Room cloud fitness method, system and device based on ar device and storage medium

Abstract

The application provides an AR device-based room cloud fitness method, system, device and storage medium, the method comprising: a trainer user wearing a first visual enhancement device, configuring a cloud trainer group, and matching an idle trainer when a guest user connects a mobile terminal to a second visual enhancement device in a room; a server respectively collecting a first motion image captured by the first mobile terminal and a second motion image captured by the second mobile terminal; performing recognition based on the first motion image, and sending at least an image representing the trainer user to the second visual enhancement device and synchronously displaying; the second visual enhancement device performing similarity matching on the motion postures of the first motion image and the second motion image, and superimposing the local first motion image of the unmatched part on the second motion image. The application can provide a room cloud fitness scene for each hotel room under the premise of basically not increasing the cost, and greatly enhances the user experience.

Description

Technical Field

[0001] This invention relates to the field of fitness interaction, and more specifically, to a method, system, device, and storage medium for cloud fitness in guest rooms based on AR devices. Background Technology

[0002] Although smart fitness mirrors and other devices already exist on the market, they are expensive. Simply equipping each room with them would greatly increase the hotel's operating costs, making them not commercially viable.

[0003] Furthermore, Augmented Reality (AR) technology is currently a technology that cleverly integrates virtual information with the real world. It widely uses various technologies such as multimedia, 3D modeling, real-time tracking and registration, intelligent interaction, and sensing to simulate and apply computer-generated virtual information such as text, images, 3D models, music, and videos to the real world. The two types of information complement each other, thereby achieving "enhancement" of the real world.

[0004] Therefore, the present invention provides a method, system, device and storage medium for cloud fitness in guest rooms based on AR devices. Summary of the Invention

[0005] To address the problems in the prior art, the present invention aims to provide a method, system, device, and storage medium for cloud fitness in hotel rooms based on AR devices. This overcomes the difficulties of the prior art and can provide a cloud fitness scenario in each hotel room without significantly increasing costs, thereby greatly enhancing the user experience.

[0006] An embodiment of the present invention provides a cloud-based fitness method for guest rooms based on AR devices, comprising the following steps:

[0007] Coach users wear primary vision enhancement devices and are configured with cloud coach teams. When guest users connect their mobile devices to secondary vision enhancement devices in their guest rooms, the server matches the guest users with an available coach.

[0008] The server respectively acquires a first motion image captured by a first mobile terminal connected to a first visual enhancement device through a first reflective surface and a second motion image captured by a second mobile terminal connected to a second visual enhancement device through a second reflective surface;

[0009] Based on the first motion image, at least one image representing the coach user is sent to the second visual enhancement device, and the first motion image and the second motion image are simultaneously displayed on the mobile terminal.

[0010] The second visual enhancement device performs similarity matching on the motion postures of the first motion image and the second motion image, and superimposes the local parts of the first motion image that do not match to the second motion image.

[0011] Preferably, the coach user wears a first visual enhancement device and is configured with a cloud coaching team. When the guest user connects their mobile terminal to a second visual enhancement device in the guest room, the server matches the guest user with an available coach, including:

[0012] Coaches wear first-person vision enhancement devices, and the server dynamically configures cloud coaching groups based on the real-time work status of coaches from several hotels.

[0013] When a guest connects their mobile device to the second visual enhancement device in the guest room, the mobile device is triggered to send a cloud fitness request to the server.

[0014] The server matches a guest user with an available coach.

[0015] Preferably, the server acquires a first motion image captured by a first mobile terminal connected to a first visual enhancement device through a first reflective surface and a second motion image captured by a second mobile terminal connected to a second visual enhancement device through a second reflective surface, including:

[0016] The front surface of the first visual enhancement device is provided with a first QR code. The first visual enhancement device of the coach user captures a first motion image with the first QR code through the first mirror in the gym.

[0017] The front surface of the second visual enhancement device is provided with a second QR code. The second visual enhancement device of the guest user takes a second moving image with the second QR code through the second reflective surface in the guest room. The second QR code has a mapping relationship with the guest room where the second visual enhancement device is located.

[0018] The server acquires the first motion image and the second motion image.

[0019] Preferably, the method further includes the following steps:

[0020] A service bill corresponding to the guest room is generated based on the second QR code.

