TWI893992B - Device and method for generating evaluation content based on practice status and physiological status - Google Patents

Abstract

A device for generating evaluation content based on practice status and physiological status and a method thereof are provided. By reading practice evaluation data in a practice mode, obtaining interactive connection data from the practice evaluation data based on skill practice information o indicating user's hand movements and user’s body postures in skill demonstration scene for the user can perform the skill demonstration realize the practice of professional skills in the scene, choosing whether to create evaluation data based on the skill practice information and physiological state data of the user, reading the practice evaluation data corresponding to the evaluation data in an evaluation mode to provide the user with skills in the skill realize the practice of professional skills in the demonstration scene, and generating an evaluation score based on the user's hand movements and user’s body postures in the skill demonstration scene, the device and the method can accurately detect user movements and physiological conditions, and can achieve the effect of interaction and measurement through reality technology.

Description

Device and method for generating assessment content based on training status and physiological conditions

A device and method for generating assessment content, particularly a device and method for generating assessment content based on training conditions and physiological conditions.

With the development of extended reality (XR) technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), these technologies are gradually being applied to education and training.

However, existing real-world systems mostly collect user actions and interact with them based on the corresponding results of the user's actions. At the same time, when interacting with users, existing real-world systems usually do not monitor all actions of the user during the interaction process, but only interact with the user based on the user's last action.

Therefore, when using a real-world system to assess the practical ability of professional skills, the assessment can only be based on whether the user's implementation results are correct or not. It is impossible to judge whether the user is familiar with and/or proficient in the operation process based on the user's implementation process. Once the user accidentally produces the correct result, the user may receive a passing assessment result, which results in the assessment results being unable to effectively reflect the user's proficiency and familiarity with the professional skills.

In summary, we can see that the long-standing problem in previous technologies is that real-world technology cannot provide an effective assessment mechanism. Therefore, it is necessary to propose improved technical means to solve this problem.

In view of the problem that the existing technology only gives scores based on the final results, the present invention discloses a device and method for generating assessment content based on the training status and physiological status, wherein:

The device disclosed in the present invention for generating assessment content based on practice conditions and physiological conditions includes at least: a data loading module for loading skill reality data corresponding to professional skills, and reading scene data and practice assessment data corresponding to the interaction mode from the skill reality data, the practice assessment data including a plurality of interaction connection data, the interaction mode being an assessment mode or a practice mode, and when the interaction mode is an assessment mode, the practice assessment data including assessment data corresponding to professional skills in the assessment question bank, and the assessment data corresponding to the interaction connection data; a communication interface for transmitting scene data to a wearable device so that the wearable device displays a skill demonstration scene based on the scene data, and for receiving the hand movements and body postures of the user in the skill demonstration scene generated by the wearable device to generate skill practice data. The module receives the user's physiological status data obtained by the wearable device when the interaction mode is the practice mode. The skill practice information includes the user's hand movement signals and body posture information obtained by the wearable device. The question bank establishment module is used to record the assessment data as the previous interaction connection data obtained before receiving the skill practice message in the assessment question bank when the interaction mode is the practice mode and the skill practice information or physiological status data is determined to meet the assessment conditions. The interaction processing module is used to obtain interaction connection data from the practice assessment data based on the skill practice message and transmit the interaction connection data to the wearable device through the communication interface, so that the wearable device displays real-world content corresponding to the user's hand signals and body posture in the skill demonstration scene based on the interaction connection data.

