In the past, computing applications such as computer games and multimedia applications used controls to allow users to manipulate game characters or other aspects of an application. Typically such controls are input using, for example, controllers, remotes, keyboards, mice, or the like. More recently, computer games and multimedia applications have begun employing cameras and software gesture recognition engines to provide a human computer interface (“HCI”). With HCI, user gestures are detected, interpreted and used to control game characters or other aspects of an application.
In conventional gaming and multimedia applications, HCI is used to measure on a pass/fail basis whether or not a user has adequately performed a given gesture in response to a prompt or scenario. By contrast, conventional systems do not measure how the gesture was performed. As long as the HCI system determines the requested gesture is performed to a threshold level, the user is rewarded pursuant to the game/application metric. However, a user's movements may provide a wealth of information above and beyond simply whether or not a requested gesture was performed to a threshold level. Different users perform gestures in different ways. Some may perform a given gesture more gracefully than others. Some may try harder and exert more effort in performing a gesture than others. Conventional HCI systems do not take these parameters into account when measuring the pass/fail status of a given gesture.
Disclosed herein are systems and methods for determining whether a given gesture was performed with a particular style. This additional information may then be used to personalize a gaming or multimedia experience, rewarding users for their individual style. In one embodiment, the present technology relates to a gaming system including an image capture device for capturing data relating to motion of a user, a computing environment for receiving image data from the capture device and for hosting a gaming application, and an audiovisual device, coupled to the computing environment.
The computing environment includes a first order gesture recognition engine for receiving data relating to the motion of a user, and determining on a pass/fail basis whether the motion of the user qualifies as a predefined gesture. The computing environment further includes a second order gesture recognition engine for receiving the data or information derived from the data. The second order gesture recognition engine determines, in addition to a threshold determination of whether the motion of the user qualifies as a predefined gesture, whether the motion of the user includes a stylistic attribute which qualifies as a predefined style associated with the user motion. The computing environment further stores a set of rules that are used by the second order gesture recognition engine. The set of stored rules include definitions of when a predefined set of user motions is to be interpreted as a predefined style.
The audiovisual device presents a graphical representation of the user and the user's motion based on information received from the computing environment. The graphical representation of the user or user's surrounding may be enhanced by showing a user's motion with graphics representing a style determined to exist by the second order gesture recognition engine.
Embodiments of the present technology will now be described with reference to
Referring initially to
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Other movements by the user 18 may also be interpreted as other controls or actions, such as controls to bob, weave, shuffle, block, jab, or throw a variety of different power punches. Moreover, as explained below, once the system determines that a gesture is one of a punch, bob, weave, shuffle, block, etc., additional qualitative aspects of the gesture in physical space may be determined These qualitative aspects can affect how the gesture (or other audio or visual features) are shown in the game space as explained hereinafter.
In example embodiments, the human target such as the user 18 may have an object. In such embodiments, the user of an electronic game may be holding the object such that the motions of the player and the object may be used to adjust and/or control parameters of the game. For example, the motion of a player holding a racket may be tracked and utilized for controlling an on-screen racket in an electronic sports game. In another example embodiment, the motion of a player holding an object may be tracked and utilized for controlling an on-screen weapon in an electronic combat game.
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According to another embodiment, the capture device 20 may include two or more physically separated cameras that may view a scene from different angles, to obtain visual stereo data that may be resolved to generate depth information.
The capture device 20 may further include a microphone 30. The microphone 30 may include a transducer or sensor that may receive and convert sound into an electrical signal. According to one embodiment, the microphone 30 may be used to reduce feedback between the capture device 20 and the computing environment 12 in the target recognition, analysis, and tracking system 10. Additionally, the microphone 30 may be used to receive audio signals that may also be provided by the user to control applications such as game applications, non-game applications, or the like that may be executed by the computing environment 12.
In an example embodiment, the capture device 20 may further include a processor 32 that may be in operative communication with the image camera component 22. The processor 32 may include a standardized processor, a specialized processor, a microprocessor, or the like that may execute instructions that may include instructions for receiving the depth image, determining whether a suitable target may be included in the depth image, converting the suitable target into a skeletal representation or model of the target, or any other suitable instruction.
