Detection of hand gestures using gesture language discrete values

Computer implemented method for detecting a hand gesture of a user, comprising:

RELATED APPLICATIONS

This application is related to co-filed, co-pending and co-assigned U.S. Patent Applications entitled “HAND GESTURE API USING FINITE STATE MACHINE AND GESTURE LANGUAGE DISCRETE VALUES” (U.S. application Ser. No. 14/985,691), “MULTIMODAL INTERACTION USING A STATE MACHINE AND HAND GESTURES DISCRETE VALUES” (U.S. application Ser. No. 14/985,716), “RECOGNITION OF HAND POSES BY CLASSIFICATION USING DISCRETE VALUES” (U.S. application Ser. No. 14/985,741), “TRANSFORM LIGHTWEIGHT SKELETON AND USING INVERSE KINEMATICS TO PRODUCE ARTICULATE SKELETON” (U.S. application Ser. No. 14/985,777), “STRUCTURE AND TRAINING FOR IMAGE CLASSIFICATION” (U.S. application Ser. No. 14/985,803), “TRANSLATION OF GESTURE TO GESTURE CODE DESCRIPTION USING DEPTH CAMERA” (U.S. application Ser. No. 14/985,804), “GESTURES VISUAL BUILDER TOOL” (U.S. application Ser. No. 14/985,775, and “ELECTRICAL DEVICE FOR HAND GESTURES DETECTION” (U.S. application Ser. No. 14/985,728), the disclosures of which are incorporated herein by reference.

BACKGROUND

With the evolution of computerized environments, the use of human-machine interfaces (HMI) has dramatically increased. A growing need is identified for more natural human-machine user interface (NUI) methods such as, for example, voice and/or gaze and more specifically for hand gestures interaction to replace and/or complement traditional HMI such as, for example, keyboards, pointing devices and/or touch interfaces. Doing so may serve to, for example, eliminate and/or reduce the need for intermediator devices (such as keyboard and/or pointing devices), support hands free interaction, improving accessibility to population(s) with disabilities and/or provide a multimodal interaction environment. Current solutions for identifying and/or recognizing hand(s) gestures may exist, however they are mostly immature, present insufficient accuracy and/or high complexity while requiring high computation resources for extensive computer vision processing and/or machine learning. Such technologies may rely on full hand skeleton articulation and/or complex machine learning algorithms for detection and/or classification of hand gestures which may make such implementations costly and unattractive for integration preventing them from being adopted for wide scale usage.

SUMMARY

According to some embodiments of the present disclosure, there are provided systems and methods for detecting multimodal user interaction acts which may include on or more actions, for example, a hand gesture, a head gesture, a body gesture, a facial expression, a gaze direction, a text, a voice, and/or a tactile input. Detection of the multimodal acts may be performed for a user using one or more computerized devices attached to one or more capturing devices. The captured multimodal user interaction may be processed to identify one or more of the multimodal acts and optionally initiate one or more application functions, actions, operations and/or commands which may be associated with the detected multimodal act(s).

Detecting the one or more hand gestures of the user as depicted in the image(s) by estimating the discrete hand values scores of the runtime hand dataset compared to the discrete hand values of the pre-defined hand gesture features records may dramatically reduce the required computation load in runtime. The detection process is reduced to an optimization process over the plurality of discrete hand values of the runtime hand dataset and those of the pre-defined hand gestures where the optimization is performed over binary representations of the discrete hand values. Furthermore due to the discrete nature of the detection process in which the discrete hand values are processed, a finite number of possible values, for example 5, 10 and/or 20 may be valid for each hand feature avoiding the need to fully articulate the hand skeleton thus further reducing computer vision and modeling resources.

DETAILED DESCRIPTION

Basing the detection of user hand gestures on the discrete architecture may dramatically reduce the runtime computation load since the need for full hand skeleton modeling is avoided by employing an optimization process over the plurality of discrete hand values (pre-defined) and the discrete hand values scores (runtime) which present a finite number of possible values. Furthermore, the optimization is based on estimation terms which are generated using binary representation of the hand poses and/or hand motions which further reduces the complexity of computation since binary computation requires considerably less processing resources compared to continuous representations a skeletal hand. Specializing the one or more SSVM functions may reduce even more the computation load at runtime since a limited number of pre-defined hand gestures are considered during the optimization process makes the optimization process may become simpler and faster.

According to some embodiments of the present disclosure, there are provided systems and methods for detecting multimodal user interaction acts comprising one or more actions, for example, a hand gesture, a head gesture, a body gesture, a facial expression, a gaze direction, a text, a voice, and/or a tactile input. Detection of the multimodal acts may be performed for a user using one or more computerized devices, for example, computer, mobile device, computerized machine and/or computerized appliance equipped and/or attached to one or more user interaction capturing devices, for example, an imaging device, a sound recorder, a tactile reception device, a text input device such as, for example, such as a keyboard, a digital pen, a touchpad and the likes, a touchscreen, a digital surface and/or a pointing device(s) such as for example, a mouse, a trackball, a game controller, a joystick and the likes. The captured multimodal user interaction may be processed to identify the one or more multimodal acts and optionally initiate one or more application functions, actions, operations and/or commands which may be associated with the detected multimodal act(s). The multimodal acts may be classified and/or identified during runtime by employing one or more detection techniques to identify the one or more user actions constituting the multimodal act, for example, text input parsing, pointing device input analysis, speech analysis, tactile input analysis and specifically hand gesture detection.

Before explaining at least one embodiment of the exemplary embodiments in detail, it is to be understood that the disclosure is not necessarily limited in its application to the details of construction and the arrangement of the components and/or methods set forth in the following description and/or illustrated in the drawings and/or the Examples. The disclosure is capable of other embodiments or of being practiced or carried out in various ways.