The method disclosed in the present invention for generating assessment content based on practice conditions and physiological conditions includes at least the following steps: a real-world server provides a wearable device connection; the real-world server loads skill real-world data corresponding to professional skills, and reads scene data and exercise assessment data corresponding to the interaction mode from the skill real-world data, the exercise assessment data includes a plurality of interaction connection data, the interaction mode is an assessment mode or a exercise mode, when the interaction mode is an assessment mode, the exercise assessment data includes assessment data corresponding to professional skills in the assessment question bank, and the assessment data corresponds to the interaction connection data; the wearable device displays a skill demonstration scene based on the scene data transmitted by the real-world server; the wearable device generates skill practice information based on the hand movements and body postures of the wearer in the skill demonstration scene, and the skill practice The message includes the user's hand movement signal and body posture information obtained by the wearable device; the wearable device transmits the skill practice message to the real server, and when the interaction mode is the practice mode, it obtains the user's physiological state data and transmits the physiological state data to the real server; the real server obtains the interaction connection data corresponding to the skill practice message from the practice evaluation data, and transmits the extracted interaction data. The data is transmitted to the wearable device. When the interaction mode is training mode and the real-world server determines that the skill practice information or physiological status data meets the assessment conditions, it records the assessment data in the assessment question bank as the previous interaction connection data obtained before receiving the skill practice information. The wearable device displays the real-world content corresponding to the user's hand signals and body posture in the skill demonstration scene based on the interaction connection data.

The device and method disclosed in the present invention are as described above. The difference between the present invention and the previous technology lies in that the present invention reads the practice assessment data in the practice mode, and obtains the corresponding interactive connection data from the practice assessment data based on the user's hand movements and body postures in the skill demonstration scene, so that the user can practice professional skills in the skill demonstration scene, and chooses whether to establish assessment data based on the skill practice information and the user's physiological state data. In the assessment mode, the practice assessment data corresponding to the assessment data is read to provide the user with the practice of professional skills in the skill demonstration scene, and an assessment score is generated based on the user's hand movements and body postures in the skill demonstration scene. This solves the problems existing in the previous technology and can achieve the technical effect of providing an effective assessment mechanism through real-world technology.

The following will be used in conjunction with drawings and embodiments to describe in detail the features and implementation methods of the present invention. The content is sufficient to enable anyone familiar with the relevant technology to easily and fully understand the technical means used by the present invention to solve the technical problems and implement them accordingly, thereby achieving the effects that can be achieved by the present invention.

This invention allows users to train and assess professional skills by displaying simulated scenes of professional skills through wearable devices that provide extended reality (XR). The extended reality mentioned in this invention includes, but is not limited to, augmented reality (AR), virtual reality (VR), and mixed reality (MR). The professional skills mentioned in this invention include, but are not limited to, surgery and machine repair.

The apparatus implementing the present invention may be a computing device. The computing device described herein includes, but is not limited to, one or more processing modules, one or more memory modules, and hardware components such as a bus that connects various hardware components (including the memory modules and processing modules). Through these hardware components, the computing device can load and execute an operating system, allowing the operating system to run on the computing device, and can also execute software or programs. The computing device also includes a housing, within which the aforementioned hardware components are housed.

The bus of the computing device provided in the present invention may include one or more types of buses, such as a data bus, an address bus, a control bus, an expansion bus, and/or a local bus. Buses used in computing devices include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Peripheral Component Interconnect (PCI) bus, the Video Electronics Standards Association (VESA) local bus, the Universal Serial Bus (USB) bus, and the PCI Express (PCI-E/PCIe) bus.

The processing module of the computing device proposed in the present invention is coupled to a bus. The processing module includes a register group or register space, which can be completely set on the processing chip of the processing module, or completely or partially set outside the processing chip and coupled to the processing chip via a dedicated electrical connection and/or via a bus. The processing module can be a central processing unit, a microprocessor, or any suitable processing element. If the computing device is a multi-processor device, that is, the computing device includes multiple processing modules, then the processing modules included in the computing device are the same or similar, and are coupled and communicated through a bus. In some embodiments, the processing module can interpret a computer instruction or a series of multiple computer instructions to perform specific calculations or operations, such as mathematical operations, logical operations, data comparison, copying/moving data, etc., to drive other hardware components in the computing device or run an operating system or execute various programs and/or modules. The computer instructions can be assembly language instructions, instruction set architecture instructions, machine instructions, machine-related instructions, microinstructions, firmware instructions, or source code or object code written in any combination of one or more programming languages, and the computer instructions can be executed entirely on a single computing device, partially on a single computing device, or partially on one computing device and partially on another connected computing device. The above-mentioned programming languages include object-oriented programming languages such as Common Lisp, Python, C++, Objective-C, Smalltalk, Delphi, Java, Swift, C#, Perl, Ruby, etc., and conventional procedural programming languages such as C or other similar programming languages.

