Device and method for analyzing yoga actions and physiological conditions to generate exercise plans

Abstract

A device and method for generating a practice plan by analyzing a user's image to generate initial posture data of a user, and determining an initial physical state of the user according to the initial posture data of the user and physiological state data collected synchronously with the user's image. And after a yoga action conforming to the initial body state is selected to generate a training plan, a technical means that a virtual coach performs action demonstration and key point description is simulated and displayed by using a reality technology according to demonstration action data of the yoga action in the training plan, so that real-time action guidance can be provided to enable a user to confirm whether the posture is correct or not; and the technical effect of providing an individual exercise plan is achieved.

Description

Technical Field

[0001] The invention discloses a device and method for generating a yoga exercise plan, and in particular, a device and method for generating an exercise plan by analyzing yoga movements and physiological conditions. Background Art

[0002] Yoga is a system that helps people realize their full potential by raising their consciousness. Yoga postures use ancient and easy-to-master techniques to improve people's physical, psychological, emotional and spiritual abilities. It is a form of exercise that achieves harmony between body, mind and spirit. What modern people call yoga is mainly a series of methods of self-cultivation and mind-nourishing, including postures for adjusting the body, breathing methods for regulating the breath, and meditation methods for regulating the mind.

[0003] Most of the current yoga courses provide teaching of postures (yoga movements), that is, they provide guidance on postures of various postures. Therefore, in addition to taking classes in yoga classrooms with yoga instructors, existing yoga courses can also be taught through videos. There are even auxiliary tools for yoga exercises.

[0004] However, most of the existing yoga auxiliary tools only provide static posture guidance, and there is no coach to give guidance based on the user's actual movements. As a result, the user cannot determine whether the posture is correct and may not achieve effective training results.

[0005] In summary, it can be seen that the prior art has long had the problem of yoga exercise auxiliary tools providing users with confirmation of whether their postures are correct, so it is necessary to propose improved technical means to solve this problem. Summary of the invention

[0006] In view of the problem that the prior art yoga exercise auxiliary tools provide users with confirmation of whether the posture is correct, the present invention discloses a device and method for analyzing yoga movements and physiological conditions to generate a practice plan, wherein:

[0007] The device disclosed in the present invention for analyzing yoga movements and physiological conditions to generate an exercise plan at least comprises: a data acquisition module for acquiring a user image and physiological state data collected synchronously with the user image; an image analysis module for analyzing the user image to generate initial posture data, the initial posture data comprising the positions and angles of multiple key parts of the user's body, and the initial posture data corresponding to the physiological state data; a state judgment module for judging the user's initial body state based on the initial posture data and the physiological state data; a plan generation module for selecting multiple yoga movements that match the initial body state to generate an exercise plan, the exercise plan comprising some yoga movements and the exercise time corresponding to each yoga movement; a data loading module for loading demonstration movement data corresponding to the current yoga movement; and a reality interaction module for simulating a virtual coach using reality technology, and simulating and displaying the virtual coach performing the current yoga movement and explaining the key points of the movement based on the demonstration movement data using reality technology.

[0008] The method disclosed in the present invention analyzes yoga movements and physiological conditions to generate an exercise plan, and its steps at least include: obtaining a user image and physiological status data collected synchronously with the user image; analyzing the user image to generate initial posture data, the initial posture data includes the positions and angles of multiple key parts of the user's body, and the initial posture data corresponds to the physiological status data; judging the user's initial body state based on the initial posture data and the physiological status data; selecting multiple yoga movements that match the initial body state to generate an exercise plan, the exercise plan includes multiple yoga movements and exercise times corresponding to each yoga movement; using reality technology to simulate a virtual coach; loading demonstration movement data corresponding to the current yoga movement; using reality technology to simulate and display the virtual coach performing the current yoga movement and explaining the key points of the movement based on the demonstration movement data.

[0009] The device and method disclosed in the present invention are as described above. The difference between the present invention and the prior art is that the present invention generates the user's initial posture data by analyzing the user's image, and determines the user's initial physical state based on the user's initial posture data and the physiological state data collected synchronously with the user's image, and selects yoga movements that match the initial physical state to generate a training plan. Then, based on the demonstration movement data of the yoga movements in the training plan, the present invention uses reality technology to simulate and display a virtual coach to demonstrate the movements and explain the key points, thereby solving the problems existing in the prior art and achieving the technical effect of providing real-time movement guidance and personal practice plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of the components of a device for analyzing yoga movements and physiological conditions to generate an exercise plan provided by the present invention.