Referring now to the drawings,FIG. 1is a flowchart of an exemplary process for detecting a hand gesture depicted by an image(s), according to some embodiments of the present disclosure. An exemplary hand gesture detection process100presents a detection process of movements a hand of a user depicted in one or more images as a pre-defined hand gesture. As shown at110, the exemplary process100starts with receiving a plurality of pre-defined hand gestures each represented as a sequential logic model. Each of the sequential logic models may map one or more hand poses and/or hand motions each represented by a unique one of a plurality of hand features records. The hand features record may be a hand pose features record or a hand motion features record each associated with the hand pose and the hand motion respectively. Each of the hand features records is defined by one or more of a plurality of discrete hand values each indicating a state of a respective hand feature, for example, a pose of a palm, a pose of a finger(s), a motion of the palm and/or a motion of the finger(s). Similarly, the discrete hand values may be discrete pose values and/or discrete motion values each indicative of a state of a corresponding hand feature which may be a hand pose feature and/or a hand motion feature.

As shown at120, a runtime sequence is received which includes one or more of a plurality of runtime hand datasets. Each of the runtime hand datasets is defined by a plurality of discrete hand values scores each indicating a current state of a respective hand feature of a moving hand of the user. The plurality of the discrete hand values scores are inferred and/or calculated by analyzing one or more of a plurality of images depicting the moving hand of the user.

Before further describing the hand gesture detection process100it is important to understand the discrete architecture used for defining hand gestures, hand poses and/or hand motions. The discrete hand features defining the hand features records as well as the discrete hand features scores defining the runtime hand datasets all refer to the same discrete hand values as presented herein. The discrete hand values may be discrete pose values or discrete motion values. Continuous values of the one or more hand features may be represented by discrete hand values by quantizing the continuous values to support the discrete architecture of the hand gesture detection process.

Reference is now made toFIG. 2which is a block diagram of the building blocks of an exemplary hand gesture, according to some embodiments of the present disclosure. Illustration200depicts several construction schemes of exemplary hand gestures210. The hand gestures210may be created through one or more possible constructions, for example:(a) The hand gesture210may consist of a hand pose250.(b) The hand gesture210may be a combination and/or sequence of two hand poses250.(c) The hand gesture210may be a combination and/or sequence of the hand pose250and a hand motion260.(d) The (first) hand gesture210may be a combination and/or sequence of a second hand gesture210and the hand pose250. The second hand gesture may be the same gesture as the first one or it may be a different hand gesture210.(e) The (first) hand gesture210may be a combination and/or sequence of a second hand gesture210and the hand motion260. The second hand gesture210may be the same gesture as the first one or it may be a different hand gesture210.

The hand gesture210may be created through multiple iterations of the constructions (d) and/or (e) above. Each hand gesture210is constructed as a unique combination and/or sequence represented by a hand gesture sequence201which comprises of the one or more of hand poses250, hand motions260and/or hand gestures210. Each of the hand gestures210starts and ends with an idle state280which may be a virtual state identifying the start and/or the end of the unique hand gesture sequence201of the hand gesture210. The hand gesture sequence201may be considered a sequential logic model describing the hand gesture210.

Reference is now made toFIG. 3which is a schematic illustration of an exemplary FSM representing a hand gestures sequential logic model as a sequence of hand motions and hand poses, according to some embodiments of the present disclosure. An illustration300depicts an FSM301which may represent a sequential logic model of a hand gesture sequence such as the hand gesture sequence201. The FSM301starts with a start point310indicating a starting state for the FSM301. The FSM301may include one or more states302A,302B,302C through302N. The first state302A is a hand pose such as the hand pose250which may describe a start of a sequence representing a hand gesture such as the hand gesture210. Each of the succeeding states302B,302C through302N may be either a hand pose250or a hand motion such as the hand motion260. The FSM301is ended with an FSM end point320. The start point310and/or the end point320may be defined as a virtual idle state such as the idle state280. During detection process of the hand gesture210such as the hand gesture detection process100, the FSM301may be augmented with one or more score functions over one or more sequences within the FSM301in order to allow processing one or more runtime hand datasets included in a runtime sequence depicting a moving hand of a user compared to each of the hand poses250and/or hand motions260comprising each of the sequences in the FSM301. Each of the hand poses250and/or hand motions260is represented by a respective hand pose features record and a hand motion features record. Using the FSM301to represent each of the plurality of hand gesture sequences201each correlating to one of the hand gestures210, allows to significantly simplify the estimation of the runtime sequence of runtime hand datasets to detect it as one of pre-defined hand gestures such as the hand gestures210in runtime using dynamic programming by applying, for example, viterbi decoding over the score function(s) which augmented the FSM301.

