates to machine learning especially in the context of generating motion recognizers from example motions in some embodiments a set of generated motion recognizers can be incorporated into end user applications with the effect that those applications are capable of recog nizing motions 2 Related Art Writing program code to recognize whether a supplied motion is an example of one of an existing set of known motion classes or motion types can be difficult This is because the representation of a motion can often be counter intuitive For example if a motion is created with a device containing at least one accelerometer relating the resulting data to an intuitive notion of the motion performed can be extremely difficult with known techniques The problem is difficult because the same motion can be quite different when performed by different people or even by the same person at different times In addition the motion recording device might introduce measurement errors or noise that can make it harder to recognize a motion Handwriting recognition HWR is a spe
30. layer tries to perform the same motion such that the said classifier successfully recognizes the said motion as an instance of the same motion type as the proto types Setup for Tuning a Classifier FIG 6 shows the setup 600 for tuning a classifier The classifiers provided by the developer 603 are stored on disc or can be downloaded over the network as downloadable content and etc These classifiers are then loaded by the game 606 that is running on the console 604 The players then use the wireless controllers 602 to perform their versions of the predefined moves 601 The run time library 607 then uses the new example moves to tune the classifiers 603 to create ver sions tuned for individual users 605 The tuned classifiers 605 can then be saved out to a memory card or some other con venient storage medium Process for Tuning a Classifier FIG 4 shows the process 400 of tuning a classifier The classifiers are initially loaded 402 by an application e g a game Next a human tunes the classifier by providing labeled examples 403 that represent his her interpretation of the motions the classifier already knows how to classify The human can continue to provide new examples until he she is happy with the classification performance or the application decides enough tuning has been completed The new examples provided by the human will typically be smoothed 404 before trying to classify it Ifthe classifier determines the new example
31. lication 35 The system as recited in claim 1 wherein one or more of the generated motion recognizers are coupled to a law enforcement monitoring application 36 The system as recited in claim 1 wherein one or more of the motion recognizers provide feedback in response to a measure of comparison between possibley new motions anda set of motions used to generate or tune the motion recogniz ers 37 A method for recognizing motions the method com prising generating a set of motion recognizers for use in videoga mes in response to a first training set of first motion signals and associating each of the motion recognizers with one or more of the first motion signals wherein each of the first set of motion signals describes a motion of a trainer manipulating at least one first handheld motion sensi tive device over a period of time and generating for each of the motion recognizers a motion recognition signal in a computing unit in response to a second motion signal from a second handheld motion sensitive device generating a second set of motion signals from the second handheld motion sensitive device when the second handheld motion sensitive device is manipulated by an end user wherein each of the second set of motion signals describes at least one motion of the second hand held motion sensitive device over a period of time and tuning the motion recognizers in response to one or more of the second set of motion signals 3
32. n using LiveMove can be found in the incorporated disclosure the LiveMove manual Generality of the Invention This invention should be read in the most general possible form This includes without limitation the following possi bilities included within the scope of or enabled by the inven tion In one set of embodiments extensions of the invention might allow players to generate their own motion recognizers from scratch This might be performed by re compiling the libConsoleLM runtime library to incorporate the code used in ImMaker to generate classifiers In one set of embodiments extensions of the invention might enable a completely new class of games For example a team based Simon Says game that is a synchronized motions game in which a team of players competes against another team of players each with a controller in hand The prototype motion is the captured data of all of the first teams motion data over time The opposing team has to mimic the motion The contest would be like a sporting event the syn chronized motion Olympics The invention might be used to help people who are severely disabled but still have gross motor control but not fine control In particular they could then type via the motion recognition interface The ability to define your own motions means that they can settle on motions that are easy and comfortable for them to perform After reading this application those skilled in the art would see