[0011] Figure 2This is a module diagram of the processor provided by the present invention.

[0012] Figure 3A A flow chart of the method provided by the present invention for analyzing yoga movements and physiological conditions to generate an exercise plan.

[0013] Figure 3B This is a flow chart of the method for adjusting the exercise plan and providing action guidance provided by the present invention.

[0014] The following are the descriptions of the reference numerals:

[0015] 100: Installation

[0016] 110: Memory

[0017] 120: Camera

[0018] 130: Communication interface

[0019] 140: Storage Media

[0020] 150: Input element

[0021] 170: Processor

[0022] 190: Bus

[0023] 210: Data acquisition module

[0024] 220: Image analysis module

[0025] 240:Data loading module

[0026] 250: Status judgment module

[0027] 260: Plan generation module

[0028] 270:Action Guidance Module

[0029] 290: Reality Interaction Module

[0030] 400: Reality Device

[0031] Step 310: Obtain user images and synchronously collected physiological status data

[0032] Step 320: Analyze the user image to generate initial posture data corresponding to the physiological state data

[0033] Step 330: Determine the user's initial physical state based on the initial posture data and physiological state data

[0034] Step 340: Select yoga movements that match the initial physical state to generate a practice plan, which includes yoga movements and corresponding practice times.

[0035] Step 350: Use real-world technology to simulate a virtual coach

[0036] Step 361: Load the demonstration movement data corresponding to the current yoga movement

[0037] Step 365: Use the real-world technology to simulate and display the virtual coach performing the current yoga movements and explaining the key points of the movements based on the demonstration movement data

[0038] Step 371: Obtain the user's exercise images and physiological status data in real time

[0039] Step 375: Analyze the user's training image to generate training posture data

[0040] Step 381: Determine the degree of action completion based on the practice posture data and the standard posture data

[0041] Step 385: Generate guidance information when the difference between the practice posture data and the standard posture data reaches a threshold

[0042] Step 391: Use real-world technology to simulate a virtual coach to provide action guidance based on the guidance information

[0043] Step 395: Adjust the exercise plan based on the degree of movement completion and physiological status data DETAILED DESCRIPTION

[0044] The features and implementation methods of the present invention will be described in detail below with reference to the accompanying drawings and embodiments. The content is sufficient to enable any person skilled in the art 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 achievable by the present invention.

[0045] The present invention can generate a practice plan suitable for the user according to the user's posture and physiological state when performing yoga exercises, and provide movement guidance through reality technology when the user performs yoga exercises according to the practice plan. The reality technology mentioned in the present invention can be augmented reality or mixed reality, but the present invention is not limited thereto.

[0046] The apparatus for implementing the present invention may be a computing device. The computing device mentioned in the present invention includes but is not limited to hardware components such as one or more processing modules, one or more memory modules, and a bus connecting different hardware components (including memory modules and processing modules). Through the multiple hardware components included, the computing device can load and execute an operating system so that the operating system runs on the computing device, and can also execute software or programs. The computing device also includes a housing, and the above-mentioned hardware components are arranged in the housing.

[0047] The bus of the computing device mentioned in the present invention may include one or more types, such as a data bus, an address bus, a control bus, an expansion bus, and / or a local bus. The bus of the computing device includes, but is not limited to, an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, a Video Electronics Standards Association (VESA) local bus, a serial Universal Serial Bus (USB), a Peripheral Component Interconnect Express (PCI Express, PCI-E / PCIe) bus, and the like.

[0048] The processing module of the computing device mentioned 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 all or part of it can be 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, 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 operations or operations, such as mathematical operations, logical operations, data comparison, copying / moving data, etc., so as to drive other hardware components in the computing device or run an operating system or execute various programs and / or modules. The computer instructions may be assembly language instructions, instruction set architecture instructions, machine instructions, machine-dependent instructions, microinstructions, firmware instructions, or initial codes or object codes (Object Code) written in any combination of one or more programming languages, and the computer instructions may be executed completely on a single computing device, partially on a single computing device, 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 language or other similar programming languages.