[0021] Preferably, the step of recognizing the motion image based on the first motion image, at least sending an image representing the coach user to the second visual enhancement device, and simultaneously displaying the first motion image and the second motion image on the mobile terminal, includes:

[0022] Based on the first motion image, an image representing the coach user is obtained;

[0023] The server will at least send an image representing the coach user to the second visual enhancement device;

[0024] The first motion image and the second motion image are displayed simultaneously on the mobile terminal.

[0025] Preferably, the second visual enhancement device performs similarity matching on the motion poses of the first motion image and the second motion image, and superimposes the local portions of the first motion image that do not match onto the second motion image, including:

[0026] Using the MoveNet pre-trained model, 17 skeletal key points of the human body were extracted from the first motion image and the second motion image, respectively.

[0027] And establish a mapping relationship between the skeletal key points in the first motion image and the second motion image and the local first motion image and the local second motion image around the skeletal key points;

[0028] The coordinates of 17 skeletal key points in the first and second motion images are transformed into a coordinate system with one of the key points as the origin.

[0029] The coordinate-transformed key points of the 17 human skeletons were input into a multilayer perceptron for training and similarity matching.

[0030] The second motion image is obtained by superimposing the local first motion images corresponding to the mismatched parts of the key points of the human skeleton in the first motion image and the second motion image respectively.

[0031] Preferably, the first motion image is superimposed on the second motion image, which corresponds to the mismatched portion of the human skeleton key points in the first motion image and the second motion image respectively. The mismatched human skeleton key points in the first motion image are mapped to the same bone points in the second motion image. Then, the first motion image of the mismatched portion of the human skeleton key points is superimposed on the area of ​​the second motion image where the matching bone points are located.

[0032] Embodiments of the present invention also provide a guest room cloud fitness system based on AR devices, used to implement the above-described guest room cloud fitness method based on AR devices, wherein the guest room cloud fitness system based on AR devices includes:

[0033] The coach matching module allows coach users to configure a cloud coaching team by wearing a first visual enhancement device. When a guest user connects their mobile terminal to a second visual enhancement device in the guest room, the server matches the guest user with an available coach.

[0034] The motion image module, the server respectively acquires a first motion image captured by a first mobile terminal connected to a first visual enhancement device through a first reflective surface and a second motion image captured by a second mobile terminal connected to a second visual enhancement device through a second reflective surface;

[0035] The image recognition module identifies the motion image based on the first motion image, and at least sends the image representing the coach user to the second visual enhancement device, and simultaneously displays the first motion image and the second motion image on the mobile terminal.

[0036] The visual enhancement module, the second visual enhancement device, performs similarity matching on the motion postures of the first motion image and the second motion image, and superimposes the local parts of the first motion image that do not match to the second motion image.

[0037] Embodiments of the present invention also provide a cloud-based fitness device for guest rooms based on AR devices, comprising:

[0038] processor;

[0039] A memory in which executable instructions of the processor are stored;

[0040] The processor is configured to execute the steps of the AR-based cloud fitness method for guest rooms via executing the executable instructions.

[0041] Embodiments of the present invention also provide a computer-readable storage medium for storing a program that, when executed, implements the steps of the above-described AR-based cloud fitness method for guest rooms.

[0042] The purpose of this invention is to provide a method, system, device, and storage medium for cloud fitness in hotel rooms based on AR devices, which can provide a cloud fitness scene in each hotel room without increasing costs, greatly enhancing the user experience. Attached Figure Description

[0043] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0044] Figure 1 This is a flowchart of the AR-based cloud fitness method for guest rooms according to the present invention.

[0045] Figures 2 to 7 This is a schematic diagram illustrating the implementation process of the AR-based cloud fitness method for guest rooms according to the present invention.

[0046] Figure 8 This is a schematic diagram of the module of the AR-based cloud fitness system for guest rooms according to the present invention.

[0047] Figure 9 This is a schematic diagram of the structure of the AR-based cloud fitness device for guest rooms according to the present invention.

[0048] Figure 10 This is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present invention. Detailed Implementation

[0049] The following specific examples illustrate the implementation methods of this application. Those skilled in the art can easily understand the other advantages and effects of this application from the content disclosed herein. This application can also be implemented or applied through other different specific embodiments, and various details in this application can be modified or changed according to different viewpoints and application systems without departing from the spirit of this application. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0050] The embodiments of this application will now be described in detail with reference to the accompanying drawings, so that those skilled in the art can easily implement the application. This application may be embodied in many different forms and is not limited to the embodiments described herein.