33. ng the motions with an effect of tuning the motion recognizers to be either more specific or less specific 48 The method as recited in claim 45 wherein the motion recognizer tuner performs at least one of operations adding new motion recognizers responsive to the addi tional training set removing the motion recognizers or merging the motion recognizers 49 The method as recited in claim 38 wherein one or more motion recognizers perform calculating an incremental mea sure of distance comparison between one or more new motion signals and potential representative prototypes 50 The method as recited in claim 37 including saving one or more of the motion recognizers in a storage medium and making the generated motion recognizers available for use on another computing device 51 The method as recited in claim 37 including sending one or more of the generated motion recognizers over a net work for use on one or more other computing devices 52 The method as recited in claim 51 including comput ing a class label as a recognition signal for the second motion signal 53 The method as recited in claim 52 including computing a list of possible class labels ranked by a mea sure of relative likelihood that the second motion signal is a member of a class 54 The method as recited in claim 37 wherein the second motion signal is a sequence of numbers that represent X Y Z accelerations of the second handheld motion sensitive
34. ns are coupled to events in the game US 7 702 608 B1 13 8 The system as recited in claim 1 wherein the first train ing set of motion signals are a set of raw motion signals each describing one or more motions of the first handheld motion sensitive device 9 The system as recited in claim 8 wherein the computing unit is configured to include a component to process the raw motion signals to filter out undesired effect therein 10 The system as recited in claim 9 wherein the process ing of the raw motion signals includes compressing the raw motion signals adaptively sampling the raw motion signals modifying the raw motion signals to make the raw motion signals more similar to one or more previously observed motions or filtering the raw motion signals in order to make the raw motion signals easier to store or recognize 11 The system as recited in claim 8 wherein the raw motion signals are generated in besides the first handheld motion sensitive device one or more input devices respon sive to one or more humans animals or machines an output of executed program code the raw motion signals generated at different locations dates and times stored on storage media and made available locally or over a network 12 The system as recited in claim 8 wherein the raw motion signals are generated in one or more input devices that include one or more accelerometers and transmit the raw motion signals wirelessly to the computing un
35. orporated into an end user application In this case not just the application developers but also any users of the end user application can generate their own recognizers from any combination of existing motions their own new motions or both When the generated motion recognizer includes elements of previous motion rec ognizers or is responsive to existing motions the newly generated motion recognizer can be thought of as an alter ation or modification of the previously existing motion rec ognizers The ability for users of an application to tune or generate their own motion recognizers is an enabling technology for a wide class of applications that while possibly previously imagined were not feasible Although many potential applications of motion recogni tion are known the invention is an enabling technology fora wide class of applications BRIEF DESCRIPTION OF THE DRAWINGS FIG 1 shows the different components of a preferred embodiment in relation to one another FIG 2 shows a process of classifying a new motion FIG 3 shows a process of generating a new classifier in response to a set of labeled examples FIG 4 shows a process of tuning a classifier FIG 5 shows a typical setup that a developer might use when developing a console game and FIG 6 shows a setup for tuning a classifier DETAILED DESCRIPTION Generality of the Description This application should be read in the most general pos sible form This incl
36. otion signals when the first handheld motion sensitive device is being manipu lated bv a trainer each of the first motion signals describ ing a motion of the first handheld motion sensitive device over a period of time each of the motion recog nizers configured to generate a motion recognition sig nal in response to a second motion signal from a second handheld motion sensitive device wherein the computing unit is configured to tune the motion recognizers in response to one or more of a second set of motion signals generated from the second handheld motion sensitive device when the second handheld motion sensitive device is manipulated by an end user each of the second set of motion signals describing at least one motion of the second handheld motion sensitive device over a period of time 2 The system as recited in claim 1 wherein a time warp distance is calculated from the second motion signal to one or more motion prototypes stored in one or more of the motion recognizers to determine a subset of the prototypes when the time warp distance is below a predefined distance threshold and wherein motion recognition of the second handheld motion sensitive device is responsive to the subset of the prototypes 3 The system as recited in claim 1 wherein the second motion signal is an actual motion signal from the second handheld motion sensitive device used to interact with a game integrated with some or all of the motion recognizers 4