[0049] The computing device also usually includes 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. That is, the memory controller and the peripheral input / output (I / O) controller can be included in one integrated circuit, or can be implemented using two or more integrated circuits. The chipset usually provides input / output and memory management functions, as well as multiple general and / or special registers, timers, etc., wherein the above-mentioned general and / or special 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.

[0050] The processing module of the computing device can also access the data in the memory module and the large-capacity storage area installed on the computing device through the memory controller. The above-mentioned memory module includes any type of volatile memory and / or non-volatile memory (NVRAM) memory, such as static random access memory (SRAM), dynamic random access memory (DRAM), read-only memory (ROM), flash memory, etc. The above-mentioned large-capacity storage area can include any type of storage device or storage medium, such as a hard disk drive, an optical disc, a flash drive, a memory card, a solid-state disk (SSD), or any other storage device. In other words, the memory controller can access the data in the static random access memory, dynamic random access memory, flash memory, hard disk drive, and solid-state disk.

[0051] The processing module of the computing device can also be connected and communicated with peripheral devices or interfaces such as peripheral output devices, peripheral input devices, communication interfaces, various data or signal receiving devices, etc. through the peripheral input and output controller via the peripheral input and output bus. The peripheral input device can be any type of input device, such as a keyboard, a mouse, a trackball, a touchpad, a joystick, etc. The peripheral output device can be any type of output device, such as a display, a printer, etc. The peripheral input device and the peripheral output device can also be the same device, such as a touch screen, etc. The communication interface can include a wireless communication interface and / or a wired communication interface. The wireless communication interface can include an interface that supports a wireless local area network (such as Wi-Fi, Zigbee, etc.), Bluetooth, infrared, near-field communication (NFC), 3G / 4G / 5G and other mobile communication networks (cellular networks) or other wireless data transmission protocols. The wired communication interface can 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, etc. The data or signal receiving device may include a GPS receiver or a physiological signal receiver, and the physiological signals received by the physiological signal receiver include but are not limited to heartbeat, blood oxygen, etc. The processing module may periodically poll various peripheral devices and interfaces, so that the computing device can input and output data through various peripheral devices and interfaces, and can also communicate with another computing device having the hardware elements described above.

[0052] The following first Figure 1 The present invention provides a schematic diagram of the components of the device for analyzing yoga movements and physiological conditions to generate an exercise plan to illustrate the device for implementing the present invention. Figure 1 As shown, the device 100 of the present invention includes a memory 110, a camera 120, a communication interface 130, a storage medium 140, an input element 150, a processor 170, and a bus 190. The processor 170 is connected to the memory 110, the camera 120, the communication interface 130, the storage medium 140, and the input element 150 via the bus 190.

[0053] Memory 110 may store one or more sets of computer instructions.

[0054] The camera 120 may include a circuit board, a lens assembly, and an image sensing element (all not shown in the figure), and the lens assembly and the image sensing element are connected through the circuit board. The camera 120 can obtain images through the lens assembly and the image sensing element. In the present invention, the device 100 is not limited to including one camera 120, and may also include multiple cameras 120 that synchronously obtain images.

[0055] The communication interface 130 can be connected to an external network storage device or a network device such as a server, and request and download data from the connected network device.

[0056] The storage medium 140 may store data or signals downloaded by the communication interface 130 , may store data or signals provided to the processor 170 or required by the processor 170 when operating, and may store data or signals generated by the processor 170 .

[0057] The input element 150 may provide input data through a peripheral input device of the device 100. For example, the input element 150 may input data through a keyboard, a mouse, a touch pad, or a touch screen.

[0058] The processor 170 may be configured as follows: Figure 2 As shown in the module diagram provided by the present invention, it includes modules such as data acquisition module 210, image analysis module 220, data loading module 240, state judgment module 250, plan generation module 260, reality interaction module 290, and may also include an additional action guidance module 270. In some embodiments, the processor 170 can execute the computer instructions stored in the memory 110, and can generate after executing the computer instructions. Figure 2 In each module; in another part of the embodiment, Figure 2 Each module in the above-mentioned can be generated by one or more circuits and / or complete or partial chips and other hardware components, that is, the processor 170 includes components Figure 2 That is, the modules included in the processor 170 may be software modules or hardware modules, and the present invention has no particular limitation.

