This application claims the benefit of priority from Chinese Patent Application No. 202110242312.3, filed on Mar. 5, 2021. The content of the aforementioned application, including any intervening amendments thereto, is incorporated herein by reference in its entirety.
This application relates to information technology-based teaching, and more particularly to a fusion method for movements of a teacher in a teaching scene.
A hybrid-augmented teaching scene can not only synchronize and fuse virtual and real scenes with users, but also construct an information loop of interactive feedback between the users and the virtual and real scenes through real-time interaction. The advancement of the 5th-generation (5G) commercialization and the continuous development of the virtual reality (VR)/augmented reality (AR)/mixed reality (MR) industry realize an effective fusion of multiple motion mechanisms in the hybrid-augmented teaching scene, which is beneficial to enhancing the fusion of information technology and teaching courses. In view of this, it will play an extremely active role in a new generation of three-dimensional teaching environment and has a brilliant application prospect.
Unfortunately, there are some limitations in the fusion of multiple motion mechanisms in the hybrid-augmented teaching scene in the prior art. For example, in the existing hybrid-augmented teaching scene, the real teaching space has the same scale as the virtual teaching space, such that it is difficult to realize the movement gain between different scales, failing to eliminate a contradiction between finiteness of the real teaching space and infiniteness of the virtual teaching space and affecting the realistic experience effect of the interaction. Moreover, when a user experiences a long-time and frequent and movement in the hybrid-augmented teaching scene, motion sickness and disorientation will occur to the user. The existing motion mechanisms distract the user by deviating from reality, which fails to improve the user comfort and attention. In addition, the existing hybrid-augmented teaching scene has insufficient interactive reality, and the motion guidance and sensory feedback cannot fully consider feelings of the user, thereby affecting visual perception and interactive experience of the user.
An object of this application is to provide a fusion method for movements of a teacher in a teaching scene to overcome the defects in prior art, which provides a new and complete approach for interaction, perception and fusion of the teacher in a hybrid-augmented teaching scene.
Technical solutions of this application are described as follows.
This application provides a fusion method for movements of a teacher in a teaching scene, comprising:
(1) dividing a virtual teaching space into different areas by a Spatial Mapping technology; and calculating a scale transformation of moving position and area of a virtual model and the teacher according to a gain scaling factor of the virtual teaching space and a real teaching space;
(2) constructing a series of motion response rules; adopting a collision detection algorithm and an A* algorithm to realize movement of the virtual model; and enhancing motion perception of the teacher by using prompting modes of a direction arrow, a third-person perspective and a navigation map; and
(3) performing a transition between different transmission scenes by adopting transparent gradient modes of texture and colors; support the teacher to select important and difficult contents to explain by a preview screen, an automatic path finding algorithm and a backtracking mechanism; and enhancing fusion representation of the teacher in moving process by adopting a collision feedback, a haptic feedback and a visual feedback.
Compared to the prior art, this disclosure has the following beneficial effects.
A three-dimensional surface model of a real teaching space is collected and is divided into different grid areas. A scale transformation of a moving position and a moving range of a teacher and a moving position and a moving range of a virtual model are calculated according to a gain scaling factor of the real teaching space and the virtual teaching space to realize a movement gain between different scales, eliminating a contradiction between finiteness of the real teaching space and infiniteness of the virtual teaching space, and improving the realistic experience effect of the interaction. Motion response rules are constructed by using a recognition-tracking algorithm to mark movement trigger information of virtual objects. A collision detection algorithm is configured to realize autonomous movement or of the virtual model. A motion perception of the teacher is enhanced by using prompting modes of a direction arrow, a third-person perspective and a navigation map, such that when the teacher experiences a long-time and frequent and movement in a hybrid-augmented teaching scene, motion sickness and disorientation fail to occur to the teacher, thereby improving the teacher comfort and attention. A transition between different transmission scenes is performed by adopting transparent gradient modes of texture and color through setting of a transmit point followed in combination with a preview scene, an automatic path finding algorithm and a backtracking mechanism to support the teacher to select important and difficult contents to explain. Fusion representation of the teacher in moving process is enhanced by adopting a collision feedback, a haptic feedback and a visual feedback to improve interactive authenticity, such that motion guidance and sensory feedback can fully consider feelings of the teacher, thereby improving visual perception and interactive experience of the teacher. The higher demand for a hybrid-augmented teaching brings a higher demand for the fluency, comfort and realistic experience of the teacher's movements in the teaching process. The application can meet the needs of effect of the fusion of movements in the hybrid-augmented teaching scene.