Reference is now made toFIG. 4which is a schematic illustration of exemplary hand poses discrete representation, according to some embodiments of the present disclosure. Illustration400depicts exemplary hand poses250representation as a hand pose features record251which includes one or more hand pose features410,420,430and440. The hand pose features records251which was referenced within the generalized term of hand features record may be represented with one or more of the discrete pose value411,421,431and/or441each indicating a state (value) of the respective hand pose feature410,420,430and/or440for an associated hand pose of the hand poses250. The combination of the one or more discrete pose values411,421,431and/or441of the respective hand pose features410,420,430and440as defined by the hand pose features record251identifies a specific pose of the hand poses250which may be detected by analyzing one or more images depicting movement of a user's hands. Continuous values of the one or more hand pose features410,420,430and/or440may be represented by the discrete pose values411,421,431and/or441by quantizing the continuous values. The hand pose features record251may be represented as, for example, a features vector, a features matrix and/or a features table. The hand pose features record251may include values of one or more of the following exemplary hand pose features:Palm pose features—one or more palm pose features410include, for example, hand selection, palm direction, palm rotation and/or hand location. Hand selection may identify which hand is active and may include discrete pose values411such as, for example, right, left, both and/or any. Palm direction may define the direction in which the palm of the active hand is facing and may include discrete pose values411such as, for example, left, right, up, down, forward and/or backward. Palm rotation may define the rotation state of the palm of the active hand and may include discrete pose values411such as, for example, left, right, up, down, forward and/or backward. Hand location may identify the spatial location of the active hand in space and may include discrete pose values111such as, center of field of view (FOV), right side of FOV, left side of FOV, top of FOV, bottom of FOV, front of FOV and/or rear of FOV. Where FOV is for example, the visible space of an imaging device monitoring the movement of the user's hand. Optionally, hand location is identified with respect to a fixed object present in the FOV, for example, keyboard and/or pointing device so that hand location may be defined by discrete pose values411such as, for example, above_keybord, behind_keyboard, right_of_keyboard and/or left_of_keyboard.Finger flexion features—one or more finger flexion features420which are defined per finger. For example, a finger feature420may be a flexion and/or curve state which may include discrete pose values421such as, for example stretched, folded and/or open represented, for example by 0, 1, and 2. Each finger (thumb, index, middle, ring and/or pinky) is assigned one or more specific finger features, for example, {thumb, middle, ring, pinky} in {folded} state and {index} in {stretched} state.Finger tangency condition features—one or more fingers tangency features430which are defined per finger. The tangency feature may define a touch condition of any two or more fingers and/or touch type and may include discrete pose values431such as, for example, not touching, fingertip and/or full touch.Finger relative location condition features—one or more fingers relative location features440are defined per finger. Each of the finger relative location condition features440may define a relative location of one finger in relation to another. The fingers relative location features440may include discrete pose values441such as, for example, one or more fingers are located relatively to another one or more fingers to the left, right, above, below, inward, outward, in front and/or behind.

Each one of the hand poses250is defined by a unique one of the hand pose features records251which may be a combination and/or sequence of one or more discrete pose values411,421,431and/or441each indicating a value of the corresponding hand pose feature410,420,430and/or440. The hand pose features records251may include only some (and not all) of the discrete pose values411,421,431and/or441while other discrete pose values411,421,431and/or441which are not included are left free. For example, the hand pose features records251may define a specific state of the fingers (for example discrete pose values421,431and/or441) while the direction of the palm is left unspecified (for example discrete pose value411). In this case the hand pose250is detected in runtime by identification of the fingers state as defined by the hand pose features records251with the hand facing any direction. Detection of the one or more hand poses250is simplified since the discrete pose values411,421,431and/or441may be easily identified because there is a finite, limited number of possible states for each of the hand pose features410,420,430and/or440avoiding the need for hand skeleton modeling thus reducing the level of computer vision processing. The discrete representation of the hand pose features410,420,430and/or440may not be limited to discrete values only. Continuous values of the one or more hand features410,420,430and/or440may be represented by discrete pose values411,421,431and/or441respectively by quantizing the continuous values. For example, the palm rotation palm pose feature may be defined with 8 discrete motion values411—0°, 45°, 90°, 135°, 180°, 225°, 270° and 315° to quantize the complete rotation range of 0°-360°.

Reference is now made toFIG. 5which is a schematic illustration of an exemplary pinch hand pose discrete representation, according to some embodiments of the present disclosure. Illustration500depicts an exemplary pinch hand pose250A representation by a pinch pose features record251A comprising discrete pose values such as the discrete pose values411,421,431and/or441, each indicating a value of a corresponding pose feature such as the pose features410,420,430and/or440. The pinch hand pose250A which is visualized through an image capture501is defined by some of the plurality of discrete pose values411,421,431and441as follows:A hand selection feature410A is assigned a discrete pose value411A to indicate the left hand is active.A palm direction feature410B is assigned a discrete pose value411B to indicate the palm of the active hand is facing forward.A fingers flexion feature420A is assigned a discrete pose value421A and a discrete flexion value421B to indicate the thumb and index fingers are folded.A fingers flexion feature420B is assigned a discrete pose value421C and a discrete pose value421D to indicate the middle, ring and pinky fingers are open.A fingers tangency condition feature430A is assigned a discrete pose value431A to indicate the thumb and index fingers are touching at their tips.A fingers relative location feature440A is assigned a discrete pose value441A, a discrete pose value441B and a discrete pose value441C to indicate the index finger is located above the thumb finger.

As seen above, the pinch hand pose250A is uniquely defined by a pinch features pose features record251A comprising the discrete pose values411A,411B,421A,421B,421C,421D,431A,431B,441A,441B and441C corresponding to the pose features410A,410B,420A,420B,430A and440A respectively. Similarly additional hand poses250may be defined.