[0059] The data acquisition module 210 is responsible for acquiring the user image. The data acquisition module 210 can acquire the user image through the camera 120, or provide the input element 150 to input the image storage path of the user image, and read the user image from the storage medium 140 according to the image storage path, or connect to the data server (not shown) through the communication interface 130 to download the user image.

[0060] The data acquisition module 210 is also responsible for acquiring physiological state data collected synchronously with the acquired user image. The physiological state data acquired by the data acquisition module 210 includes but is not limited to heart rate, respiratory rate, and blood oxygen concentration. Generally speaking, the physiological state data can be generated by the wearable device (such as a smart bracelet, a smart watch, a heart rate sensor, etc.) worn by the user to detect the user's physiological state, and the generated physiological state data can be directly transmitted to the device 100 for storage or transmitted to the real device 400 for transfer to the device 100. In this way, the data acquisition module 210 can receive the physiological state data transmitted by the wearable device (not shown in the figure) or the real device 400 through the communication interface 130, and can also read the pre-stored physiological state data from the storage medium 140 according to the file storage path input by the input element 150 or download the physiological state data from the data server through the communication interface 130, but the method for the data acquisition module 210 to obtain the physiological state data is not limited to the above.

[0061] The data acquisition module 210 can also acquire the user's training image in real time. Generally speaking, the data acquisition module 210 can acquire the user's training image through the camera 120 , or can receive the training image transmitted by the reality device 400 through the communication interface 130 .

[0062] The image analysis module 220 is responsible for analyzing the user image acquired by the data acquisition module 210 to generate initial posture data corresponding to the physiological state data. The initial posture data generated by the image analysis module 220 includes the positions and angles of multiple key parts of the user's body. The above-mentioned key parts include but are not limited to the top of the head, neck, shoulders, chest, elbows, hips, pelvic floor, knees, etc. The image analysis module 220 can analyze the user image through a deep learning model or computer vision technology. For example, if the user image contains depth data, the image analysis module 220 can use posture estimation models such as OpenPose, PostNet, HRNet, etc., to obtain the human body contour and local (such as head, limbs, etc.) features in the user image through a convolutional neural network (CNN) to generate a heat map (Heatmap) representing the probability of each pixel in the user image being in the key part and an offset map (Offset) representing the vector value of each pixel in the user image deviating from the key part The image analysis module 220 can detect the key parts of the user's body in the user image using the posture estimation model, and then calculate the position and angle of each key part in the space by using the triangulation method.

[0063] The image analysis module 220 can also analyze the training image obtained by the data acquisition module 210 to generate the training posture data. Generally speaking, the image analysis module 220 can use the same process as above to analyze the training image to generate the training posture data.

[0064] The image analysis module 220 can also determine the yoga action performed by the user in the user image based on the generated initial posture data, and can also determine the yoga action currently practiced by the user based on the generated practice posture data. For example, the image analysis module 220 can connect the key parts of the human body generated by the posture estimation model included in the initial posture data according to the predefined connection method of each key part to generate a human skeleton diagram, and can compare the similarity of the standard skeleton diagram of various yoga actions with the generated human skeleton diagram, so as to determine the yoga action corresponding to the initial posture data or the practice posture data, that is, to determine the yoga action performed by the user, but the present invention is not limited to this.

[0065] The data loading module 240 can load the standard posture data corresponding to the initial posture data generated by the image analysis module 220, for example, the standard posture data of the yoga action determined by the image analysis module 220 can be loaded from the storage medium 140 or connected to the data server through the communication interface 130; the data loading module 240 is also responsible for loading the demonstration action data corresponding to the current yoga action. The standard posture data mentioned in the present invention includes the position and relative distance of each joint of the body and the body angle of the corresponding yoga action under ideal conditions.

[0066] The state determination module 250 is responsible for determining the initial physical state of the user based on the initial posture data generated by the image analysis module 220 and the physiological state data obtained by the data acquisition module 210. The state determination module 250 can synchronize the initial posture data and the physiological state data corresponding to the time of the user image obtained by the data acquisition module 210, and determine the initial physical state of the user based on the synchronized initial posture data and the physiological state data.