The capture device 20 may further include a memory component 34 that may store the instructions that may be executed by the processor 32, images or frames of images captured by the 3-D camera or RGB camera, or any other suitable information, images, or the like. According to an example embodiment, the memory component 34 may include random access memory (RAM), read only memory (ROM), cache, Flash memory, a hard disk, or any other suitable storage component. As shown in
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Additionally, the capture device 20 may provide the depth information and images captured by, for example, the 3-D camera 26 and/or the RGB camera 28, and a skeletal model that may be generated by the capture device 20 to the computing environment 12 via the communication link 36. A variety of known techniques exist for determining whether a target or object detected by capture device 20 corresponds to a human target. Skeletal mapping techniques may then be used to determine various spots on that user's skeleton, joints of the hands, wrists, elbows, knees, nose, ankles, shoulders, and where the pelvis meets the spine. Other techniques include transforming the image into a body model representation of the person and transforming the image into a mesh model representation of the person.
The skeletal model may then be provided to the computing environment 12 such that the computing environment may track the skeletal model and render an avatar associated with the skeletal model. The computing environment may further determine which controls to perform in an application executing on the computer environment based on, for example, gestures and gesture styles of the user that have been recognized from the skeletal model. For example, as shown, in
A graphics processing unit (GPU) 108 and a video encoder/video codec (coder/decoder) 114 form a video processing pipeline for high speed and high resolution graphics processing. Data is carried from the GPU 108 to the video encoder/video codec 114 via a bus. The video processing pipeline outputs data to an A/V (audio/video) port 140 for transmission to a television or other display. A memory controller 110 is connected to the GPU 108 to facilitate processor access to various types of memory 112, such as, but not limited to, a RAM.
The multimedia console 100 includes an I/O controller 120, a system management controller 122, an audio processing unit 123, a network interface controller 124, a first USB host controller 126, a second USB host controller 128 and a front panel I/O subassembly 130 that are preferably implemented on a module 118. The USB controllers 126 and 128 serve as hosts for peripheral controllers 142(1)-142(2), a wireless adapter 148, and an external memory device 146 (e.g., flash memory, external CD/DVD ROM drive, removable media, etc.). The network interface 124 and/or wireless adapter 148 provide access to a network (e.g., the Internet, home network, etc.) and may be any of a wide variety of various wired or wireless adapter components including an Ethernet card, a modem, a Bluetooth module, a cable modem, and the like.
System memory 143 is provided to store application data that is loaded during the boot process. A media drive 144 is provided and may comprise a DVD/CD drive, hard drive, or other removable media drive, etc. The media drive 144 may be internal or external to the multimedia console 100. Application data may be accessed via the media drive 144 for execution, playback, etc. by the multimedia console 100. The media drive 144 is connected to the I/O controller 120 via a bus, such as a Serial ATA bus or other high speed connection (e.g., IEEE 1394).
The system management controller 122 provides a variety of service functions related to assuring availability of the multimedia console 100. The audio processing unit 123 and an audio codec 132 form a corresponding audio processing pipeline with high fidelity and stereo processing. Audio data is carried between the audio processing unit 123 and the audio codec 132 via a communication link. The audio processing pipeline outputs data to the A/V port 140 for reproduction by an external audio player or device having audio capabilities.
The front panel I/O subassembly 130 supports the functionality of the power button 150 and the eject button 152, as well as any LEDs (light emitting diodes) or other indicators exposed on the outer surface of the multimedia console 100. A system power supply module 136 provides power to the components of the multimedia console 100. A fan 138 cools the circuitry within the multimedia console 100.
The CPU 101, GPU 108, memory controller 110, and various other components within the multimedia console 100 are interconnected via one or more buses, including serial and parallel buses, a memory bus, a peripheral bus, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures can include a Peripheral Component Interconnects (PCI) bus, PCI-Express bus, etc.
When the multimedia console 100 is powered ON, application data may be loaded from the system memory 143 into memory 112 and/or caches 102, 104 and executed on the CPU 101. The application may present a graphical user interface that provides a consistent user experience when navigating to different media types available on the multimedia console 100. In operation, applications and/or other media contained within the media drive 144 may be launched or played from the media drive 144 to provide additional functionalities to the multimedia console 100.
The multimedia console 100 may be operated as a standalone system by simply connecting the system to a television or other display. In this standalone mode, the multimedia console 100 allows one or more users to interact with the system, watch movies, or listen to music. However, with the integration of broadband connectivity made available through the network interface 124 or the wireless adapter 148, the multimedia console 100 may further be operated as a participant in a larger network community.