Computing devices usually also include one or more chipsets. The processing module of the computing device can be coupled to the chipset or electrically connected to the chipset through a bus. The chipset is composed of one or more integrated circuits (ICs), including a memory controller and a peripheral input/output (I/O) controller, etc. In other words, the memory controller and the peripheral input/output (I/O) controller can be included in one IC, or can be implemented using two or more ICs. The chipset usually provides input/output and memory management functions, as well as multiple general-purpose and/or dedicated registers, timers, etc., wherein the above-mentioned general-purpose and/or dedicated registers and timers can be accessed or used by one or more processing modules coupled or electrically connected to the chipset. In some embodiments, the chipset may also be part of the processing module.

The processing module of a computing device can also access data from memory modules and mass storage areas installed on the computing device through a memory controller. The aforementioned memory modules include any type of volatile memory and/or non-volatile memory (NVRAM), such as static random access memory (SRAM), dynamic random access memory (DRAM), read-only memory (ROM), and flash memory. The aforementioned mass storage area can include any type of storage device or storage media, such as a hard drive, optical disc, flash drive, memory card, solid-state drive (SSD), or any other storage device. In other words, the memory controller can access data in static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, hard drives, and SSDs.

The processing module of a computing device can also connect to and communicate with peripheral devices or interfaces, such as peripheral output devices, peripheral input devices, communication interfaces, and various data or signal receiving devices, through a peripheral input/output controller via a peripheral input/output bus. A peripheral input device can be any type of input device, such as a keyboard, mouse, trackball, touchpad, or joystick. A peripheral output device can be any type of output device, such as a monitor or printer. The peripheral input device and peripheral output device can also be the same device, such as a touchscreen. The communication interface may include a wireless communication interface and/or a wired communication interface. The wireless communication interface may include an interface supporting wireless local area networks (such as Wi-Fi, Zigbee, etc.), Bluetooth, infrared, near-field communication (NFC), 3G/4G/5G mobile communication networks (cellular networks), or other wireless data transmission protocols. The wired communication interface may be an Ethernet device, a DSL modem, a cable modem, an asynchronous transfer mode (ATM) device, or an optical fiber communication interface and/or component. The data or signal receiving device may include a GPS receiver or a physiological signal receiver. The physiological signals received by the physiological signal receiver include, but are not limited to, heart rate and blood oxygen levels. The processing module can periodically poll various peripheral devices and interfaces, allowing the computing device to input and output data through various peripheral devices and interfaces, and also to communicate with another computing device having the hardware components described above.

The following will first illustrate the device implementing the present invention, using Figure 1, a schematic diagram of the components of the device for generating assessment content based on exercise status and physiological conditions. As shown in Figure 1, the device 100 of the present invention includes a memory 110, a communication interface 130, a storage medium 140, a processor 170, and a bus 190. The processor 170 can be interconnected with the memory 110, the communication interface 130, and the storage medium 140 via bus 190.

Memory 110 can store one or more sets of computer instructions.

The communication interface 130 can provide a connection to the wearable device 400 and can receive data or signals transmitted by the wearable device 400, as well as transmit data or signals to the wearable device 400. The communication interface 130 can also connect to an external network device such as a network storage device or server (not shown) and request and download data from the connected network device.

The storage medium 140 can store data received by the communication interface 130 , and can also store data required by the processor 170 (ie, provided to the processor 170 ); the storage medium 140 can also store data generated by the processor 170 .