Reference is now made toFIG. 6which is a schematic illustration of exemplary hand motions discrete representation, according to some embodiments of the present disclosure. Illustration600depicts each of a plurality of exemplary hand motions260representation as a hand motion features record261. The hand motion features record261which was referenced within the generalized term of hand features record may include one or more hand motion features610and620. Each of the hand motion features610and620may be assigned with one or more discrete motion values611and/or621which indicate a state (value) of the respective hand motion feature610and/or620for an associated hand motion of the hand motions260. Continuous values of the one or more hand motion features610and/or620may be represented by the discrete motion values611and/or621by quantizing the continuous values. The hand motion features record261identifies a specific motion of a hand and/or finger(s) which may be detected by analyzing one or more images depicting movement of a user's hands. The hand motion features record261may be represented as, for example, a features vector, a features matrix and/or a features table. The hand motion features record261may include one or more of the following exemplary hand motion features:Motion property features—one or more motion property features610may include, for example, motion size, motion speed and/or motion location. Motion size may identify the size (scope) of the motion, and may include discrete motion values611such as, for example, small, normal and/or large. Motion speed may define the speed of the motion and may include discrete motion values611such as, for example, slow, normal, fast and/or abrupt. Motion location may identify the spatial location in which the motion is performed, and may include discrete motion values611such as, for example, center of FOV, right side of FOV, left side of FOV, top of FOV, bottom of FOV, front of FOV and/or rear of FOV. Optionally, the hand location is identified with respect to a fixed object present in the FOV, for example, keyboard and/or pointing device so that hand location may include discrete motion values611such as, for example, above_keybord, behind_keyboard, right_of_keyboard and/or left_of_keyboard.Motion script features—one or more motion script features620may define the actual motion performed. The motion script values620may include, for example, motion direction, motion start point, motion end point and/or pre-defined curve shapes. The motion direction feature620may include discrete motion values621such as, for example, upward, downward, left_to_right, right_to_left, diagonal_left_upward, diagonal_right_upward, diagonal_left_downward, diagonal_right_downward, clockwise_arc_right_upward, clockwise_arc_right_downward, clockwise_arc_left_upward, clockwise_arc_left_downward, counter_clockwise_arc_right_upward, counter_clockwise_arc_right_downward, counter_clockwise_arc_left_upward and/or counter_clockwise_arc_left_downward. Optionally, the motion curve shapes may include for example, at-sign (@), infinity sign (∞), digit signs, alphabet signs and the likes. Optionally, additional one or more curve shapes, for example, checkmark, bill request and the likes may be defined by the hand motion features record261. Each of the motion script features620is defined for a two dimensional (2D) plane, however each of the motion script features620may be transposed to depict another 2D plane, for example, X-Y, X-Z and/or Y-Z. Optionally, the motion script features620define three dimensional (3D) motions and/or curves using a 3D image data representation format.

Each one of the hand motions260is defined by a unique one of the hand motion features records261which may a combination and/or sequence of one or more discrete motion values611and/or621each indicating a value of the corresponding hand motion features610and/or620. Using the discrete motion values621and/or621allows for simple detection of the hand motions260as there are a finite number of discrete motion values611and/or621to be analyzed and estimated avoiding the need for full hand skeleton modeling thus reducing the level of computer vision processing. For instance the motion speed feature included in the hand motion property feature610may include up to four discrete motion values611—slow, normal, fast and abrupt. Similarly additional hand motions260may be defined. The discrete representation of the hand motion features610and/or620may not be limited to discrete values only, continuous values of the one or more hand motion features610and/or620may be represented by discrete motion values611and/or621respectively by quantizing the continuous values. For example, the motion speed property feature may be defined with 6 discrete motion values611—5 m/s (meter/second), 10 m/s, 15 m/s, 20 m/s, 25 m/s and 30 m/s to quantize the motion speed of a normal human hand of 0 m/s—30 m/s.

Reference is now made toFIG. 7which is a schematic illustration of an exemplary half circle hand motion discrete representation, according to some embodiments of the present disclosure. Illustration700depicts an exemplary left_to_right_upper_half_circle hand motion260A construction by a left_to_right_upper_half_circle motion features record261A comprising discrete motion values such as the discrete motion values611and/or621, each indicating a value of a corresponding hand motion feature such as the hand motion features610and/or620. The left_to_right_upper_half_circle hand motion260A which is visualized through image captures701A,701B and701C is created with some of the plurality of discrete motion values611and621as follows:A motion size feature610A is assigned a discrete motion value611A to indicate the motion size is normal.A motion speed feature610B is assigned a discrete motion value611B to indicate the motion speed is normal.A motion location feature610C is assigned a discrete motion value611C to indicate the motion is performed above a keyboard.A first motion script feature620A is assigned a discrete motion value621A to indicate a motion shape of clockwise_arc_left_upward as presented by the image capture701B.A second motion script feature620B is assigned a discrete motion value621B to indicate a motion shape of clockwise_arc_left_downward as presented by the image capture701C.

As seen above, the left_to_right_upper_half_circle motion260A is uniquely defined by a left_to_right_upper_half_circle motion features record261A comprising of the discrete motion values611A,611B,611C,621A and621B corresponding to the hand motion features610A,610B,610C,620A and620B respectively. Similarly additional hand and/or finger(s) motion may be defined.

Reference is now made toFIG. 8which is a block diagram of the building blocks of an exemplary slingshot hand gesture, according to some embodiments of the present disclosure. Illustration800depicts an exemplary slingshot hand gesture210A discrete representation as multiple hand poses such as the hand pose250and hand motions such as the hand motion260. The slingshot hand gesture210A which is visualized through image captures801A,801B,801C and801D is constructed of a combination and/or sequence of an idle state such as the virtual idle state280, a no pinch hand pose250B, a pinch hand pose such as the pinch hand pose250A, a retract hand motion260B and an end idle state such as the virtual idle state280. The sequence of the slingshot hand gesture210A is as follows:A virtual idle state280defines the starting state and/or point of the sequence of the slingshot hand gesture210A.A no pinch hand pose250B defined by a hand pose features record251B represents no pinching action as depicted in image capture801A.A pinch hand pose250A defined by the hand pose features record251A in which a pinch action is identified as depicted in image capture801B.A retract hand motion260B defined by a hand motion features record261B in which the hand is moved backwards as is depicted in image capture801C.A no pinch hand pose250B defined by the hand pose features record251B represents the pinch pose is released and identified as no pinching action as depicted in image capture801D.An idle state280defines the end state and/or point of the sequence of the slingshot hand gesture210A.