When the multimedia console 100 is powered ON, a set amount of hardware resources are reserved for system use by the multimedia console operating system. These resources may include a reservation of memory (e.g., 16 MB), CPU and GPU cycles (e.g., 5%), networking bandwidth (e.g., 8 kbs), etc. Because these resources are reserved at system boot time, the reserved resources do not exist from the application's view.
In particular, the memory reservation preferably is large enough to contain the launch kernel, concurrent system applications and drivers. The CPU reservation is preferably constant such that if the reserved CPU usage is not used by the system applications, an idle thread will consume any unused cycles.
With regard to the GPU reservation, lightweight messages generated by the system applications (e.g., popups) are displayed by using a GPU interrupt to schedule code to render popup into an overlay. The amount of memory required for an overlay depends on the overlay area size and the overlay preferably scales with screen resolution. Where a full user interface is used by the concurrent system application, it is preferable to use a resolution independent of the application resolution. A scaler may be used to set this resolution such that the need to change frequency and cause a TV resynch is eliminated.
After the multimedia console 100 boots and system resources are reserved, concurrent system applications execute to provide system functionalities. The system functionalities are encapsulated in a set of system applications that execute within the reserved system resources described above. The operating system kernel identifies threads that are system application threads versus gaming application threads. The system applications are preferably scheduled to run on the CPU 101 at predetermined times and intervals in order to provide a consistent system resource view to the application. The scheduling is to minimize cache disruption for the gaming application running on the console.
When a concurrent system application requires audio, audio processing is scheduled asynchronously to the gaming application due to time sensitivity. A multimedia console application manager (described below) controls the gaming application audio level (e.g., mute, attenuate) when system applications are active.
Input devices (e.g., controllers 142(1) and 142(2)) are shared by gaming applications and system applications. The input devices are not reserved resources, but are to be switched between system applications and the gaming application such that each will have a focus of the device. The application manager preferably controls the switching of input stream, without knowledge of the gaming application's knowledge and a driver maintains state information regarding focus switches. The cameras 26, 28 and capture device 20 may define additional input devices for the console 100.
In
The computer 241 may also include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only,
The drives and their associated computer storage media discussed above and illustrated in
The computer 241 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 246. The remote computer 246 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 241, although only a memory storage device 247 has been illustrated in
When used in a LAN networking environment, the computer 241 is connected to the LAN 245 through a network interface or adapter 237. When used in a WAN networking environment, the computer 241 typically includes a modem 250 or other means for establishing communications over the WAN 249, such as the Internet. The modem 250, which may be internal or external, may be connected to the system bus 221 via the user input interface 236, or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer 241, or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,
As indicated above, gesture recognizer engine 190 within computing environment 12 is provided for receiving gesture information and identifying gestures and gesture styles from this information. In particular, gesture recognizer engine 190 includes a first order recognition engine 190a for detecting gestures, and a second order recognition engine 190b for detecting qualitative aspects of a detected gesture. The first order recognition engine 190a will now be described, followed by a description of the second order recognition engine 190b.
Through moving his/her body, a user may create gestures. A gesture comprises a motion or pose by a user that may be captured as image data and parsed for meaning. A gesture may be dynamic, comprising a motion, such as mimicking throwing a ball. A gesture may be a static pose, such as holding one's crossed forearms 304 in front of his torso 324. A gesture may also incorporate props, such as by swinging a mock sword. A gesture may comprise more than one body part, such as clapping the hands 302 together, or a subtler motion, such as pursing one's lips.
Gestures may be used for input by the first order recognition engine 190a in a general computing context. For instance, various motions of the hands 302 or other body parts may correspond to common system wide tasks such as navigate up or down in a hierarchical list, open a file, close a file, and save a file. Gestures may also be used by the first order recognition engine 190a in a video-game-specific context, depending on the game. For instance, with a driving game, various motions of the hands 302 and feet 320 may correspond to steering a vehicle in a direction, shifting gears, accelerating, and breaking.
A user may generate a gesture that corresponds to walking or running, by walking or running in place himself The user may alternately lift and drop each leg 312-320 to mimic walking without moving. The first order recognition engine 190a may parse this gesture by analyzing each hip 312 and each thigh 314. A step may be recognized when one hip-thigh angle (as measured relative to a vertical line, wherein a standing leg has a hip-thigh angle of 0°, and a forward horizontally extended leg has a hip-thigh angle of 90°) exceeds a certain threshold relative to the other thigh. A walk or run may be recognized after some number of consecutive steps by alternating legs. The time between the two most recent steps may be thought of as a period. After some number of periods where that threshold angle is not met, the system may determine that the walk or running gesture has ceased.