The processor 170 may include a data loading module 210, a question bank creation module 250, an interactive processing module 270, an appendable data generation module 230, a skill assessment module 290 and other modules as shown in FIG2 , a module schematic diagram of the present invention. In some embodiments, the processor 170 can execute computer instructions stored in the memory 110 and can generate the modules in "Figure 2" after executing the computer instructions; in other embodiments, the modules in "Figure 2" can be generated by one or more circuits and/or hardware components such as complete or partial chips, that is, the processor 170 includes hardware components that constitute the modules in "Figure 2", that is, the modules included in the processor 170 can be software modules or hardware modules, and the present invention has no special limitations.

The data loading module 210 is responsible for loading the skill reality data corresponding to the professional skills, and reading the scene data and the exercise evaluation data corresponding to the interactive mode from the loaded skill reality data. The interactive mode mentioned in the present invention includes an evaluation mode or an exercise mode, but the present invention is not limited thereto; the skill reality data mentioned in the present invention may include scene data and exercise evaluation data, wherein the scene data may be provided to the wearable device 400 to display the virtual scene required for practicing professional skills or to display the virtual objects required for practicing professional skills in the real world; the exercise evaluation data corresponds to the interactive mode and may include the steps required for the user to complete the practice of professional skills from start to finish. The interaction continuation data for each action performed by the user can allow the wearable device 400 to display changes in objects associated with the action performed by the user. More specifically, when the interaction mode is the practice mode, the practice assessment data corresponding to the interaction mode can include all interaction continuation data corresponding to professional skills, and when the interaction mode is the assessment mode, the practice assessment data corresponding to the interaction mode can include interaction continuation data corresponding to professional skills recorded in the assessment question bank. In the present invention, the interaction continuation data recorded in the assessment question bank is also referred to as assessment data. It should be noted that the assessment question bank may not record the complete interaction continuation data associated with each action, but may only record the identification data or index data of the interaction continuation data associated with the action. The data loading module 210 may load one or more specific interaction continuation data corresponding to the professional skills based on the identification data or index data.

In some embodiments, the data loading module 210 can load the interactive connection data generated by the data generating module 230, but the present invention is not limited thereto.

The data generation module 230 can train a machine learning model and use the trained machine learning model to generate interactive data for each action required to implement a professional skill. In some embodiments, the data generation module 230 can also use a state machine or other method to generate interactive data for each action required to implement a professional skill, but this invention is not particularly limited to this method.

The question bank creation module 250 is responsible for obtaining the skill practice information and physiological status data transmitted by the wearable device 400 received by the communication interface 130 when the interaction mode is the practice mode, and is responsible for determining whether the obtained skill practice information and/or physiological status data meet the assessment conditions. If the question bank creation module 250 determines that the assessment conditions are met, the question bank creation module 250 can record the interaction connection data before receiving the skill practice information in the practice assessment data loaded by the data loading module 210 into the assessment question bank to become the assessment data.

The assessment conditions proposed in the present invention can be the number of times the similarity between the hand movement data and body posture data in the practice assessment data obtained by the question bank creation module 250 and the hand movement signals and body posture information in the skill practice information falls below a similarity threshold and reaches a behavior threshold, or the number of times the amplitude of the change in the physiological state data obtained by the question bank creation module 250 reaches a change threshold and reaches a state threshold. The hand movement signals can represent hand movements of the user of the wearable device 400, and the body data information can represent the body posture of the user of the wearable device 400.

The interaction processing module 270 is responsible for obtaining the skill practice information transmitted by the wearable device 400 and received by the communication interface 130 when the interaction mode is the training mode, and is responsible for obtaining interaction connection data from the training assessment data loaded by the data loading module 210 based on the obtained skill practice information, and is responsible for transmitting the obtained interaction connection data to the wearable device 400 through the communication interface 130, so that the wearable device 400 displays real-world content corresponding to the user's hand signals and body postures in the skill demonstration scene based on the received interaction connection data.

The interaction processing module 270 can also determine the similarity between the hand movement signals and body posture information in the skill practice information and the hand movement data and body posture data in the practice assessment data when determining that the interaction mode is the practice mode. When it is determined that the similarity is lower than a predetermined value, the interaction processing module 270 can transmit the hand movement data and body posture data to the wearable device 400 through the communication interface 130, so that the wearable device 400 displays the hand movement data and body posture data to guide the user to perform appropriate movements.