[0067] More specifically, the state judgment module 250 can judge the degree of action completion based on the initial posture data generated by the image analysis module 220 and the standard posture data loaded by the data loading module 240, can also judge the degree of action fluency based on the initial posture data, and can judge the initial body state based on the determined degree of action completion and the degree of action fluency. For example, the state judgment module 250 can connect the specific key parts in the initial posture data to establish a skeleton based on the connection relationship of each key part in the standard posture data, and can calculate the position and angle of each key part in the initial posture data, and can compare the initial posture data with the position of each key part in the standard posture data to find out the deviation (such as the difference in angle or position). For example, the state judgment module 250 can judge whether the arm is stretched in place by whether the angle formed by the shoulder, elbow, and wrist in the initial posture data and the standard posture data has an angle deviation; for another example, the state judgment module 250 can calculate the distance and relative position between each key part in the initial posture data and the standard posture data to judge the position deviation. For example, in the downward dog pose, the initial posture data and the standard posture data can be checked to determine whether the hip joint is sufficiently extended upward, and the position of the hip joint and the key parts of the hands and feet (elbows, wrists, knees, ankles, etc.) can be compared to determine whether the action is in place. After that, the deviation value of each key part can be weighted to obtain a deviation score. In some embodiments, if certain key parts have a greater correlation with the completion of the action (such as the role of the hip joint in the downward dog pose), the deviation values ​​of these key parts will be given a higher weight. For example, for a standard downward dog pose, the angle of the hip joint, the angle between the arm and the ground, and the position of the heel are the main indicators. The accuracy of the action can be comprehensively evaluated based on the deviations of these indicators.

[0068] The state determination module 250 can also evaluate the user's fatigue status based on the changes in the action completion, action fluency, and physiological state data. For example, it can track whether the accuracy of action completion decreases as the practice time progresses to determine the fatigue accumulation.

[0069] The state judgment module 250 can also judge the action completion degree according to the practice posture data and the standard posture data analyzed by the image analysis module 220. For example, the state judgment module 250 can calculate the action completion degree according to the similarity between the standard skeleton diagram formed by connecting the key parts in the standard posture data and the human skeleton diagram formed by connecting the key parts in the practice posture data, and the difference between the relative distance and relative position between the key parts pre-defined for different yoga actions in the standard posture data and the relative distance and relative position between the key parts in the practice posture data, but the present invention is not limited thereto.

[0070] The plan generation module 260 is responsible for selecting a plurality of yoga movements that match the initial physical state to generate a practice plan, wherein the practice plan includes the selected yoga movements and the practice time corresponding to each selected yoga movement. Among them, beginners may obtain simpler variations, while advanced users will obtain a high-difficulty challenge.

[0071] The plan generation module 260 can adjust the exercise plan according to the action completion degree determined by the state determination module 250 and the physiological state data obtained by the data acquisition module 210. For example, if the heart rate and breathing rate are abnormally increased, the plan generation module 260 can extend the rest time or reduce the difficulty of the exercise action. At the same time, the plan generation module 260 can also gradually increase the difficulty of the action according to the user's progress. For example, if the user can stably complete all the actions of the existing plan, the plan generation module 260 can add new difficult actions or extend the practice time of certain actions.

[0072] The action guidance module 270 may generate guidance information when the difference between the practice posture data analyzed by the image analysis module 220 and the standard posture data reaches a threshold.

[0073] The reality interaction module 290 is responsible for simulating a virtual coach using reality technology, wherein the virtual coach can be a realistic character designed and produced using 3D modeling software or a cute Q-version character, and the present invention has no particular limitation.

[0074] The reality interaction module 290 is also responsible for using the same reality technology to simulate and display a virtual coach performing the current yoga action and explaining the key points of the yoga action based on the demonstration action data of the current yoga action. The reality interaction module 290 can provide the user with a change of viewing angle, so that the user can watch the details of the yoga action from different angles to learn the correct posture.

[0075] The reality interaction module 290 can also use the same reality technology to simulate a virtual coach to give action guidance to the user based on the guidance information generated by the action guidance module 270 .

[0076] Next, an embodiment is used to explain the system operation and method of the present invention, and please refer to Figure 3A Flow chart of the method for analyzing yoga movements and physiological conditions to generate an exercise plan provided by the present invention. In this embodiment, it is assumed that the device 100 is a real-world server that provides real-world device connections, but the present invention is not limited thereto.