Given a “walk or run” gesture, an application may set values for parameters associated with this gesture. These parameters may include the above threshold angle, the number of steps required to initiate a walk or run gesture, a number of periods where no step occurs to end the gesture, and a threshold period that determines whether the gesture is a walk or a run. A fast period may correspond to a run, as the user will be moving his legs quickly, and a slower period may correspond to a walk.
A gesture may be associated with a set of default parameters at first that the application may override with its own parameters. In this scenario, an application is not forced to provide parameters, but may instead use a set of default parameters that allow the gesture to be recognized in the absence of application-defined parameters.
There are a variety of outputs that may be associated with the gesture. There may be a baseline “pass or fail” as to whether a gesture is occurring. There also may be a confidence level, which corresponds to the likelihood that the user's tracked movement corresponds to the gesture. This could be a linear scale that ranges over floating point numbers between 0 and 1, inclusive. Wherein an application receiving this gesture information cannot accept false-positives as input, it may use only those recognized gestures that have a high confidence level, such as at least 0.95. Where an application must recognize every instance of the gesture, even at the cost of false-positives, it may use gestures that have at least a much lower confidence level, such as those merely greater than 0.2. The gesture may have an output for the time between the two most recent steps, and where only a first step has been registered, this may be set to a reserved value, such as −1 (since the time between any two steps must be positive). The gesture may also have an output for the highest thigh angle reached during the most recent step.
Another exemplary gesture is a “heel lift jump.” In this, a user may create the gesture by raising his heels off the ground, but keeping his toes planted. Alternatively, the user may jump into the air where his feet 320 leave the ground entirely. The system may parse the skeleton for this gesture by analyzing the angle relation of the shoulders 310, hips 312 and knees 316 to see if they are in a position of alignment equal to standing up straight. Then these points and the upper 326 and lower 328 spine points may be monitored for any upward acceleration. A sufficient combination of acceleration may trigger a jump gesture.
Given this “heel lift jump” gesture, an application may set values for parameters associated with this gesture. The parameters may include the above acceleration threshold, which determines how fast some combination of the user's shoulders 310, hips 312 and knees 316 must move upward to trigger the gesture, as well as a maximum angle of alignment between the shoulders 310, hips 312 and knees 316 at which a jump may still be triggered.
The outputs may comprise a confidence level, as well as the user's body angle at the time of the jump.
Setting parameters for a gesture based on the particulars of the application that will receive the gesture is important in accurately identifying gestures. Properly identifying gestures and the intent of a user greatly helps in creating a positive user experience. Where a gesture recognizer system 190 is too sensitive, and even a slight forward motion of the hand 302 is interpreted as a throw, the user may become frustrated because gestures are being recognized where he has no intent to make a gesture, and thus, he lacks control over the system. Where a gesture recognizer system is not sensitive enough, the system may not recognize conscious attempts by the user to make a throwing gesture, frustrating him in a similar manner. At either end of the sensitivity spectrum, the user becomes frustrated because he cannot properly provide input to the system.
Another parameter to a gesture may be a distance moved. Where a user's gestures control the actions of an avatar in a virtual environment, that avatar may be arm's length from a ball. If the user wishes to interact with the ball and grab it, this may require the user to extend his arm 302-310 to full length while making the grab gesture. In this situation, a similar grab gesture where the user only partially extends his arm 302-310 may not achieve the result of interacting with the ball.
A gesture or a portion thereof may have as a parameter a volume of space in which it must occur. This volume of space may typically be expressed in relation to the body where a gesture comprises body movement. For instance, a football throwing gesture for a right-handed user may be recognized only in the volume of space no lower than the right shoulder 310a, and on the same side of the head 322 as the throwing arm 302a-310a. It may not be necessary to define all bounds of a volume, such as with this throwing gesture, where an outer bound away from the body is left undefined, and the volume extends out indefinitely, or to the edge of the scene that is being monitored. As explained below, even where a given gesture is defined by a volume of space (such as from the shoulder up for a throwing motion), motions, velocities and accelerations of other joints may still be monitored during the gesture for determining gesture style.