The skill assessment module 290 is responsible for obtaining the skill practice information transmitted by the wearable device 400 received by the communication interface 130 when the interaction mode is the assessment mode, and is responsible for generating an assessment score based on the similarity between the hand movement signals and body posture information in the obtained skill practice information and the hand movement data and body posture data in the practice assessment data, and is responsible for transmitting the assessment score to the wearable device 400 for display through the communication interface 130 after the professional skill represented by the practice assessment data loaded by the data loading module 210 is completed.

The skill assessment module 290 may generate a corresponding practice score each time it receives a skill practice message, and may add up all practice scores after the exercise assessment data is completed to generate an assessment score. The skill assessment module 290 may also generate a corresponding practice score each time it receives a skill practice message, and may accumulate the generated practice scores, so that after the exercise assessment data is completed, the accumulated practice scores are used as the assessment scores. The skill assessment module 290 may also generate a practice score based on each skill practice message received after the exercise assessment data is completed, and accumulate the generated practice scores to generate a practice score. However, the manner in which the skill assessment module 290 generates assessment scores is not limited to the above.

Next, the operating system and method of the present invention will be explained using an embodiment. Please refer to FIG. 3 for a flow chart of the method for generating assessment content based on training status and physiological status. In this embodiment, it is assumed that device 100 is a reality server providing virtual reality services, but the present invention is not limited to this.

First, the reality server can provide a connection to the wearable device 400 (step 301). After the wearable device 400 is connected to the reality server, the reality server's data loading module 210 can load the skill reality data corresponding to the professional skills and read the scene data and exercise assessment data corresponding to the interaction mode from the skill reality data (step 310). In this embodiment, it is assumed that after the wearable device 400 is connected, the data loading module 210 can request the wearable device 400 to select a professional skill and interaction mode through the reality server's communication interface 130 and receive the professional skill and interaction mode selected by the wearable device 400.

When the interaction mode is the training mode, after the communication interface 130 of the real server transmits the training evaluation data to the wearable device 400, the wearable device 400 can display the skill demonstration scene according to the scene data in the received training evaluation data (step 320), so that the user of the wearable device 400 can start demonstrating professional skills in the skill demonstration scene.

Subsequently, wearable device 400 can generate a skill practice message containing the user's hand movement signals and body posture information based on the user's hand movements and body postures during the skill demonstration scenario, and can transmit the generated skill practice message to the real-world server (step 330). Simultaneously, wearable device 400 can also obtain the user's physiological status data and transmit the obtained physiological status data to the real-world server (step 340).

The communication interface 130 of the real server can receive the skill practice information and physiological status data transmitted by the wearable device 400, and the interaction processing module 270 of the real server can obtain the interaction connection data corresponding to the skill practice information received through the communication interface 130 from the practice assessment data loaded by the data loading module 210 of the real server, and transmit the extracted interaction connection data to the wearable device 400 through the communication interface 130 (step 350), so that the wearable device 400 can display the real content corresponding to the user's hand signals and body postures in the displayed skill demonstration scene based on the received interaction connection data (step 360).

Similarly, after the communication interface 130 of the real-world server receives the skill practice message and physiological status data transmitted by the wearable device 400, the question bank establishment module 250 of the real-world server can determine whether the skill practice message or physiological status data received by the communication interface 130 meets the assessment conditions. When the question bank establishment module determines that the received skill practice message or physiological status data meets the assessment conditions, the assessment data is recorded in the assessment question bank as the interaction connection data before the skill practice message is received (step 370).

When the interaction mode is the assessment mode, after the communication interface 130 of the real server transmits the exercise assessment data to the wearable device 400 (step 310), the wearable device 400 can display the skill demonstration scene according to the scene data in the received exercise assessment data (step 320), so that the user of the wearable device 400 can start demonstrating professional skills in the skill demonstration scene.