The sequence of the slingshot hand gesture210A as described above is represented through a unique slingshot hand gesture sequence251A which may be considered a sequential logic model associated with the slingshot hand gesture210A. For each of the hand poses250A,250B and the hand motion260B only relevant discrete pose and/or motion values may be defined. For example, the no pinch hand pose features record251B is defined by the hand selection discrete pose value411(left), the finger flexion discrete pose value421(stretched) and the finger tangency discrete pose value431(not touching) are defined for the no pinch pose250B. Other discrete pose values which are irrelevant to distinguishing between the no pinch hand pose250B from the pinch hand pose250A are left free and are not specified. Specifying only the relevant discrete pose and/or motion values allows for several degrees of freedom in the articulation of the hand poses250and/or hand motions260as performed by different one or more users at runtime. This means that each of the one or more users may perform the hand pose250and/or hand motion260slightly differently at runtime and yet they are still detected the same.

Reference is made once again toFIG. 1. As shown at130, the one or more runtime hand datasets included in the received runtime sequence depicting the moving hand of the user are submitted to one or more SSVM functions together with the plurality of the pre-defined hand features records (hand pose features records and/or hand motion features records). The one or more SSVM functions generate a plurality of estimation terms which will later be used for estimating the runtime sequence as one of the plurality of hand gestures such as the hand gestures210.

Conventions and Notations:

xtis defined a set of discrete hand values scores for a specific runtime hand dataset at time t where each of the d scores corresponds to a particular hand feature, for example, index finger pointing forward, middle finger is touching the thumb, ring finger is folded and the likes. For brevity, the notation x1:tis used to describe (x1, . . . , xt).2) yt∈Yt; y1:t=(y1, . . . , yt)ytis defined a set of pre-defined hand features records (hand pose features records and/or hand motion features records) associated with a specific hand gesture210which is part of the pre-defined hand gestures set Ytthat is valid at time t.For brevity, the notation y1:tis used to describe (y1, . . . , yt).

The estimation terms include singleton terms and pairwise terms. The singleton terms relate to estimation terms in which each of the runtime hand datasets is simulated by the plurality of discrete hand values of the valid pre-defined hand features records (each defining one of the hand poses and/or hand motions included in the valid hand gestures210). Calculation of the singleton terms is expressed in equation 1 below.
S(x1:T,yr)=ws,Fs(x1:t,yr);ws∈RDsEquation 1:Where S(x1:T, yt) is a set of singleton terms, Fs(x1:t, yt) is a set of singleton features and wsis a set of weights assigned to each of the singleton features. The set of singleton terms as well as the set of weights wsmay be created through a training session using the one or more SSVM functions to select singleton terms and weights that provide the best accuracy for the associated set of pre-defined hand features records.

The pairwise estimation terms relate to estimation terms in which each of the runtime hand datasets is simulated by the plurality of discrete hand values of a current pre-defined hand features record and a predecessor pre-defined hand features record of the valid pre-defined hand features records (each defining one of the hand poses and/or hand motions included in the valid hand gestures210). Calculation of the pairwise terms is expressed in equation 2 below.
P(x1:T,yt-1,yt)=wp,Fp(x1:t,yt-1,yt);wp∈RDpEquation 2:Where P(x1:T, yt-1, yt) is a set of pairwise terms, Fp(x1:t, yt-1, yt) is a set of pairwise features and wpis a set of weights assigned to each of the pairwise features. The set of pairwise terms as well as the set of weights wpmay be created through a training session using the one or more SSVM functions to select pairwise terms and weights that provide the best accuracy for the associated set of pre-defined hand features records.

The sets of singleton features and the sets of the pairwise features are created by simulation of the discrete hand values defining the hand features records of the valid hand gestures210over the discrete hand values scores of the runtime hand datasets. The discrete hand values may be expressed in a Boolean form, for example, “(index finger is pointing up OR index finger is pointing right) AND (ring finger is touching the thumb)”. Following this process the entire hand features record is represented by Boolean expressions. The Boolean expression may allow for efficient and/or simple simulation. The Boolean representation may take many forms, however the Boolean representation that presents best results may be, for example, CNF. CNF is a Boolean representation in which every Boolean expression may be expressed as AND operators over two or more OR operators, for example, “(palm facing forward OR palm facing upward OR palm facing LEFT) AND (index finger touching thumb OR middle finger touching thumb)”. Simulating the discrete hand values defining the hand features records over the discrete hand values scores of the runtime hand datasets is performed using one or more of a plurality of parametric functions in which one or more parameters are used to achieve best simulation results. The generation of the singleton features is given in equations 3 below.

Fs⁡(x1:t,yt)=[∝(xt,yt;ρ1)⋮∝(xt,yt;ρDs)]Equation⁢⁢3Where ∝(xt, yt; ρ) is a set of parametric functions from which the one that provides the singleton feature presenting best accuracy is selected. The set parametric functions is executed at runtime since the parameter ρ which provides best accuracy results for the hand gesture detection process100may not be known in advance. The execution of the parametric functions for each of the singleton features may be performed using the one or more SSVM functions.

Similarly generation of the pairwise features is given in equations 4 below.

As shown at140, the hand gesture detection process100proceeds to perform an optimization of one or more score functions which use the generated estimation terms (singleton terms and/or pairwise terms) to select a pre-defined hand gesture210that best fits the runtime sequence comprising the one or more runtime hand datasets. The score function is optimized by applying it to one or more sequences within an FSM such as the FSM301, where each of the one or more sequences corresponds to one of the hand gestures210. The score function is expressed in equation 5 below.

Optionally, the set of valid hand gestures210at the time t includes one or more hand gestures210which are not pre-defined but rather consisting one or more hand poses such as the hand poses250and/or hand motions such as the hand motions260which may be represented using the discrete hand values.