37. pecify which motion it is an example of Processed Motion Creation Step Processed motions are created by adaptively smoothing the raw motions They are simply a compressed version of the raw motions that are convenient easier and faster to work with The processed motion can optionally contain the raw motion from which it was created Raw and processed motions will sometimes be referred to simply as motions Motion Classifier Creation Step Using ImMaker a game developer will select which set of labeled example motions to use to create a classifier The set of selected examples is sometimes referred to as a training set Once a classifier is created it is saved onto the disk of the host PC To generate a classifier each example motion is examined in turn To each of these motions the time warped distance is computed to each of the other motions Where the time warped distance used is roughly the same as the one described in 1 As each motion is examined in turn if it is within some prespecified distance of another motion then it is classified as an instance of that other motion For each motion we there fore end up with a classification of all the other motions By comparing the assigned classification with the actual class label the classification rate can be determined where the classification rate is a measure of the number of false positives versus the number of false negatives All the motions can thus be ranked according to their
38. pplica tions such as the game 108 or ImCalibrator 107 On the console 105 the classifiers 104 can be used by the LievMove library 109 to classify new motions They can also be tuned to improve their performance which will be further detailed below with reference to FIG 4 Classifying New Motions FIG 2 shows a process 200 of classifying a new motion 202 The raw motion signal is possibly filtered 203 for example using adaptive smoothing and then the time warp distance to the prototypes 204 stored in the classifier is computed If no prototypes are within any prototype s classification distance 205 then the motion 202 is labeled as unknown or undeter mined 206 If there is only one proto type for which the motion 202 is within the prototype s classification distance then the motion 202 is labeled with the label associated with the said prototype If there is more than one candidate proto type 207 then the best prototype used to assign the label 210 is picked by majority vote or is the one with the smallest distance 209 The game can use the label determined by the classifier to drive an animation change the game state etc Those skilled in the art would recognize that generated classifiers motion can be arranged in a hierarchy For example one set of classifiers may determine if a motion was a punch Then if additional information was required a sec ond set of classifiers could be called upon to determine if the punch was
39. r of a class 25 The system as recited in claim 23 wherein the motion recognition signal includes at least one response indicating that none of the motion recognizers recognize the second motion signal or one of the second set of motion signals 26 The system as recited in claim 1 wherein the motion recognition signal is a sequence of numbers that represent X Y Z accelerations of the second handheld motion sensitive device 27 The system as recited in claim 2 wherein as the second handheld motion sensitive device is caused to move around the second set of motion signals result in a plurality of motions ranked according to respective classification rates 28 The system as recited in claim 26 wherein the motion recognizer generators create the motion recognizers in vary ing capacity 29 The system as recited in claim 1 further comprising a coupled application being executed in another computing unit wherein one or more end users of the application are able to redefine one or more of the motion recogniz ers in response to either a new training set or a modified existing training set 30 The system as recited in claim 29 wherein the coupled application includes a game played by one or more game players with one or more controllers gener ating various motion signals the second motion signal or one of the second set of motion signals represents one or more motions performed by the one or more game players one or more of t
40. respective classification rates The top n classifiers are chosen to be prototypes for the class where n is an integer number e g 1 2 3 4 The generation of classifiers has a number of tunable parameters such as the classification rate that must be set in advance Currently the inventors have assigned these values but those skilled in the art would quickly realize that expert users could easily be given access to these settings so that they can experiment for themselves libConsoleLM Incorporation Step A game developer will insert the required API calls into their code by including the libConsoleLM header files and making calls to the functions contained therein and link in the libConsoleLM run time library Those skilled in the art would immediately recognize this as standard practice for using third party libraries Game Shipping Step As part of the usual process of ship ping a game a developer will store a compiled version of the game source code onto some media so that they accessible to the game during game play Not intended to be limiting in any way examples include saving the classifiers on DVD memory cards or servers accessible over some network 20 40 45 50 55 65 10 The game will incorporate the libConsoleLM run time library The created classifier will also be distributed along with the game From the developer s point of view the clas sifier is one of the game s assets Other more commonpl