Afterwards, the wearable device 400 can generate a skill practice message including the user's hand movement signals and body posture information based on the hand movements and body postures of the user of the wearable device 400 demonstrating professional skills in the skill demonstration scene, and can transmit the generated skill practice message to the real server (step 330).

The communication interface 130 of the real server can receive the skill practice message transmitted by the wearable device 400, and the interaction processing module 270 of the real server can obtain the interaction connection data corresponding to the skill practice message received through the communication interface 130 from the practice assessment data loaded by the data loading module 210 of the real server, and transmit the retrieved interaction connection data to the wearable device 400 through the communication interface 130 (step 350), so that the wearable device 400 can display the real content corresponding to the user's hand signals and body postures in the displayed skill demonstration scene according to the received interaction connection data (step 360).

Similarly, after the communication interface 130 of the real server receives the skill practice message sent by the wearable device 400, as shown in the process of "Figure 3B", the skill assessment module 290 of the real server can generate an assessment score based on the similarity between the hand movement signal and body posture information in the skill practice message received by the communication interface 130 and the hand movement data and body posture data in the practice assessment data loaded by the data loading module 210 of the real server (step 381), and transmit the assessment score to the wearable device 400 after the practice assessment data is completed (step 383), so that the wearable device 400 displays the assessment score (step 385).

In summary, the difference between the present invention and the prior art is that the present invention has the function of reading the practice evaluation data in the practice mode, and obtaining the corresponding interactive connection data from the practice evaluation data according to the user's hand movements and body postures in the skill demonstration scene, so that the user can practice professional skills in the skill demonstration scene, and choose whether to establish the evaluation data according to the skill practice information and the user's physiological status data. The model reads the corresponding practice assessment data to provide users with a way to practice professional skills in a skill demonstration scenario. It also generates an assessment score based on the user's skill practice information such as hand movements and body posture in the skill demonstration scenario. This technical means can solve the problem of previous real-world technology that only scores based on the final results, thereby achieving the technical effectiveness of providing an effective assessment mechanism through real-world technology.

Furthermore, the method of the present invention for generating assessment content based on exercise status and physiological status can be implemented in hardware, software, or a combination of hardware and software. It can also be implemented in a centralized manner in a computer system or in a distributed manner with different components distributed across several interconnected computer systems.

While the embodiments disclosed above are limited to the present invention, these descriptions are not intended to directly limit the scope of patent protection for this invention. Any modifications or alterations in the form and details of the present invention made by a person skilled in the art without departing from the spirit and scope of this invention are within the scope of patent protection for this invention. The scope of patent protection for this invention shall remain subject to the scope of the attached patent application.

100: Device 110: Memory 130: Communication Interface 140: Storage Media 170: Processor 190: Bus 210: Data Loading Module 230: Data Generation Module 250: Question Bank Creation Module 270: Interaction Processing Module 290: Skill Assessment Module 400: Wearable Device Step 301: Reality Server Provides Connection to Wearable Device Step 310: The reality server loads the skill reality data corresponding to the professional skill and retrieves the scene data and the practice assessment data corresponding to the interaction mode from the skill reality data. Step 320: The wearable device displays the skill demonstration scene based on the scene data transmitted by the reality server. Step 330: The wearable device generates skill practice messages based on the hand movements and body posture of the wearable device user in the skill demonstration scene and transmits the skill practice messages to the reality server. Step 340: When the interaction mode is practice mode, the wearable device obtains the user's physiological status data and transmits the physiological status data to the reality server. Step 350: The real-world server retrieves interaction data corresponding to the skill practice information from the practice assessment data and transmits the retrieved interaction data to the wearable device. Step 360: Based on the interaction data, the wearable device displays real-world content corresponding to the user's hand signals and body postures in the skill demonstration scene. Step 370: When the interaction mode is set to practice mode and the real-world server determines that the skill practice message or physiological status data meets the assessment criteria, it records the assessment data in the assessment question bank as the interaction connection data obtained before receiving the skill practice message. Step 381: When the interaction mode is set to assessment mode, the real-world server generates an assessment score based on the similarity between the hand movement signals and body posture information in the skill practice message and the hand movement data and body posture data in the practice assessment data. Step 383: After the practice assessment data is completed, the real-world server transmits the assessment score to the wearable device. Step 385: The wearable device displays the assessment score.