Optionally, the one or more SSVM functions are specialized by selecting the set of valid pre-defined hand gestures210at the time t to include only one or more registered hand gestures210. The one or more registered hand gestures210may be considered valid with respect to a context of the runtime environment of the user. The context may describe one or more runtime execution parameters and/or conditions at the time t such as, for example, active application, user interaction state and/or limitation of hand gestures210available to the user at the time t. Specializing the one or more SSVM functions may further accelerate the optimization process to allow for a more rapid hand gesture detection process100.

Reference is now made toFIG. 9which is a schematic illustration of an exemplary system for detecting a hand gesture depicted by an image(s), according to some embodiments of the present disclosure. An exemplary system900includes an imaging unit910for capturing one or more images of a moving hand of a user950, one or more hardware processors920for detecting hand gestures such as the hand gestures210performed by the user950as depicted by the one or more captured images and a storage medium430for storing one or more pre-defined hand gestures sequential logic models201(each representing a corresponding hand gesture such as the hand gesture210) and/or one or more trained SSVM functions. The imaging unit910may include one or more imaging devices, for example, a camera sensor, a stereo camera, a depth camera and/or an IR sensor. The imaging unit910which monitors the moving hand(s) of the user950may transmit to the processor920one or more captured images which are timed and synchronized. The one or more timed captured images are analyzed to create a runtime sequence of one or more runtime hand datasets each defined by one or more of a plurality of discrete hand values scores inferred from the one or more timed captured images. The processor920may employ a hand gesture detection process, such as the hand gesture detection process100to estimate an optimal one of the plurality of pre-defined hand gestures210(each represented by one of the sequential logic models201) that best matches the runtime sequence of gestures performed by the user950. Optionally, the system900includes an SSVM hardware unit925which may execute the SSVM function on one or more hardware circuits optimized for SSVM execution. The SSVM hardware unit925may efficiently and rapidly execute the one or more SSVM functions of the hand gesture detection process100thus reducing the computation load of the processor920. Optionally, the SSVM hardware unit925is integrated within one or more of the processors920.

According to some embodiments of the present disclosure, there are provided systems and methods for detecting multimodal user interaction acts. Each of the multimodal acts may include one or more user action, for example, a hand gesture such as the hand gesture210, a head gesture, a body gesture, a facial expression, a gaze direction, a text, a voice, and/or a tactile input. Detection of the multimodal acts may be performed for a user using one or more computerized devices, for example, computer, mobile device, computerized machine and/or computerized appliance equipped and/or attached to one or more user interaction capturing devices, for example, an imaging device, a sound recorder, a tactile reception device, a text input device such as, for example, such as a keyboard, a digital pen, a touchpad and the likes, a touchscreen, a digital surface and/or a pointing device(s) such as for example, a mouse, a trackball, a game controller, a joystick and the likes.

The captured user interaction may be processed to identify the one or more multimodal acts. Once the one or more multimodal acts are identified, one or more associated functions, actions, operations and/or commands may be initiated. The multimodal acts may be classified and/or identified during runtime by employing one or more detection techniques to identify the one or more user actions constituting the multimodal act, for example, text input parsing, pointing device input analysis, speech analysis, tactile input analysis and specifically hand gesture detection using a hand gesture detection process such as the hand gesture detection process100.

Reference is now made toFIG. 10which is a block diagram of the building blocks of exemplary multimodal acts, according to some embodiments of the present disclosure. Illustration1000depicts several construction schemes of exemplary multimodal acts1010. The multimodal acts1010may be constructed as one or more possible constructions, for example:(a) The multimodal act1010may be a combination and/or sequence of a hand gesture210a voice input1020.(b) The multimodal act1010may be a combination and/or sequence of the voice input1020and the hand gesture210.(c) The multimodal act1010may be a combination and/or sequence of the hand gesture210and a text input1030.(d) The multimodal act1010may be a combination and/or sequence of the text input1030and the hand gesture210.(e) The multimodal act1010may be a combination and/or sequence of the hand gesture210and a visual element selection1040.(f) The multimodal act1010may be a combination and/or sequence of the visual element selection1040and the hand gesture210.(g) The multimodal act1010may be a combination and/or sequence of the hand gesture210and a tactile input1050.(h) The multimodal act1010may be a combination and/or sequence of the tactile input1050and the hand gesture210.(i) The multimodal act1010may be a combination and/or sequence of another multimodal act1010and one of the hand gestures210.(j) The multimodal act1010may be a combination and/or sequence of another multimodal act1010and a non-gesture input1060, for example, the voice input1020, the text input1030, the visual element, selection1040and/or the tactile input1050.

The multimodal act1010may include multiple iterations of the constructions (i) and/or (j) above. Each multimodal act1010is defined by a unique combination and/or sequence represented by a multimodal sequence1011which comprises of one or more of the multimodal actions, for example, the hand gesture210, the voice input1020, the text input1030, the visual element, selection1040, the tactile input1050and/or the multimodal act1010. Each of the multimodal acts1010starts and ends with an idle state1080which is a virtual state identifying the start and/or end of the unique multimodal sequence1011of the multimodal act1010. The one or more multimodal actions which constitute the multimodal sequence1011may be defined to occur in sequence and/or in parallel to one another. The one or more hand gestures210and the one or more non-gesture input1060may support a context to each other thus making detection and/or classification of the multimodal act1010as there are fewer possible hand gestures210and/or non-gesture input1060candidates which are valid to match a runtime multimodal sequence of one or more user actions. For example, in case the multimodal act1010includes a voice input such as the voice input1020that specifies a text related application function, only hand gestures210relating to text manipulation may be considered during detection and/or classification.