[0077] The data acquisition module 210 of the device 100 can acquire the user image and the physiological state data collected synchronously with the acquired user image (step 310), and the image analysis module 220 of the device 100 can analyze the user image acquired by the data acquisition module 210 to generate initial posture data corresponding to the physiological state data acquired by the data acquisition module 210 (step 320). In this embodiment, it is assumed that the initial posture data includes the positions and angles of multiple key parts of the user's body.

[0078] After the image analysis module 220 of the device 100 generates the initial posture data, the state determination module 250 of the device 100 can determine the initial physical state of the user according to the initial posture data generated by the image analysis module 220 and the physiological state data obtained by the data acquisition module 210 (step 330). In this embodiment, it is assumed that the state determination module 250 can compare the posture data with the standard posture data read by the data loading module 240 to determine the initial physical state of the user.

[0079] After the state determination module 250 of the device 100 determines the initial physical state of the user, the plan generation module 260 of the device 100 can select multiple yoga movements that match the initial physical state and set the practice time of the selected yoga movements to generate a practice plan (step 340).

[0080] Afterwards, the reality interaction module 290 of the device 100 can simulate the virtual coach using reality technology (step 350). In this embodiment, it is assumed that the reality interaction module 290 can simulate and display the virtual coach on the display device using augmented reality.

[0081] At the same time, the reality interaction module 290 of the device 100 can also load the demonstration action data corresponding to the current yoga action (step 361), and can use reality technology to simulate and display the virtual coach performing the current yoga action and explaining the key points of the action based on the demonstration action data (step 365).

[0082] In this way, through the present invention, an exercise plan suitable for the user can be generated, and when the user performs yoga exercises according to the exercise plan, movement guidance can be given through real-life technology.

[0083] In the above embodiment, after the reality interaction module 290 of the device 100 uses the reality technology to simulate and display the virtual coach performing the current yoga action and explaining the key points of the action according to the demonstration action data (step 365), the user can also Figure 3B As shown in the process, the data acquisition module 210 of the device 100 can obtain the user's practice images and physiological state data in real time (step 371), the image analysis module 220 of the device 100 can analyze the user's practice images to generate practice posture data (step 375), the state judgment module 250 of the device 100 can judge the degree of action completion based on the practice posture data and the standard posture data (step 381), the action guidance module 270 of the device 100 can generate guidance information when it is judged that the difference between the practice posture data and the standard posture data reaches a threshold (step 385), the reality interaction module 290 of the device 100 can use reality technology to simulate a virtual coach to give action guidance based on the guidance information (step 391), and at the same time, the plan generation module 260 of the device 100 can adjust the exercise plan based on the action completion and physiological state data (step 395).

[0084] In summary, it can be seen that the difference between the present invention and the prior art lies in that the present invention has the technical means of analyzing the user's image to generate the user's initial posture data, judging the user's initial body state based on the user's initial posture data and the physiological state data collected synchronously with the user's image, and selecting the yoga movements that match the initial body state to generate a training plan, and then using reality technology to simulate and display the virtual coach to demonstrate the movements and explain the key points based on the demonstration movement data of the yoga movements in the training plan. This technical means can solve the problem that the prior art cannot provide the user with confirmation whether the posture is correct, thereby achieving the technical effect of providing real-time movement guidance and personal practice plans.

[0085] Furthermore, the method of analyzing yoga movements and physiological conditions to generate an exercise plan of the present invention 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 in which different components are distributed among several interconnected computer systems.

[0086] Although the embodiments disclosed in the present invention are as above, the contents described are not intended to directly limit the scope of patent protection of the present invention. Any ordinary technician in the field, without departing from the spirit and scope disclosed in the present invention, makes some modifications to the form and details of the implementation of the present invention, which are within the scope of patent protection of the present invention. The scope of patent protection of the present invention shall still be based on the scope defined by the attached claims.

Claims

1. A method for analyzing yoga movements and physiological conditions to generate a practice plan, which is applied to a device or system, characterized in that: The method comprises at least the following steps: Acquire a user image and physiological status data collected synchronously with the user image; Analyzing the user image to generate initial posture data, the initial posture data including positions and angles of a plurality of key parts of the user's body, and the initial posture data corresponding to the physiological state data; determining the initial physical state of the user according to the initial posture data and the physiological state data; Selecting a plurality of yoga movements that match the initial physical state to generate an exercise plan, wherein the exercise plan includes the plurality of yoga movements and exercise times corresponding to the respective yoga movements; Use reality technology to simulate virtual coaches; Loading demonstration movement data corresponding to the current yoga movement; and The real-world technology is used to simulate and display the virtual coach performing the current yoga movements and explaining the key points of the movements based on the demonstration movement data.