Figure 1 is a schematic diagram of the components of the device for generating assessment content based on exercise status and physiological conditions according to the present invention. Figure 2 is a schematic diagram of the modules of the processor according to the present invention. Figure 3A is a flow chart of the method for generating assessment content based on exercise status and physiological conditions according to the present invention. Figure 3B is a flow chart of the method for generating assessment scores according to the present invention.

Step 301: The reality server provides a connection to the wearable device.

Step 310: The reality server loads the skill reality data corresponding to the professional skill and reads the scene data and the exercise assessment data corresponding to the interaction mode from the skill reality data.

Step 320: The wearable device displays the skill demonstration scene based on the scene data sent by the real-world server.

Step 330: The wearable device generates a skill practice message based on the wearable device user's hand movements and body posture in the skill demonstration scene and transmits the skill practice message to the reality server.

Step 340: When the interaction mode is training mode, the wearable device obtains the user's physiological status data and transmits the physiological status data to the real-world server.

Step 350: The real-world server retrieves the interaction connection data corresponding to the skill practice information from the practice assessment data and transmits the retrieved interaction connection data to the wearable device.

Step 360: The wearable device displays real-world content corresponding to the user's hand signals and body posture in the skill demonstration scene based on the interaction data.

Step 370: When the interaction mode is set to practice mode and the real-world server determines that the skill practice information or physiological status data meets the assessment criteria, it records the assessment data in the assessment question bank as the interaction connection data corresponding to the skill practice information.

Claims (10)