The present disclosure will be further described in detail with reference to the embodiments and the accompanying drawings to make objects, technical solutions and advantages of the present disclosure better understood. It should be understood that the embodiments presented in the accompanying drawings are merely illustrative of the disclosure, and are not intended to limit the present disclosure. In addition, technical features in the following embodiments can be combined with each other as long as they do not conflict with each other.
As shown in
(1) Normalization
A virtual teaching space is divided into different areas by a Spatial Mapping technology. A scale conversion of moving position and area of a virtual model and the teacher is calculated according to a gain scaling factor of the virtual teaching space and a real teaching space.
(1-1) Determination of Moving Range
A three-dimensional (3D) surface model of the real teaching space is collected by a depth camera. The virtual teaching space is divided into different grid areas by the Spatial Mapping technology. A boundary plane of the virtual teaching space is extracted, and a collider is added to the boundary plane to define the moving range of the teacher and the 3D surface model.
(1-1-1) Acquisition of 3D Surface Model of Real Teaching Space
Point cloud data of the real teaching space are collected by the depth camera, such as 3D position and depth value. Grids of a virtual surface model are generated by using a 3D reconstruction technology. The 3D surface model is divided into model objects comprising walls, tables and chairs according to spatial characteristics of the 3D surface model.
(1-1-2) Grid Division of Virtual Teaching Space
As shown in
(1-1-3) Determination of Moving Range of Teacher and Virtual Object
The boundary planes of the virtual teaching space in horizontal, longitudinal and vertical directions are obtained by traversing to correspondingly create virtual surfaces. Covariance matrixes of each of the boundary planes are calculated. A center point, direction and side length of a bounding volume hierarchy (BVH) box are determined by combining the extreme values of the axes. It is assumed that there are n triangles (, , ) in each of the boundary planes (0≤<), each of the covariance matrixes is expressed as follow:
where and represent a full area and centroid of each of the boundary plane, respectively; and represent an area and centroid of a triangle k, respectively; and represent weight of x and y axes, respectively; and and represent values of centroid on the x and y axes.
The collider of each of the boundary planes is constructed by using BVH-based tree-like hierarchical structure to define the moving range of the teacher and a moving range of a virtual object in the hybrid-augmented teaching environment.
(1-2) Object Position
A scale transformation between the virtual teaching space and the real teaching space is calculated according to the moving range of the teacher and the moving range of the virtual object. A position and an orientation of a virtual model are obtained in the real teaching space by using parallel ranging and imaging. A position and an orientation of the teacher are determined in the real teaching space by using an inertial navigation technology to realize a position conversion between the virtual teaching space and the real teaching space.
(1-2-1) Realization of Scale Transformation Between Real Teaching Space and Virtual Teaching Space
The scale transformation between the virtual scene and the real teaching space is calculated according to the moving range of the teacher and the moving range of the virtual object. A transformation of position and range are enabled between the teacher, the virtual model and environment in the virtual teaching space and the real teaching space by setting a homogeneous coordinate W, where W>1 means zoom out and W<1 means to zoom in.
(1-2-2) Acquisition of Position and Orientation of Virtual Model
A position coordinate (x, y, z) and an orientation posture (tx, ty, tz) of the virtual model comprising mountain, solar system, magnetic field model are positioned in the real teaching space by using the parallel ranging and the imaging technology. Information of the virtual model is calculated and updated in the real teaching space in real time when the position and orientation of the virtual model change, such as a distance of the teacher with respect to the wall, desk, chair, blackboard, lectern in the virtual objects.
(1-2-3) Acquisition of Position and Orientation of Teacher
A position coordinate (x′, y′, z′) and an orientation posture (tx′, ty′, tz′) of the teacher are calculated in the real teaching space by using the inertial navigation technology with help of an accelerometer and a gyroscope. Changes in the position and the orientation of the teacher are calculated and positioned with respect to surrounding environment by dead reckoning.
(1-3) Movement Gain of Teacher
A weighted calculation is performed to a gain scaling factor in combination with collected distance, speed and time of the movements of the teacher according to the moving range of the teacher and the moving range of the 3D surface model by using an Eye-Level Scaling method. A movement gain of the teacher is calculated in the virtual teaching space by using a Seven-League Boots technology to realize the normalization of the movements of the teacher in the virtual teaching space and the real teaching space.