2. The method for analyzing yoga movements and physiological conditions to generate an exercise plan as claimed in claim 1, characterized in that: The method further includes the steps of obtaining and analyzing the user's practice images in real time to generate practice posture data, after using the real-life technology to simulate and display the virtual coach performing the current yoga movement and explaining the key points of the movement based on the demonstration movement data, and generating guidance information when the difference between the practice posture data and the standard posture data reaches a threshold, and using the real-life technology to simulate the virtual coach giving movement guidance based on the guidance information.

3. The method for analyzing yoga movements and physiological conditions to generate an exercise plan according to claim 1, wherein: After using the real-world technology to simulate and display the virtual coach performing the current yoga movement and explaining the key points of the movement based on the demonstration movement data, the method also includes the steps of obtaining the user's practice images and corresponding physiological status data in real time, analyzing the practice images to generate practice posture data, judging the degree of movement completion based on the practice posture data and the standard posture data, and adjusting the practice plan based on the degree of movement completion and the physiological status data.

4. The method for analyzing yoga movements and physiological conditions to generate an exercise plan according to claim 1, wherein: The step of obtaining the physiological state data collected synchronously with the user's image is to detect the user's physiological state through a wearable device to generate the physiological state data.

5. The method for analyzing yoga movements and physiological conditions to generate an exercise plan according to claim 1, wherein: The step of judging the initial physical state of the user based on the initial posture data and the physiological state data also includes judging the degree of movement completion based on the initial posture data and the standard posture data, judging the initial physical state based on the degree of movement completion and the movement fluency of the initial posture data, and evaluating the fatigue condition of the user based on changes in the degree of movement completion, the movement fluency and the physiological state data.

6. A device for analyzing yoga movements and physiological conditions to generate a practice plan, characterized in that: The device at least comprises: A data acquisition module, used to acquire a user image and physiological status data collected synchronously with the user image; An image analysis module, used for analyzing the user image to generate initial posture data, wherein the initial posture data includes positions and angles of a plurality of key parts of the user's body, and the initial posture data corresponds to the physiological state data; A state determination module, used for determining the initial physical state of the user according to the initial posture data and the physiological state data; a plan generating module, for selecting a plurality of yoga movements that match the initial physical state to generate an exercise plan, wherein the exercise plan includes the plurality of yoga movements and the exercise time corresponding to each of the yoga movements; A data loading module, used for loading demonstration movement data corresponding to the current yoga movement; and The reality interaction module is used to use the reality technology to simulate the virtual coach, and use the reality technology to simulate and display the virtual coach performing the current yoga movements and explaining the key points of the movements based on the demonstration movement data.

7. The device for analyzing yoga movements and physiological conditions to generate an exercise plan as claimed in claim 6, characterized in that: The device also includes an action guidance module, which is used to generate guidance information when the difference between the practice posture data generated by the practice image acquired in real time by the data acquisition module when the image analysis module analyzes the practice posture data and the standard posture data reaches a threshold value. The reality interaction module is also used to use the reality technology to simulate the virtual coach to give action guidance based on the guidance information.

8. The device for analyzing yoga movements and physiological conditions to generate an exercise plan as claimed in claim 6, characterized in that: The state judgment module is also used to judge the degree of action completion based on the exercise posture data and standard posture data generated by the exercise image of the user obtained in real time by the data acquisition module through analysis by the image analysis module. The plan generation module is also used to adjust the exercise plan based on the degree of action completion and the physiological state data obtained by the data acquisition module.

9. The device for analyzing yoga movements and physiological conditions to generate an exercise plan as claimed in claim 6, characterized in that: The data acquisition module acquires the physiological state data generated by the wearable device detecting the physiological state of the user.

10. The device for analyzing yoga movements and physiological conditions to generate an exercise plan as claimed in claim 6, characterized in that: The state judgment module judges the degree of action completion based on the initial posture data and the standard posture data, judges the initial body state based on the degree of action completion and the movement fluency of the initial posture data, and evaluates the user's fatigue state based on changes in the degree of action completion, the movement fluency and the physiological state data.

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