Filters may be modular or interchangeable. In an embodiment, a filter has a number of inputs, each of those inputs having a type, and a number of outputs, each of those outputs having a type. In this situation, a first filter may be replaced with a second filter that has the same number and types of inputs and outputs as the first filter without altering any other aspect of the recognizer engine architecture. For instance, there may be a first filter for driving that takes as input skeletal data and outputs a confidence that the gesture associated with the filter is occurring and an angle of steering. Where one wishes to substitute this first driving filter with a second driving filter—perhaps because the second driving filter is more efficient and requires fewer processing resources—one may do so by simply replacing the first filter with the second filter so long as the second filter has those same inputs and outputs—one input of skeletal data type, and two outputs of confidence type and angle type.
The first order gesture recognition engine 190a may not make user of metadata 428 associated with a given filter. For instance, a “user height” filter that returns the user's height may not allow for any parameters that may be tuned. An alternate “user height” filter may have tunable parameters—such as whether to account for a user's footwear, hairstyle, headwear and posture in determining the user's height.
Inputs to a filter may comprise things such as joint data about a user's joint position, like angles formed by the bones that meet at the joint, RGB color data from the scene, and the rate of change of a kinetic aspect of the user. Outputs from a filter may comprise things such as the confidence that a given gesture is being made, the speed at which a gesture motion is made, and a time at which a gesture motion is made.
A context may be a cultural context, and it may be an environmental context. A cultural context refers to the culture of a user using a system. Different cultures may use similar gestures to impart markedly different meanings. For instance, an American user who wishes to tell another user to “look” or “use his eyes” may put his index finger on his head close to the distal side of his eye. However, to an Italian user, this gesture may be interpreted as a reference to the mafia.
Similarly, there may be different contexts among different environments of a single application. Take a first-person shooter game that involves operating a motor vehicle. While the user is on foot, making a fist with the fingers towards the ground and extending the fist in front and away from the body may represent a punching gesture. While the user is in the driving context, that same motion may represent a “gear shifting” gesture. There may also be one or more menu environments, where the user can save his game, select among his character's equipment or perform similar actions that do not comprise direct game-play. In that environment, this same gesture may have a third meaning, such as to select something or to advance to another screen.
The first order gesture recognition engine 190a may have a base recognizer engine 416 that provides functionality to a gesture filter 418. In an embodiment, the functionality that the recognizer engine 416 implements includes an input-over-time archive that tracks recognized gestures and other input, a Hidden Markov Model implementation (where the modeled system is assumed to be a Markov process—one where a present state encapsulates any past state information necessary to determine a future state, so no other past state information must be maintained for this purpose—with unknown parameters, and hidden parameters are determined from the observable data), as well as other functionality required to solve particular instances of gesture recognition.
Filters 418 are loaded and implemented on top of the base recognizer engine 416 and can utilize services provided by the engine 416 to all filters 418. In an embodiment, the base recognizer engine 416 processes received data to determine whether it meets the requirements of any filter 418. Since these provided services, such as parsing the input, are provided once by the base recognizer engine 416 rather than by each filter 418, such a service need only be processed once in a period of time as opposed to once per filter 418 for that period, so the processing required to determine gestures is reduced.
An application may use the filters 418 provided by the first order gesture recognition engine 190a, or it may provide its own filter 418, which plugs in to the base recognizer engine 416. In an embodiment, all filters 418 have a common interface to enable this plug-in characteristic.
In
In
While the capture device 20 captures a series of still images, such that in any one image the user appears to be stationary, the user is moving in the course of performing this gesture (as opposed to a stationary gesture, as discussed supra). The system is able to take this series of poses in each still image, and from that determine the confidence level of the moving gesture that the user is making. Moreover, as indicated above and explained below, the first order gesture recognition engine 190a may additionally store metadata 428 associated with the gesture shown in
In performing the gesture, a user may be unable to create an angle as shown by the right shoulder 310a, right elbow 306a and right hand 302a of, for example, between 140° and 145°. So, the application using the filter 418 for the fair catch gesture 426 may tune the associated parameters 428 to best serve the specifics of the application. For instance, the positions in
As indicated above, in addition to detecting gestures, the present technology also examines qualitative aspects of a gesture, and provides feedback to the user based on detection of one or more predefined qualitative attributes. These qualitative attributes are the style with which a given gesture or user motion is performed. As shown in the block diagrams of
Referring to
The operation of the second order gesture recognition engine 190b will now be explained with reference to the block diagram of
There is a variety of metadata which may be used to determine whether a gesture or motion was performed with a predefined style. This metadata is generated from movement by the user and captured by capture device 20. In embodiments, this metadata may be a measurement of the maximum and minimum position of the user, measured in x, y, z space relative to a position of depth camera 22. This may be the x, y and z minimum and maximum image plane positions detected by the capture device 20. The metadata may also include a measurement or measurements of the change in position over time, dx/dt, for discrete time intervals over which metadata 428 is taken. The discrete time intervals may be as long as the entire time to perform the gesture or motion or as small as a single frame from the depth camera 22. This change in position metadata gives the different velocities of the user's body during the time that the user is performing the detected gesture (or other defined time period mentioned above).