[0051] In this application, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics represented in connection with that embodiment or example, which are included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics represented may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate different embodiments or examples represented in this application, as well as features of different embodiments or examples.

[0052] Furthermore, the terms "first" and "second" are used for illustrative purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the representation of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0053] For the purpose of clearly describing this application, devices that are not relevant to the description are omitted, and the same or similar components throughout the specification are given the same reference numerals.

[0054] Throughout this specification, when it is said that a device is "connected" to another device, this includes not only "direct connection" but also "indirect connection" by placing other components in between. Furthermore, when it is said that a device "comprises" a certain constituent element, unless otherwise stated otherwise, this does not exclude other constituent elements, but rather implies that other constituent elements may be included.

[0055] When we say that a device is "above" another device, this can mean that it is directly above the other device, or it can mean that other devices are present in between. Conversely, when we say that a device is "directly" "above" another device, there are no other devices present in between.

[0056] Although the terms first, second, etc., are used in some instances herein to refer to various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, first interface and second interface, etc., are used. Furthermore, as used herein, the singular forms “a,” “an,” and “the” are intended to also include the plural forms unless the context indicates otherwise. It should be further understood that the terms “comprising,” “including,” indicate the presence of features, steps, operations, elements, components, items, kinds, and / or groups, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are interpreted as inclusive, or mean any one or any combination thereof. Thus, “A, B, or C” or “A, B, and / or C” means “any one of: A; B; C; A and B; A and C; B and C; A, B, and C.” Exceptions to this definition will only occur if the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0057] The technical terms used herein are for reference only to specific embodiments and are not intended to limit the scope of this application. The singular form used herein includes the plural form unless the statement explicitly indicates otherwise. The word "comprising" as used in the specification means to specify a particular characteristic, region, integer, step, operation, element, and / or component, and does not exclude the presence or addition of other characteristics, regions, integers, steps, operations, elements, and / or components.

[0058] Although not explicitly defined, all terms, including technical and scientific terms used herein, shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. Terms defined in commonly used dictionaries shall be further interpreted as having a meaning consistent with the relevant technical literature and the content of this present application, and shall not be over-interpreted as having an ideal or overly formulaic meaning unless otherwise defined.

[0059] Figure 1This is a flowchart of the AR-based cloud fitness method for guest rooms according to the present invention. Figure 1 As shown, an embodiment of the present invention provides a cloud-based fitness method for guest rooms based on AR devices, comprising the following steps:

[0060] S110: When a coach user wears a first-vision augmentation device and is configured with a cloud coaching team, the server matches the guest user with an available coach when the guest user connects their mobile terminal to the second-vision augmentation device in the guest room.

[0061] S120, the server respectively acquires a first motion image captured by a first mobile terminal connected to the first visual enhancement device through a first reflective surface and a second motion image captured by a second mobile terminal connected to the second visual enhancement device through a second reflective surface;

[0062] S130: Recognize the first motion image, send at least the image representing the coach user to the second visual enhancement device, and simultaneously display the first motion image and the second motion image on the mobile terminal;

[0063] S140, the second visual enhancement device performs similarity matching on the motion postures of the first motion image and the second motion image, and superimposes the local parts of the first motion image that do not match to form the second motion image.

[0064] In a preferred embodiment, step S110 includes:

[0065] S111: Coach users wear first-person vision enhancement devices, and the server dynamically configures the real-time working status of coach users in several hotels into a cloud coaching group.

[0066] S112. When a guest connects their mobile terminal to the second visual enhancement device in the guest room, the mobile terminal is triggered to send a guest room cloud fitness request to the server.

[0067] S113, The server matches a guest user with an available coach.

[0068] In a preferred embodiment, step S120 includes:

[0069] S121. A first QR code is provided on the front surface of the first visual enhancement device. The first visual enhancement device of the coach user captures a first motion image with the first QR code through the first mirror in the gym.

[0070] S122. A second QR code is provided on the front surface of the second visual enhancement device. The second visual enhancement device of the guest user captures a second moving image with the second QR code through the second reflective surface in the guest room. The second QR code has a mapping relationship with the guest room where the second visual enhancement device is located.