A method for generating assessment content based on practice conditions and physiological conditions, the method comprising at least the following steps: A reality server provides a connection to a wearable device; The reality server loads skill reality data corresponding to a professional skill, and reads scene data and practice assessment data corresponding to an interaction mode from the skill reality data, the practice assessment data comprising a plurality of interaction connection data, the interaction mode being either an assessment mode or a practice mode. When the interaction mode is the assessment mode, the practice assessment data comprises assessment data corresponding to the professional skill in an assessment question bank, the assessment data corresponding to at least one of the interaction connection data; The wearable device displays a skill demonstration scene based on the scene data transmitted by the reality server; The wearable device generates a skill practice message based on the hand movements and body posture of the user wearing the wearable device in the skill demonstration scenario and transmits the skill practice message to the real-world server. The skill practice message includes the user's hand movement signals and body posture information obtained by the wearable device. When the interaction mode is the practice mode, the wearable device obtains physiological status data of the user and transmits the physiological status data to the real-world server. The real-world server obtains the interaction connection data corresponding to the skill practice message from the practice assessment data and transmits the interaction connection data corresponding to the skill practice message to the wearable device. When the interaction mode is the practice mode and the real-world server determines that the skill practice message or the physiological status data meets the assessment criteria, it records the assessment data in the assessment question bank as other interaction connection data obtained before receiving the skill practice message. Based on the received interaction connection data, the wearable device displays real-world content corresponding to the user's hand signals and body postures in the skill demonstration scene. A method for generating assessment content based on practice conditions and physiological conditions as described in claim 1, wherein after the real-world server obtains the interaction continuation data from the practice assessment data based on the skill practice message and transmits the interaction continuation data to the wearable device, the method further includes the step of the real-world server, when the interaction mode is the assessment mode, generating an assessment score based on the similarity between the hand movement signal and the body posture information in the skill practice message and the hand movement data and body posture data in the practice assessment data, and transmitting the assessment score to the wearable device for display after the practice assessment data is completed. A method for generating assessment content based on practice conditions and physiological conditions as described in claim 1, wherein after the real-world server obtains the interaction continuation data from the practice assessment data based on the skill practice message and transmits the interaction continuation data to the wearable device, the method further includes the step of, when the real-world server determines that the interaction mode is the practice mode, determining the similarity between the hand movement signal and the body posture signal in the skill practice message and the hand movement data and body posture data in the practice assessment data, and when the similarity is lower than a predetermined value, transmitting the hand movement data and the body posture data to the wearable device, causing the wearable device to display the hand movement data and the body posture data to guide the user. A method for generating assessment content based on practice conditions and physiological conditions as described in claim 1, wherein the assessment conditions include the number of times the similarity between the hand movement data and the body posture data in the practice assessment data and the hand movement signals and the body posture information in the skill practice information is lower than a similarity threshold and reaches a behavior threshold, or the number of times the change amplitude of the physiological state data reaches a change threshold and reaches a state threshold. A method for generating assessment content based on practice conditions and physiological conditions as described in claim 1, wherein the step of the reality server reading the scene data and the exercise assessment data corresponding to the interaction mode from the skill reality data further includes the step of the reality server using a trained machine learning model to generate the exercise assessment data. A device that generates assessment content based on practice and physiological conditions, connected to a wearable device, comprises at least: A data loading module configured to load skill context data corresponding to a professional skill and retrieve scene data and practice assessment data corresponding to an interaction mode from the skill context data. The practice assessment data comprises a plurality of interaction connection data, wherein the interaction mode is either an assessment mode or a practice mode. When the interaction mode is the assessment mode, the practice assessment data comprises assessment data corresponding to the professional skill in an assessment question bank, and the assessment data corresponds to at least one piece of the interaction connection data. A communication interface is configured to transmit the scene data to the wearable device, causing the wearable device to display a skill demonstration scene based on the scene data, and to receive skill practice messages generated by the wearable device based on the user's hand movements and body postures in the skill demonstration scene. The communication interface also receives physiological status data of the user acquired by the wearable device when the interaction mode is the practice mode. The skill practice message includes the user's hand movement signals and body posture information acquired by the wearable device. A question bank creation module is configured to, when the interaction mode is the practice mode and the skill practice message or physiological status data is determined to meet the assessment criteria, record the assessment data in the assessment question bank as another interaction connection data acquired before the skill practice message was received. The interaction processing module is configured to obtain the interaction connection data corresponding to the skill practice message from the practice assessment data and transmit the interaction connection data corresponding to the skill practice message to the wearable device via the communication interface, so that the wearable device displays real-world content corresponding to the user's hand signals and body postures in the skill demonstration scene based on the received interaction connection data. A device for generating assessment content based on practice conditions and physiological conditions as described in claim 6, wherein the device further includes a skill assessment module for generating an assessment score based on the similarity between the hand movement signal and the body posture information in the skill practice message and the hand movement data and the body posture data in the practice assessment data when determining that the interaction mode is the assessment mode, and transmitting the assessment score to the wearable device for display via the communication interface after the practice assessment data is completed. As described in claim 6, the device for generating assessment content based on practice conditions and physiological conditions, wherein the interaction processing module is further used to determine that the interaction mode is the practice mode, determine the similarity between the hand movement signal and the body posture signal in the skill practice message and the hand movement data and body posture data in the practice assessment data, and when the similarity is lower than a predetermined value, transmit the hand movement data and the body posture data to the wearable device through the communication interface, so that the wearable device displays the hand movement data and the body posture data to guide the user. A device for generating assessment content based on practice conditions and physiological conditions as described in claim 6, wherein the assessment conditions include the number of times the similarity between the hand movement data and the body posture data in the practice assessment data and the hand movement signals and the body posture information in the skill practice information is lower than the similarity threshold value and reaches the behavior threshold value, or the number of times the change amplitude of the physiological state data reaches the change threshold value and reaches the state threshold value. A device for generating assessment content based on training conditions and physiological conditions as described in claim 6, wherein the device further includes a data generation module for training a machine learning model and using the trained machine learning model to generate the exercise assessment data.