In embodiments, the metadata may further include a measurement of the maximum and minimum velocity of the user, measured in x, y, z space. The metadata may also include a measurement or measurements of the change in velocity over time, dv/dt, for discrete time intervals over which metadata 428 is taken. This change in velocity metadata gives the different accelerations of the user's body during the time that the user is performing the detected gesture (or other defined time period mentioned above).
In embodiments, the metadata may further include a measurement of the maximum and minimum acceleration of the user, measured in x, y, z space. The metadata may also include a measurement or measurements of the change in acceleration over time, da/dt, for discrete time intervals over which metadata 428 is taken. This change in acceleration metadata gives different jerk measurements of the user's body during the time that the user is performing the detected gesture (or other defined time period mentioned above).
It is understood that other parameters may be used in addition to or instead of one or more of the above parameters. In on embodiment, second order differential equations may be derived which describe the trajectory of a body part as it moves in 3D space. These equations may also be used as metadata received by the engine 190b to detect a predefined style. In a further embodiment, the camera 22 may take measurements of facial expressions of the user, which may then be used with other parameters to make determinations as to the style with which a given gesture or movement was performed by the user.
In embodiments, each of the above kinetic parameters relating to position, velocity and acceleration may be taken and stored for one or more of the body parts 302 through 330 described above with respect to
This metadata 428 is forwarded by the first order gesture recognition engine 190a to the second order gesture recognition engine 190b. The second order gesture recognition engine 190b then analyzes the received metadata in step 654 to see if the metadata matches any predefined rule stored within a style library 640. Step 654 is described below with reference to
Style library 640 includes a plurality of stored rules 642 which describe when particular kinetic motions indicated by the metadata 428 are to be interpreted as a predefined style. Rules may be created by a game author, by a host of the gaming platform or by users themselves. A rule is a definition of a given set of parameter values or ranges of values. When the user moves in such a way (taking into consideration the above-described parameters) so as to satisfy a rule, the second order gesture recognition engine 190b recognizes that movement as a style. Stated another way, a rule is a predefined stored group of values or ranges of values for one or more metadata parameters (maximum/minimum position, change in position over time, maximum or minimum velocity, change in velocity over time, maximum or minimum acceleration, change in acceleration over time) for one or more body parts, which, when taken as a whole, are indicative of a particular style associated with a gesture.
Following is a description of a few rules, and general parameters making up the rules, for illustrative purposes. It is understood that there may be a wide variety of additional rules covering a wide variety of styles according to the present technology. As one example, the first order gesture recognition engine 190a may determine that a user has performed a ducking gesture, i.e., lowering closer to the ground. There are a wide variety of styles of ducking. A first user may crouch down on all fours, while a second user may “hit the deck” so as to quickly sprawl out flat against the ground.
Each of these motions may be recognized by the first order gesture recognition engine 190a as ducking. However, based on at least the change of position over time, and the change of velocity over time, of all portions of the user's body, the second order gesture recognition engine 190b can recognize a style of ducking where the user has hit the deck. This style of ducking may be recognized by a rule; that is, parameters relating to the change of distance, change of velocity, etc. defining when a user is considered to have hit the deck may be quantified and stored in a rule. When the second order gesture recognition engine 190b recognizes that the user has acted in a way that meets this rule, then the user may receive some sort of reward under the game metric and/or the user's gaming experience may be personalized to show that the system has recognized his or her own style of ducking. For example, the user's avatar may duck with the same style and the ground may shake. Other in-game style recognition indications may further be provided.
As another example of style recognition, a game may ask a user to perform dance moves by moving their feet forward, back and side to side. The first order gesture recognition engine 190a will be able to determine whether a user has properly performed the steps of the dance as indicated by the game. However, by analyzing the metadata, the second order gesture recognition engine 190b can examine the change of position data, the change of velocity data, etc. and determine whether the transition between steps is performed smoothly or in more of a jerky manner. When performed smoothly for example, the derivative of the velocity parameter will be at or near zero. When the second order gesture recognition engine 190b determines that the steps are performed smoothly, the user may be rewarded within the game and/or the user's avatar may take some action or be presented in a particular way so as to personalize the game experience for the user.