[0071] S123, The server acquires the first motion image and the second motion image.

[0072] In a preferred embodiment, the following steps are also included:

[0073] S150. Generate the service bill for the corresponding room based on the second QR code.

[0074] In a preferred embodiment, step S130 includes:

[0075] S131. Recognize the first motion image to obtain an image representing the coach user;

[0076] S132, The server will send at least one image representing the coach user to the second visual enhancement device;

[0077] S133. Simultaneously display the first motion image and the second motion image on the mobile terminal.

[0078] In a preferred embodiment, step S140 includes:

[0079] S141. Using the MoveNet pre-trained model, extract 17 skeletal key points of the human body from the first motion image and the second motion image respectively.

[0080] S142, and establish the mapping relationship between the skeletal key points in the first motion image and the second motion image and the local first motion image and the local second motion image around the skeletal key points;

[0081] S143. Perform coordinate transformation on the 17 skeletal key points in the first motion image and the second motion image, that is, transform their coordinates to a coordinate system with a certain key point as the origin;

[0082] S144. Input the 17 human skeleton key points after coordinate transformation into a multilayer perceptron for training and similarity matching.

[0083] S145. The local first motion images of the mismatched parts of the human skeleton key points of the first motion image and the second motion image are superimposed to form the second motion image.

[0084] In a preferred embodiment, in step S145, the mismatched human skeletal key points in the first motion image are mapped to the same skeletal points in the second motion image, and then the local first motion image of the mismatched part of the human skeletal key points is superimposed to the area of ​​the local second motion image of the corresponding matching skeletal point in the second motion image.

[0085] Figures 2 to 7 This is a framework diagram of the AR-based cloud fitness method for guest rooms used for product testing according to the present invention. Figures 2 to 7The specific implementation process of the present invention is as follows:

[0086] like Figure 2 As shown, coach user 2 wears the first visual enhancement device 22. Server 3 dynamically configures a cloud coaching group based on the real-time working status of coach users 2, 41, and 42 from several hotels. When guest user 1 connects their mobile terminal 11 to the second visual enhancement device 12 in their room, the mobile terminal 11 sends a cloud fitness request to server 3. In this embodiment, server 3 matches guest user 1 with an available coach user 2. The second visual enhancement device 12 in this invention may not have components such as a camera, antenna, or power supply. Instead, the second visual enhancement device 12 interacts with the mobile terminal 11, utilizing the relevant components of the mobile terminal 11, thereby significantly reducing the overall cost of the second visual enhancement device 12 and facilitating its installation in all hotel rooms.

[0087] like Figure 3 As shown, the front surface of the first visual enhancement device 22 has a first QR code. Coach user 2 connects to the mobile terminal 21 of the first visual enhancement device 22 and takes a first motion image 24 with the first QR code through the first mirror 23 in the gym. The front surface of the second visual enhancement device 12 has a second QR code. The second visual enhancement device 12 of guest user 1 takes a second motion image 14 with the second QR code through the second mirror 13 in the guest room. The second QR code has a mapping relationship with the guest room where the second visual enhancement device 12 is located. Server 3 collects the first motion image 24 and the second motion image 14. Based on the first motion image 24, it identifies and obtains an image representing coach user 2. Server 3 sends at least the image representing coach user 2 to the second visual enhancement device 12. The first motion image 24 and the second motion image 14 are displayed synchronously on the mobile terminal 11. The first motion image 24 is an image of guest user 1 taken in real time by the mobile terminal 11 through the camera facing the mirror, while the image representing coach user 2 is added to the display screen of the mobile terminal 11 by the second visual enhancement device 12.

[0088] like Figure 4 , 5As shown in Figure 6, the MoveNet pre-trained model extracts 17 skeletal keypoints of the human body from the first motion image 24 and the second motion image 14, respectively. A mapping relationship is established between the skeletal keypoints in the first motion image 24 and the second motion image 14 and their surrounding local first motion images 24 and local second motion images 14. The coordinates of the 17 skeletal keypoints in the first motion image 24 and the second motion image 14 are transformed to a coordinate system with a keypoint as the origin. The 17 coordinate-transformed human skeletal keypoints are then input into a multilayer perceptron for training and similarity matching. MoveNet is a fast and accurate pose detection model capable of detecting 17 keypoints of the human body and running at 50+ fps on laptops and mobile phones. TFLite is a toolkit for deploying deep learning models on mobile and embedded devices. It can improve computing speed and reduce memory and GPU memory usage by transforming, deploying, and optimizing trained TF models.