Reference is now made toFIG. 11which is a schematic illustration of a multimodal FSM defining a multimodal act, according to some embodiments of the present disclosure. An illustration1100depicts a multimodal FSM1101which may represent a multimodal sequence such as the multimodal sequence1011defining a multimodal act such as the multimodal act1010. The multimodal FSM1101may represent one or more multimodal actions, for example, hand gestures such as the hand gestures210and/or non-gesture input such as the non-gesture input1060. The multimodal FSM1101starts with a start point1110which may be a virtual state indicating an idle state of the multimodal FSM1101and may include one or more states1102A,1102B,1102C through1102N. Each of the states1102A-1102N may represent one of the hand gestures210and/or the non-gesture input1060. The multimodal FSM1101is ended with an FSM end point1120which identifies the completion of the multimodal sequence1011at which point the detection and/or classification process provides an estimation of the detected multimodal act. The start point1110and/or the end point1120may be defined by a virtual idle state such as the idle state1080. Optionally, any transition between one or more of the multimodal FSM states1102A-1102N may be detected and logged by a system and/or operating system (OS) and used for one or more of a plurality of uses, for example, use of the current active application, use of other one or more applications and/or use of the OS controlling the execution environment. Optionally, the multimodal FSM1101may be constructed as a complex multimodal act such as the multimodal acts1010by including repetitions of one or more of the states1102A-1102N, splitting to several parallel and/or sequential paths and/or combining two or more multimodal FSMs1101and/or parts thereof. The multimodal sequence1011being represented as the multimodal FSM1101may improve detection and/or classification of the one or more multimodal acts1010. More specifically, detection of the one or more hand gestures210included in the multimodal act1010may be highly improved since each of the hand gestures210is constructed using a discrete architecture. Each of t hand gestures210is defined by a hand gesture sequence such as the hand gesture sequence201represented by hand features records such as the hand features records. Each of the hand features records which is defined by discrete hand values scores each indicating a finite number of states of a corresponding hand feature which may be easily identified. Detection, recognition and/or classification may be simplified because there is no need for the user application to perform hand skeleton modeling for recognition, identification and/or classification of the hand gestures210. Furthermore, computer learning and/or computer vision processing may be significantly reduced due to the discrete architecture as performed by the hand gesture detection process100.

Reference is now made toFIG. 12which is a schematic illustration of exemplary multimodal acts, according to some embodiments of the present disclosure. An illustration1200depicts 3 multimodal acts1010A,1010B and1010C such as the multimodal acts1010. Each of the multimodal acts1010A,1010B and1010C is defined by a multimodal sequence such as the multimodal sequence1011, where a multimodal sequence1011A defines the multimodal act1010A, multimodal sequence1011B defines the multimodal act1010B and multimodal sequence1011C defines the multimodal act1010C. Each of the multimodal sequences1011A,1011B and1011C starts and ends with an idle state such as the idle state1080identifying the start and the end of the multimodal acts1010A,1010B and1010C respectively.

The multimodal act1010A is associated with an item fill application function in, for example, a drawing tool application. As shown, the multimodal sequence1011A includes 3 multimodal actions—an item visual element selection1040A, a brush hand gesture210B and a color voice input1020A. The item visual element selection1040A identifies an item in the drawing area of the drawing tool application. The item visual element selection1040A may be detected, for example, as selection of an item using, for example, a touchscreen, a digital surface and/or a pointing device. The brush hand gesture210B identifies a brush hand gesture, for example, swinging an open hand from left to right which is associated with an item fill application function relating to the item selected by the item visual element selection1040A. The color voice input1020A identifies the color, for example blue which is to be used to fill the item selected by the item visual element selection1040A. The outcome of the multimodal act1010A is the item selected by the item visual element selection1040A is filled with blue color.

The multimodal act1010B is associated with a text edit application function in, for example, a text editor application. As shown, the multimodal sequence1011B includes 2 multimodal actions—a selection hand gesture210C and a text input1030A. The selection hand gesture210C identifies a selection hand gesture, for example, moving a pointing index finger across a text presented on a screen by, for example, the text editing application to select a text location which needs editing. The text input1030A inserts the new and/or modified text at the location identified by the selection hand gesture210C. The outcome of the multimodal act1010B is the text identified by the selection hand gesture210C is updated with the text received by the text input1030A.

The multimodal act1010C is associated with a text message transmittal item application function using, for example, a text messaging application. As shown, the multimodal sequence1011C includes 4 multimodal actions—a text message hand gesture210D, a contact person voice input1020B, a text input1030B and a send hand gesture210E. The text message hand gesture210D, for example, making a scribble motion and thrusting the hand left is detected and initiates a text messaging application. The contact person voice input1020B identifies a contact person from a contact list of the text messaging application. The text input1030B inserts the message text to the message. The send hand gesture210E, for example, thrusting a hand to the right is detected and initiates a transmittal operation of the message to the contact person identified by the contact person voice input1020B.

It is expected that during the life of a patent maturing from this application many relevant DFE, HMI and/or NUI will be developed and the scope of the term DFE, HMI and/or NUI is intended to include all such new technologies a priori.

The term “consisting of” means “including and limited to”.

As used herein, the singular form “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “hand gesture” or “at least hand gesture” may include a single hand gesture and/or two hands gestures.