In an example within a boxing game, such as shown in
In a further example related to a baseball game, the first order gesture recognition engine 190a may recognize that a user has swung with a given velocity at the right time so as to determine that the user has hit a virtual baseball a particular distance, such as for example a home run. However, the second order gesture recognition engine 190b may analyze the metadata before, during and/or after the swing and determine that the user waited to the very last moment before initiating the swing. This may be considered stylistically significant, and there may be a rule having a set of metadata indicative of a last minute swing. For example, it may be characterized in relatively little motion until a point just before the time when a swing needs to be sensed for the underlying gesture, at which time there is a spike in position, velocity and/or acceleration. Again, a rule may be set defining these parameters, and where the second order gesture recognition engine detects metadata satisfying this rule, the user may be rewarded within the game and/or the user's avatar may take some action or be presented in a particular way so as to personalize the game experience for the user.
In another baseball example, a first user may swing a bat using only their arms and achieve a given swing velocity. However, a second user may perform a more “textbook” swing by first striding, rotating their hips, rotating their shoulders, and then swinging their arms, all in proper succession. The second order gesture recognition engine 190b may analyze the kinetic data for different points in the user's body and recognize the positions, velocities, accelerations, etc. associated with the above-described textbook swing. The user may be rewarded within the game and/or the user's avatar may take some action or be presented in a particular way so as to personalize the game experience for the user.
In a still further baseball example, upon detection of a swing by the first order gesture recognition engine 190a, the second order gesture recognition engine 190b may review the metadata and determine that the user pointed prior to the swing, similar to Babe Ruth's called home run shot in the 1932 World Series. The motions involved with pointing may be codified into a rule. Upon making the determination that the user's motions satisfy this rule, the user may be rewarded within the game and/or the user's avatar may take some action or be presented in a particular way so as to personalize the game experience for the user. For example, the appearance of the user's avatar may transform into the likeness of Babe Ruth when trotting around the bases. This example illustrates that the metadata reviewed by the second order gesture recognition engine may not solely be limited to metadata obtained during a given gesture. The metadata analyzed by the second order gesture recognition engine may also extend to a period of time before and/or after the performance of a given gesture.
With the benefit of the above disclosure, those of skill in the art will recognize a wide variety of additional styles which may be associated with a wide variety of additional gestures, which styles may be recognized on analysis of the metadata associated with a gesture, a time period before the gesture and/or a time period after the gesture.
Style library 640 may store a plurality of rules 642. In embodiments, each gesture may have a different, unique set of rules. Thus, while a given set of metadata may be stylistically significant when performed in conjunction with a first gesture, the same metadata may not be indicative of that style when performed in association with a second gesture. A single gesture may have a wide variety of styles associated therewith. In this instance, style library 640 will store a number of rules 642, one rule for each style that may be associated with a given gesture. Each predefined gesture may include such a set of rules associated therewith.
In further embodiments, a single style may be associated with more than one gesture. Furthermore, a given set of metadata may be indicative of a particular style independent of any associated gesture. In such embodiments, the second order gesture recognition engine 190b may recognize a particular style associated with a user's movement, even though that movement may not be indicative of a specific recognized gesture.
Moreover, it is contemplated that the second order gesture recognition engine 190b may detect one or more styles even where the first order gesture recognition engine determines that the user has failed in performing an attempted gesture. For example, a rule may exist for one or more gestures which indicates that a lot of movement is to be interpreted that the user is putting in a lot of effort to the one or more gestures. Thus, even if the movements do not pass as to establish a particular gesture, the second order gesture recognition engine 190b may recognize the effort exerted by the user and personalize the user's in-game experience by indicating recognition of the user's effort.
Some of the styles which may be covered by rules include but are not limited to:
Referring again to the flowchart of
In the following description, the different parameters may be indicated by the integer, i (i=1 for the first parameter, i=2 for the second parameter, etc.). The different body parts may be indicated by the integer, j (j=1 for the first body part, j=2 for the second body part, etc.). Thus, Ri,j is the stored value or range of values in a rule associated with the ith parameter for the jth body part. Mi,j is the measured or derived value or range of values from a user's gesture or motion associated with the ith parameter for the jth body part.