[0089] This invention first utilizes the camera of an Android mobile device to acquire human posture video stream data. A human posture detection model detects 17 key skeletal points, which are then classified using a human posture classification model. Human motion data is acquired through sensors and transmitted to the Android device via a data transmission unit. On the Android device, the acquired human posture data is compared with the motion posture identified by the human posture recognition module, and precise position and angle prompts are provided for incorrect movements. Addressing the drawbacks of online video-based exercise training and online medical rehabilitation, which often involve incorrect or incomplete movements, this invention proposes a posture correction system based on human posture recognition. This system can collect human motion posture data in real time through sensors and transmit the data to an Android mobile device via Bluetooth. The data is then compared with the human posture constructed by the human posture recognition module to remind users to standardize and regulate their movements, thereby achieving better exercise and rehabilitation effects. This invention captures human posture data through sensors and compares it with the standard human posture constructed by the human posture recognition module. By providing prompts for incorrect movements in terms of body position and angle, the standardization of movement posture is achieved. This technical solution is implemented in the following manner:

[0090] (1) First, use the camera of the Android mobile device to acquire human posture video data, and then use the human posture recognition module of the Android mobile device to identify and classify the posture.

[0091] (2) Use the attitude acquisition unit to acquire motion data, that is, fix the attitude acquisition sensor to the corresponding part of the human body to acquire data;

[0092] (3) Use the data transmission unit to transmit the above action data to the Android mobile device;

[0093] (4) Determine whether the motion posture data obtained by the sensor matches the motion posture recognized by the human posture recognition module, and provide error prompts such as accurate position and angle through the prompt module and perform posture correction.

[0094] This invention provides a real-time human posture recognition module based on human posture detection and classification, suitable for Android devices. The invention implements a posture correction system based on human posture recognition. It collects data in real time through sensors and transmits it to an Android mobile device via Bluetooth. The system determines whether the human posture data acquired by the sensors matches the movement posture identified by the posture recognition module. An error prompting module provides precise position and angle error alerts for posture correction.

[0095] The first visual enhancement device 22 and the second visual enhancement device 12 mainly include a data acquisition unit, a data processing unit, a data transmission unit, and a human posture recognition module and a prompting module in an Android mobile device. This method first acquires video data of human posture through the camera of the Android device and uses it as input to the human posture recognition module to identify human movement postures. Simultaneously, it acquires human movement data through sensors and transmits it to the Android device via Bluetooth. The Android device matches and judges the movement postures identified by the human posture recognition module, and the prompting module provides error prompts regarding position and angle to correct postures. This system can achieve low-power, low-cost, and high-efficiency human posture recognition and correction. This system can be used to standardize movement postures such as squats and lunges.

[0096] This invention mainly realizes a posture correction system based on human posture recognition, such as Figure 1As shown, the main process is as follows: First, the MoveNet.TFLite model is used to detect human posture, identifying 17 skeletal key points. Then, a human posture classification model learns the coordinates of these 17 key points and classifies them to determine the category of the human movement posture. These 17 key points are, in order: nose, left eye, right eye, left ear, right ear, left shoulder, right shoulder, left elbow, right elbow, left wrist, right wrist, left hip, right hip, left knee, right knee, left ankle, and right ankle. Next, a data acquisition unit collects human motion posture data. This part mainly involves fixing two sensors to the lower leg and calculating angles using the data acquired by the two sensors. Then, a data transmission unit transmits the obtained data to an Android mobile device. This part mainly uses a BLE (Bluetooth Low Energy) Bluetooth module, which is characterized by low power consumption, low cost, high speed, and long range. Finally, the human movement posture is compared with the human posture identified by the human posture recognition module to determine if they match. A prompting module provides precise prompts regarding the position and angle of incorrect movements to correct the posture.