(1-3-1) Weighted Calculation of Gain Scaling Factor
The weighted calculation is performed to obtain the gain scaling factor according to scale mapping between the virtual teaching space and the real teaching space by using the Eye-Level Scaling method in combination with the collected distance, speed and time of the movements of the teacher to calculate changes in the position and the orientation of the teacher in the virtual teaching space and the real teaching space. For example, the moving data of the teacher in the real environment is collected in the hybrid-augmented teaching environment of solar system sports teaching. Weights of a solar model is set as P1, P2 and P3, respectively, by a factor analysis method according to importance of the solar model in the motion information. The gain scaling factor of the teacher is calculated by weighted combination according to the follow formula:
where S represents a distance between the teacher and the solar model; V represents a current movement speed of the teacher and T represents an initial scaling factor of the solar model.
(1-3-2) Normalization of Coordinate in Real Teaching Space and Virtual Teaching Space
The teacher observes the virtual object in the virtual scene from a giant perspective or a dwarf perspective. A coordinate of the virtual model during the moving process is calculated using a coordinate conversion formula according to parameters of the scale transformation between the virtual teaching space and the real teaching space to realize the normalization of relevant objects in the virtual teaching space and the real teaching space. For example, based on the following conditions: the teacher uses the solar model in a teaching process; the gain scaling factor is known to N; a position coordinate of the solar model in the real teaching space is supposed to be (X, Y, Z); an initial position coordinate of a center point of a head of the teacher in the real teaching space is supposed to be (Xp, Yp, Zp); and the teacher has a height of H, a position coordinate (X′, Y′, Z′) of the solar model in the virtual scene is calculated according to the following transformation formula:
In this embodiment, the virtual teaching space includes a virtual resource, virtual teachers and students and a virtual model.
(1-3-3) Acquisition of Movement Gain
Motion and position data of the teacher are tracked and captured in teaching activities. Changes of field of view of the teacher are followed in the real teaching space. Accumulated position and gesture of the teacher are calculated in the virtual teaching space by using the Seven-League Boots technology to obtain changes of the movement gain of the teacher.
(2) Motion Perception
A series of motion response rules are constructed. A collision detection algorithm and an A* algorithm are adopted to realize movement of the virtual model. Motion perception of the teacher is enhanced by using prompting modes of a direction arrow, a third-person perspective and a navigation map.
(2-1) Construction of Motion Response Policy
Movements of torso, gesture and head of the teacher are captured by using a recognition-tracking algorithm. A series of motion response rules are constructed to unify movement effects in the virtual teaching space and the real teaching space. A moving process of the teacher is marked with a line segment. A selected state of the virtual model is highlighted.
(2-1-1) Tracking of Movement of Teacher
33 key points of a body pose of the teacher are positioned by an assembly line attitude estimation method. The torso posture and movements of the teacher are tracked in a teaching process by using a BlazePose algorithm to accurately identify movement behavior of the teacher's body. The head movements are tracked by using a head pose estimation, such as raising head, shaking head and turning head. Head behaviors of the teacher are recognized by adopting a facial key point detection model. 21 bone nodes of teacher's palm, fingertips and joints of phalanx are tracked in real time by using a MediaPipeHands detection technology of key points of hand. As shown in
(2-1-2) Construction of Motion Response Rule
The motion response rules are constructed in the hybrid-augmented teaching environment according to collected movement actions of the teacher, such as walking, swinging arms, grasping, raising head. As shown in
(2-1-3) Annotation of Movement Trigger Information
Actions of the teacher are tracked in the real teaching space, and a moving path of the teacher is dynamically displayed in real time by using a LineRender drawing method in the virtual scene. When a sight line and a gesture of the teacher move to the collider of the virtual model, the virtual model is highlighted to trigger a prompt of the selected state.
(2-2) Motion Mechanism of Virtual Model
The virtual model is selected by using an external bounding box detection algorithm. The virtual model is prevented from passing through relevant virtual objects during the moving process by using a collision detection algorithm. The virtual model avoids obstacles in the virtual scene by using an A* algorithm. The virtual model autonomously moves by using a 3D Dijkstra algorithm.
(2-2-1) Acquisition of Virtual Model
The teacher chooses the virtual model in a virtual environment by interactive modes of gaze and gesture according to teaching needs. The virtual model is selected by using the external bounding box detection algorithm when the collision is detected between the teacher and the BVH box of the virtual model. The selected virtual model is highlighted and the prompt of the external bounding box of the virtual model are displayed in the virtual teaching space.