41. t of the invention is not limited to any of these definitions or to specific examples mentioned therein but is intended to include the most general concepts embodied by these and other terms Developer Setup FIG 5 shows a typical setup 500 that a developer uses when developing a console game The console development kit 502 is almost the same as the console that the game will run on when it is finally shipped but may have some additional features to assist development The term console and console development kit can therefore be largely used interchangeably The controller 504 is con nected to the console development kit 502 by a wired or wireless connection The controller is moved around by a human 505 who may be the game developer or someone hired by the developer The console development kit 502 can communicate with a host computer 501 that is usually a standard PC The console 502 is also attached to a display device such as a TV screen 503 System Components FIG 1 shows different components of a preferred embodi ment 100 in relation to one another ImMaker 102 is an application that runs on a host PC ImRecorder 106 and ImCalibrator 107 are distributed as sample applications that can be compiled and run on the Nintendo Wii console development kit 105 The run time 20 40 45 60 65 6 librarv 109 will be compiled and linked in with all applica tions that use LiveMove on the console i e the game 108 ImCal
42. thout limitation to any fixed A63F 13 00 2006 01 set of well known gestures and without limitation to motions G06F 19 00 2006 01 that occur substantially in a plane or are substantially pre CILTS A 706 46 463 37 fined in scope From example motions for each class of 58 Field of Classification Search i 706 46 motion to be recognized a system automatically generates motion recognizers using machine learning techniques Those motion recognizers can be incorporated into an end 56 References Cited user application with the effect that when a user of the appli cation supplies a motion those motion recognizers will rec ognize the motion as an example of one of the known classes See application file for complete search history U S PATENT DOCUMENTS 5 778 157 A 7 1998 Oatman et al of motion Motion recognizers can be incorporated into an 5 995 955 A 11 1999 Oatman et al end user application tuned to improve recognition rates for 6 192 338 BI 2 2001 Haszto et al subsequent motions to allow end users to add new example 6 216 014 BL 4 2001 Proust et al monon 6 363 384 B1 3 2002 Cookmeyer II et al 6 389 405 B1 5 2002 Oatman et al 66 Claims 6 Drawing Sheets 603 is REZA 605 8 SOE N N A load i i save classifier L m game m Jill s classifier i T classifier 2 i Ben s classifier A game data i AE sF a ii i player tuned Tuning setup US 7 702
43. tion of the body or a part of the body For example not intended to be limiting in any way waving drawing a letter of the alpha bet trying to lasso a horse Gestures include motions but not all motions are necessarily gestures Classifier As used herein this term generally refers to software instructions capable of being interpreted by a com puting device to perform classification A classifier might also function by assigning probabilities that the possible class instance is an example of each possible class A classifier might also be allowed to determine that a possible class instance is in fact not an instance of any known class Tuning As used herein tuning a classifier involves provid ing additional labeled examples of pre existing motion US 7 702 608 B1 5 classes The purpose of tuning is to improve recognition rates for example to reduce the number of false positives or false negatives Game developer Anyone involved in the creation of a video game As used herein this might include but is not necessarily limited to a game programmer an AI program mer a producer a level designer a tester a hired contractor an artist a hired motion actor and the like Console One or more devices used for playing a video game For example not intended to be limiting in any way one of the following Playstation PlayStation 2 Playstation 3 XBox XBox 360 GameCube Wii PSP Dual Screen PC Mac Game Boy any other
44. udes without limitation the following References to specific structures or techniques include alternative and more general structures or techniques espe cially when discussing aspects of the invention or how the invention might be made or used References to preferred structures or techniques gener ally mean that the inventor s contemplate using those struc tures or techniques and think they are best for the intended application This does not exclude other structures or tech niques for the invention and does not mean that the preferred structures or techniques would necessarily be preferred in all circumstances References to first contemplated causes and effects for some implementations do not preclude other causes or effects that might occur in other implementations even if completely contrary where circumstances would indicate that the first US 7 702 608 B1 3 contemplated causes and effects would not be as determina tive of the structures or techniques to be selected for actual use References to first reasons for using particular structures or techniques do not preclude other reasons or other structures or techniques even if completely contrary where circum stances would indicate that the first reasons and structures or techniques are not as compelling In general the invention includes those other reasons or other structures or techniques especially where circumstances indicate they would achieve the same e
Download Pdf Manuals
Related Search