[0097] like Figure 7 As shown, the local first motion image 24 corresponding to the mismatched human skeletal key points (left arm region) of the first motion image 24 and the second motion image 14 are superimposed to form the second motion image 14. Based on the mapping of the mismatched human skeletal key points in the first motion image 24 to the same skeletal points in the second motion image 14, the local first motion image 31 (left arm region) of the mismatched human skeletal key points is then superimposed to the area of ​​the local second motion image 14 corresponding to the matching skeletal points. This provides a reminder for motion correction to guest user 1, enhancing the training effect and experience, allowing guest user 1 to receive training without leaving the room.

[0098] Finally, a service bill for the corresponding room is generated based on the second QR code.

[0099] Figure 8 Figure 8 is a schematic diagram of the AR-based cloud fitness system for guest rooms according to the present invention. As shown in Figure 8, an embodiment of the present invention also provides an AR-based cloud fitness system for guest rooms, used to implement the above-described AR-based cloud fitness method for guest rooms. The AR-based cloud fitness system for guest rooms includes:

[0100] Coach matching module 51: Coach users wear first visual enhancement devices to configure cloud coach groups. When guest users connect their mobile terminals to the second visual enhancement devices in the guest room, the server matches the guest users with an available coach.

[0101] The motion image module 52 and the server respectively acquire a first motion image captured by a first mobile terminal connected to the first visual enhancement device through a first reflective surface and a second motion image captured by a second mobile terminal connected to the second visual enhancement device through a second reflective surface;

[0102] Image recognition module 53 identifies the first motion image, sends at least the image representing the coach user to the second visual enhancement device, and displays the first motion image and the second motion image simultaneously on the mobile terminal;

[0103] The visual enhancement module 54, the second visual enhancement device, performs similarity matching on the motion postures of the first motion image and the second motion image, and superimposes the local parts of the first motion image that do not match to achieve the second motion image.

[0104] In a preferred embodiment, the coach matching module 51 is configured to have the coach user wear a first visual enhancement device, and the server dynamically configures the cloud coach group with the real-time working status of several hotel coach users; when the guest user connects the mobile terminal to the second visual enhancement device in the room, the mobile terminal is triggered to send a room cloud fitness request to the server; the server matches an available coach for the guest user.

[0105] In a preferred embodiment, the motion image module 52 is configured to have a first QR code on the front surface of the first visual enhancement device, and the first visual enhancement device of the coach user captures a first motion image with the first QR code through a first mirror in the gym; the front surface of the second visual enhancement device has a second QR code, and the second visual enhancement device of the guest user captures a second motion image with the second QR code through a second reflective surface in the guest room, and the second QR code has a mapping relationship with the guest room where the second visual enhancement device is located; the server collects the first motion image and the second motion image.

[0106] In a preferred embodiment, the method further includes the following step: generating a service bill for the corresponding guest room based on the second QR code.

[0107] In a preferred embodiment, the image recognition module 53 is configured to recognize the first motion image to obtain an image representing the coach user; the server sends at least the image representing the coach user to the second visual enhancement device; and the first motion image and the second motion image are displayed synchronously on the mobile terminal.

[0108] In a preferred embodiment, the visual enhancement module 54 is configured to extract 17 skeletal key points of the human body from the first motion image and the second motion image respectively using the MoveNet pre-trained model; and establish a mapping relationship between the skeletal key points in the first motion image and the second motion image and the local first motion image and local second motion image around the skeletal key points; perform coordinate transformation on the 17 skeletal key points in the first motion image and the second motion image, that is, transform their coordinates to a coordinate system with a certain key point as the origin; input the 17 human skeletal key points after coordinate transformation into a multilayer perceptron for training and similarity matching; and superimpose the local first motion images of the mismatched parts of the human skeletal key points in the first motion image and the second motion image to form the second motion image.

[0109] In a preferred embodiment, the mismatched human skeletal key points in the first motion image are mapped to the same skeletal points in the second motion image, and then the local first motion image of the mismatched portion of the human skeletal key points is superimposed onto the area of ​​the local second motion image of the corresponding matching skeletal point in the second motion image.

[0110] The purpose of this invention is to provide a cloud fitness system for hotel rooms based on AR devices, which can provide a cloud fitness scene in each hotel room without increasing costs, thus greatly enhancing the user experience.