(2-2-2) Motion of Virtual Model
The teacher places the obtained virtual model at a certain position in the virtual scene or real teaching space through translation, rotation and scaling. The virtual model is prevented from passing through tables, chairs, teacher and students in the real teaching space or relevant virtual objects in the virtual scene by using the collision detection algorithm.
An intersection situation of a bounding box A and a plane P is determined by comparing a size of ri and s to detect whether the collision occurs between the bounding box A and the plane P. A computational formula of ri is expressed as follows:
ri=(Vi−C)·n=(C±e00±e1u1±e2u2−C)·n=(±e0u0±e1u1±e2u2)·n;
where a vector n is a normal vector of the plane P; C is a center of the bounding box A; a vector ui (0≤i≤3) is a local coordinate axis with C as an origin; a scalar ei (0≤i≤3) is a half of a length of three sides of the cuboid bounding box A; Vi is 8 vertices of the bounding box A, and Vi=C±e0u0±e1u1±e2u2 (0≤i≤7); s is a distance from C to P; and ri (0≤i≤7) is weight of a distance between Vi and C along the vector n.
When −r≤s≤r, the bounding box intersects A with the plane P, which is considered that the virtual model in the bounding box A collides with the plane P.
(2-2-3) Autonomous Movement of Virtual Model
The teacher sets key motion nodes for the virtual model. The virtual model avoids the obstacles in the virtual scene by using the A* algorithm. A shortest path between adjacent nodes of the virtual model is calculated by using the 3D Dijkstra algorithm. The virtual model autonomously moves to avoid corresponding occlusion objects by applying a path finding navigation algorithm.
(2-3) Construction of Motion Prompt Mechanism
A transparent 3D external bounding box, a text prompt and an arrow prompt are added in a hybrid-augmented teaching environment by using a Simulated CAVE method. A third-person perspective is provided to support the teacher to follow the virtual model to move, so as to observe a moving law of the virtual model. A navigation map is generated to help the teacher master a global content of the virtual scene and navigation and positioning are facilitated.
(2-3-1) Setting of Motion Prompt
The transparent 3D external bounding box and the arrow prompt are added centered on the teacher in the hybrid-augmented teaching environment by using the Simulated CAVE method considering a phenomenon that the teacher frequently moves and is easy to lose direction sense. The 3D external bounding box moves and rotates with the teacher. The teacher is always at the center of the external bounding box, and the prompt of an instrument panel is updated on an upper right corner of the external bounding box directly in front of the teacher.
(2-3-2) Switching of Third-Person Perspective
First-person and third-person perspective options are added in the hybrid-augmented teaching environment, where the third-person perspective supports the teacher to follow the virtual model to move, so as to observe the law of moving the virtual model, such as walking, swinging arms, grasping, and raising the head. When the teacher manipulates the virtual model through gesture and sight line, an operating mechanism of the virtual model can be presented through the third-person perspective, such as an instant transmission and the movement gain.
(2-3-3) Construction of Navigation Map
The navigation map of the hybrid-augmented teaching environment from a top view is generated according to the position and orientation of the teacher. As show in
(3) Fusion of Movement
A transition between different transmission scenes is performed by adopting transparent gradient modes of texture and colors. The teacher is supported to select important and difficult contents to explain by a preview screen, an automatic path finding algorithm and a backtracking mechanism. Fusion representation of the teacher is enhanced in the moving process by adopting a collision feedback, a haptic feedback and a visual feedback.
(3-1) Generation of Fast Transmission Mechanism
A position of a transmit point is selected and set in the hybrid-augmented teaching environment according to the series of motion response rules and a motion mechanism of the virtual model. The teacher is assisted to adapt to spatial orientation and layout of a new scene by using representations of highlighting and special color in a transmission process. The transition between different transmission scenes is performed by adopting transparent gradient modes of texture and color.
(3-1-1) Preset of Transmit Point of Virtual Teaching Space
According to teaching needs, a gaze focus of the teacher is positioned by eye tracking. A spatial position of the transmit point is selected and set, so as to avoid overlapping of the transmit point with objects in the virtual teaching space or the real teaching space. A highlighted and transparent circular mark is added on the transmit point and updated to the map synchronously, where after clicking the circular mark, it will be switched and transmitted to the virtual scene corresponding to the transmit point.