According to some embodiments of the present disclosure there is provided a computer implemented method for detecting a hand gesture of a user, comprising:a) Receiving a plurality of sequential logic models each representing one of a plurality of hand gestures. The sequential logic model maps one or more of a plurality of pre-defined hand poses and pre-defined hand motions. Each one of the plurality of pre-defined hand poses and hand motions is represented by one of a plurality of pre-defined hand features records defined by one or more of a plurality of discrete hand values each indicating a state of one of a plurality of hand features of a reference hand.b) Receiving a runtime sequence of a plurality of runtime hand datasets each defined by a plurality of discrete hand values scores each indicating a current state of a respective one of said plurality of hand features of a moving hand of a user. The plurality of discrete hand values score is inferred by an analysis of a plurality of timed images visually depicting the moving hand.c) Submitting the plurality of runtime hand datasets and the plurality of pre-defined hand features records in one or more SSVM functions to generate a plurality of estimation terms for each of the plurality of runtime hand datasets with respect to the plurality of pre-defined hand features records.d) Estimating which one of the plurality of hand gestures best matches the runtime sequence depicted in the plurality of timed images by optimizing one or more score functions which use the estimation terms for each of the plurality of runtime hand datasets within the runtime sequence.

Each one of the plurality of discrete hand values is represented by a Boolean formula which is defined in the form of a Conjunctive Normal Form (CNF).

The one or more SSVM functions generate the plurality of estimation terms by applying a plurality of parametric functions to each of the plurality of runtime hand datasets to identify one or more parametric function providing highest detection accuracy for each of the plurality of runtime hand datasets. Each one of the plurality of parametric functions simulates each of the plurality of discrete hand values over each of the plurality of discrete hand values scores.

The one or more SSVM functions comprise a plurality of sub-functions each receiving as an input one of the plurality of runtime hand datasets and one of the plurality of hand features records. Each of the plurality of sub-functions is assigned with a weight which is learned by the SSVM functions during a training session.

The one or more SSVM functions comprise a plurality of sub-functions each receiving as an input one of the plurality of runtime hand datasets and two of the plurality of hand features records which represent a current and a predecessor one of the plurality of hand poses and/or hand motions within the sequential logic model. Each of the plurality of sub-functions is assigned with a weight which is learned by the SSVM functions during a training session.

Optionally, the sequential logic model is represented as an FSM. Each state of said FSM correlates to a respective one of the plurality of pre-defined hand features records. The FSM is augmented with the said one or more score functions over one or more sequence within the FSM prior to the optimization.

Optionally, the one or more SSVM functions are specialized by selecting one or more of the plurality of sequential logic models which represents a context registered hand gesture from the plurality of hand gestures.

Optionally, the plurality of runtime hand datasets is estimated as one of a plurality of hand poses and/or hand motion which are not pre-defined.

Optionally, the detection includes detection of one or more of a plurality of multimodal acts. The one or more multimodal acts comprise one or more of the plurality of hand gestures and one or more non-gesture user interaction. The non-gesture user interaction is a member of a group consisting of a text input, a visual element selection, a tactile input and/or a voice input.

According to some embodiments of the present disclosure there is provided a system for detecting a hand gesture of a user, comprising a storage storing a plurality of pre-defined hand features records, a memory storing a code and one or more processors coupled to the storage and the memory for executing the stored code. The code comprising:a) Code instructions to receive a plurality of sequential logic models each representing one of a plurality of hand gestures. The sequential logic model maps one or more of a plurality of pre-defined hand poses and pre-defined hand motions. Each one of the plurality of pre-defined hand poses and hand motions is represented by one of a plurality of pre-defined hand features records defined by one or more of a plurality of discrete hand values each indicating a state of one of a plurality of hand features of a reference hand.b) Code instructions to receive a runtime sequence of a plurality of runtime hand datasets each defined by a plurality of discrete hand values scores each indicating a current state of a respective one of said plurality of hand features of a moving hand of a user. The plurality of discrete hand values score is inferred by an analysis of a plurality of timed images visually depicting the moving hand.c) Code instructions to submit the plurality of runtime hand datasets and the plurality of pre-defined hand features records in one or more SSVM functions to generate a plurality of estimation terms for each of the plurality of runtime hand datasets with respect to the plurality of pre-defined hand features records.d) Code instructions to estimate which one of the plurality of hand gestures best matches the runtime sequence depicted in the plurality of timed images by optimizing one or more score functions which use the estimation terms for each of the plurality of runtime hand datasets within the runtime sequence.

According to some embodiments of the present disclosure there is provided a software program product for detecting a hand gesture of a user, comprising a non-transitory computer readable storage medium and program instructions:a) First program instructions to receive a plurality of sequential logic models each representing one of a plurality of hand gestures. The sequential logic model maps one or more of a plurality of pre-defined hand poses and pre-defined hand motions. Each one of the plurality of pre-defined hand poses and hand motions is represented by one of a plurality of pre-defined hand features records defined by one or more of a plurality of discrete hand values each indicating a state of one of a plurality of hand features of a reference hand.b) Second program instructions to receive a runtime sequence of a plurality of runtime hand datasets each defined by a plurality of discrete hand values scores each indicating a current state of a respective one of said plurality of hand features of a moving hand of a user. The plurality of discrete hand values score is inferred by an analysis of a plurality of timed images visually depicting the moving hand.c) Third program instructions to submit the plurality of runtime hand datasets and the plurality of pre-defined hand features records in one or more SSVM functions to generate a plurality of estimation terms for each of the plurality of runtime hand datasets with respect to the plurality of pre-defined hand features records.d) Fourth program instructions to estimate which one of the plurality of hand gestures best matches the runtime sequence depicted in the plurality of timed images by optimizing one or more score functions which use the estimation terms for each of the plurality of runtime hand datasets within the runtime sequence.

The first, second, third and fourth program instructions are executed by one or more computerized processors from the non-transitory computer readable storage medium.

Optionally, detection of the software program product includes detection of one or more of a plurality of multimodal acts. The one or more multimodal act comprise one or more of the plurality of hand gestures and one or more non-gesture user interaction. The non-gesture user interaction is a member of a group consisting of a text input, a visual element selection, a tactile input and/or a voice input.

Certain features of the examples described herein, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the examples described herein, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination or as suitable in any other described embodiment of the disclosure. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.