In step 700, when determining whether received metadata satisfies a given rule, the engine 190b initially retrieves the stored rule value Ri,j for the first body part (j=1) for the first parameter (i=1). In step 702, the engine 190b compares the received measured metadata value Mi,j against the stored rule metadata value Ri,j. In step 706, the engine 190b determines whether the current measured metadata value Mi,j is equal to or within a predefined range of the rule metadata value for Ri,j.
It is understood that while a rule may consist of a group of parameters with given values, one or more of these parameters may be weighted more heavily in determining whether a user's actions satisfy a given style rule. That is, certain parameters for certain body parts may be more indicative of a particular style than others. In embodiments, these parameters for the indicated body parts may be accorded a higher weight in the overall determination of whether the user's movements performed a given style. As such, in step 710, the engine 190b determines whether the rule value Ri,j is weighted higher or lower relative to other rule values Ri,j. This weight information may be stored with a rule in library 640.
It will seldom, if ever, happen that a given set of measured parameters will match all values in a stored rule. As explained above with respect to gestures, the second order gesture recognition engine 190b may output both a style and a confidence level which corresponds to the likelihood that the user's movement corresponds to that style. This confidence value may be calculated in the same way the confidence value for a given gesture was calculated as described above. In step 712, using the determination of steps 706 and 710, the engine 190b determines a cumulative confidence level as to whether the user's movements amount to the style covered by the rule under consideration. A cumulative confidence level will include the confidence level of all prior trips through the loop plus consideration of the current Ri,j.
In step 716, the engine 190b looks at whether there are more body parts, j, for a given parameter, i, that have not been considered for a stored rule. If so, the next body part is considered (j=j+1) in step 718 and the engine 190b returns to step 702 to compare the next measured metadata value Mi,j against the stored rule value Ri,j for the next body part j.
Alternatively, if in step 716 it is determined that the last body part within the rule for a given parameter has been considered, the engine 190b next determines in step 720 whether there are more parameters to consider in the stored rule. If so, the parameter value i is incremented by one step 724 and the engine 190b returns to step 702 to once again compare the received measured metadata value Mi,j against the stored metadata rule value Ri,j for the updated parameter value i. Those of skill in the art will appreciate other methods of comparing the measured values Mi,j against the stored rule value Ri,j. If it is determined in step 720 that there are no more parameters in the stored rule to consider, engine 190b returns the cumulative confidence level in step 728.
Referring again to
Those of skill in the art will understand other methods of analyzing the measured parameters to determine whether the parameters conform to a predefined style, for a given gesture or motion. One such additional method is disclosed in U.S. Patent Application Publication No. 2009/0074248, entitled “GESTURE-CONTROLLED INTERFACES FOR SELF-SERVICE MACHINES AND OTHER APPLICATIONS,” which publication is incorporated by reference herein in its entirety.
The detection of a given style may be used within the game or multimedia platform in a variety of ways in order to reward and/or personalize the experience for the user. For example, when the user's avatar is shown to perform the detected gesture, the gesture may further be performed by the avatar with the detected style. Additionally and/or alternatively, the appearance of the avatar may change to reflect the detected style. For example, if the user's gesture is to remain still without moving, if the user is able to perform this action not just to a threshold level, but to a level exhibiting high levels of body control, the user's avatar may become transparent and partially disappear. Given the above disclosure, those of skill in the art would appreciate a wide variety of other in-game audio and/or video effects which may be provided to illustrate the detected style associated with a performed gesture. Moreover, in addition to rendering the avatar in a different manner, the avatar's surrounding within the game may alternatively or additionally be rendered in a different manner to further illustrate the user's style and to further personalize the gaming experience for the user.
Detecting the style of a given user in accordance with present technology is conceptually different than detecting whether a user has performed a given gesture. Performance of a given gesture is typically pass/fail and, if successfully performed, will result in additional points or the user advancing under the game metric. By contrast, the detection of styles is not about whether a user has performed a given gesture but rather how the user has performed the gesture. The present technology may detect a style whether or not the user has successfully performed an underlying gesture and detection of a style does not result in points or advancement of the player under the game metric (although performing a gesture with a detected style may result in points or advancement in further embodiments). Moreover, as indicated above, a given style may be associated with a particular gesture, or it may be detected independent of any particular gesture or across a wide variety of gestures. In general, recognition of individual player styles will personalize and enhance the user experience when playing a game or using a multimedia application.
The foregoing detailed description of the inventive system has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the inventive system to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. The described embodiments were chosen in order to best explain the principles of the inventive system and its practical application to thereby enable others skilled in the art to best utilize the inventive system in various embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the inventive system be defined by the claims appended hereto.