(3-1-2) Visual Transition of Scene Transmission
Vectors of the teacher's gaze direction and light direction are obtained by transition modes of fading in and out and fast blur animation in the transmission process. Objects in a new scene, such as desks, chairs, virtual teaching aids, are highlighted by a Fresnel method. Images of the new scene is highlighted by adopting special colors to help the teacher adapt to the spatial orientation and layout of the new scene, so as to reduce a cognitive load after the virtual scene switches. A Fresnel approximation formula is expressed as follow:
F=F0+(1−F0)*(1−dot(,)){circumflex over ( )};
where is a surface normal vector; is viewing angle vector; F0 is a reflection coefficient; and is an enhancement time, which controls a size of Fresnel's influence area.
(3-1-3) Fitting of Transitional View
Content of a transitional field of view between transmission scenes is fitted according to duration and position in combination with body posture, head orientation, movement track and visual field of view of the teacher by adopting the transparent gradient modes of texture and color to realize a natural transition when the transmission scene is switched.
(3-2) Construction of Mechanism for Enhancing Sense of Direction
An instrument panel of the external bounding box and the arrow prompt function are added to assist the teacher to re-determine position and orientation according to a transmission mechanism and a motion prompt mechanism by using the Simulated CAVE method. A preview image of the transmission scene is generated and the obstacles are avoided by an automatic path finding algorithm. A backtracking mechanism is established to support the teacher to select important and difficult contents to explain.
(3-2-1) Redirection of Virtual Teaching Space
When transmitting transferring to a new virtual scene or moving to the boundary area of the virtual teaching space, the instrument panel and the arrow prompt of the external bounding box are updated in real time according to the Simulated CAVE method to show the moving direction and distance of the teacher. As show in
(3-2-2) Transmission Preview
A preview image of each transmission scene is generated in the hybrid-augmented teaching environment. The teacher can select different transmission scenes through the preview image. A preview image of the virtual scene after transmission is generated according to position and gazing direction of the teacher. The teacher avoids the obstacles in the new scene according to parameters of an initial state by adopting the automatic path finding algorithm.
(3-2-3) Motion Backtracking
A position and state of the teacher at a certain moment in the virtual scene are recorded as backtracking points, such as position, posture and gazing direction of the teacher, distribution of the virtual model, gesture and state of motion of each of models. According to a time development order, the backtracking points are organized based on a linked list structure, and a logical relationship among the backtracking points is established. The backtracking points are switched to realize switching of the virtual scene in a series of important moments, so as to support the teacher to choose the corresponding important and difficult contents for explanation. An effect of the backtracking mechanism is shown in
(3-3) Creation of Motion Feedback Mechanism
A collision feedback is triggered by using images, sounds and vibrations when the teacher collides with the virtual model according to an enhancement mechanism of a motion direction sense. A haptic feedback is calculated by capturing a transient value of motion of the virtual model to effectively avoid a “penetration” phenomenon when selecting the virtual model. The movement of the teacher is represented by using a visual feedback effect of the line segment, highlighting and particle effect, so as to realize fusion of movements of the teacher in the teaching scene.
(3-3-1) Creation of Collision Feedback Mechanism
A collision threshold is set between the teacher and a relevant virtual model according to a moving range of the hybrid-augmented teaching environment and layout information of the virtual model. As shown in
(3-3-2) Creation of Haptic Feedback Mechanism
Parallel modules with different update frequencies are generated by using a multi-rate method to record gaze, gesture and body movement of the teacher. The haptic feedback is calculated by capturing a moving transient value of the teacher. A motion trajectory of the virtual model is recorded in a form of 9-DOF. As shown in
(3-3-3) Creation of Visual Feedback Mechanism
A process of observing direction or moving an object of the teacher is recorded by using the line segment. Interaction between the teacher and relevant virtual objects is recorded by highlighting and special color. Specifically, a gesture trajectory of the teacher is recorded when the teacher writes on the blackboard. Content of a relevant area is displayed by using the special color. The feedback effect is represented when the teacher hits or touches the virtual objects using the particle effect.
The partial content is not described in detail in this application, which is known to those skilled in the prior art.
Described above are only preferred embodiments of this application, and are not intended to limit this application. Any modification, replacement and improvement made without departing from the spirit and principle of this application shall fall within the protection scope of this application.
Number | Date | Country | Kind |
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202110242312.3 | Mar 2021 | CN | national |
Number | Name | Date | Kind |
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20130120372 | Lee | May 2013 | A1 |
20180096450 | Monk | Apr 2018 | A1 |
20180122254 | Rangan | May 2018 | A1 |
20190139430 | Ghatage | May 2019 | A1 |
20200226941 | Kakaraparthy | Jul 2020 | A1 |
Number | Date | Country |
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110688005 | Jan 2020 | CN |
112230772 | Jan 2021 | CN |
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