[0111] This invention also provides an AR-based in-room cloud fitness device, including a processor and a memory storing executable instructions for the processor. The processor is configured to execute steps of an AR-based in-room cloud fitness method by executing the executable instructions.

[0112] As shown above, this embodiment of the AR-based guest room cloud fitness system of the present invention can provide a guest room cloud fitness scene for each hotel room without increasing costs, greatly enhancing the user experience.

[0113] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "platform."

[0114] Figure 9 This is a structural schematic diagram of the AR-based cloud fitness device for guest rooms according to the present invention. See below for reference. Figure 9 To describe an electronic device 600 according to this embodiment of the present invention. Figure 9The electronic device 600 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0115] like Figure 9 As shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0116] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the above-described section on the electronic prescription transfer processing method according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0117] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0118] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0119] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0120] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0121] This invention also provides a computer-readable storage medium for storing a program that, when executed, implements the steps of an AR-based cloud fitness method for hotel rooms. In some possible implementations, various aspects of the invention can also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the above-described electronic prescription processing method section of this specification according to various exemplary embodiments of the invention.

[0122] As shown above, this embodiment of the AR-based guest room cloud fitness system of the present invention can provide a guest room cloud fitness scene for each hotel room without increasing costs, greatly enhancing the user experience.

[0123] Figure 10 This is a schematic diagram of the structure of the computer-readable storage medium of the present invention. (Reference) Figure 10 As shown, a program product 800 for implementing the above-described method according to an embodiment of the present invention is described. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0124] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0125] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0126] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0127] In summary, the purpose of this invention is to provide a method, system, device, and storage medium for cloud fitness in hotel rooms based on AR devices, which can provide a cloud fitness scenario in each hotel room without significantly increasing costs, thereby greatly enhancing the user experience.

[0128] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A cloud-based fitness method for guest rooms based on AR devices, characterized in that, Includes the following steps: Coaches wear a first visual enhancement device, and the server dynamically configures cloud coaching groups based on the real-time work status of coaches from several hotels. When a guest connects their mobile terminal to a second visual enhancement device in their room, the mobile terminal sends a cloud fitness request to the server. The server then matches the guest with an available coach. The first visual enhancement device has a first QR code on its front surface. The first visual enhancement device for the coach user captures a first motion image with the first QR code through a first mirror in the gym. The second visual enhancement device has a second QR code on its front surface. The second visual enhancement device for the guest user captures a second motion image with the second QR code through a second reflective surface in the guest room. The second QR code is mapped to the guest room where the second visual enhancement device is located. The server collects the first motion image and the second motion image; it then identifies the image representing the coach user based on the first motion image. The server will at least send an image representing the coach user to the second visual enhancement device; The first motion image and the second motion image are displayed synchronously on the mobile terminal; 17 skeletal key points of the human body are extracted from the first motion image and the second motion image respectively using the MoveNet pre-trained model; a mapping relationship is established between the skeletal key points in the first motion image and the second motion image and the local first motion image and local second motion image around the skeletal key points; the coordinates of the 17 skeletal key points in the first motion image and the second motion image are transformed, that is, their coordinates are transformed into a coordinate system with a certain key point as the origin; the 17 human skeletal key points after coordinate transformation are input into a multilayer perceptron for training and similarity matching; the local first motion images of the first motion image and the second motion image corresponding to the mismatched parts of the human skeletal key points are superimposed to form the second motion image; and a service bill corresponding to the room is generated based on the second QR code.

2. The guest room cloud fitness method based on AR devices as described in claim 1, characterized in that, The process involves superimposing local first motion images corresponding to the mismatched portions of the human skeletal key points in the first motion image and the second motion image onto the second motion image, mapping the mismatched human skeletal key points in the first motion image to the same skeletal points in the second motion image, and then superimposing the local first motion images of the mismatched portions of the human skeletal key points onto the area of ​​the local second motion image corresponding to the matched skeletal points in the second motion image.

3. A guest room cloud fitness system based on AR devices, used to implement the guest room cloud fitness method based on AR devices as described in claim 1, characterized in that, include: The coach matching module involves coach users wearing first-person vision enhancement devices, and the server dynamically configures cloud coach groups based on the real-time work status of coach users from several hotels. When a guest connects their mobile device to the second visual enhancement device in their room, the mobile device sends a cloud fitness request to the server; the server then matches the guest with an available coach. The motion image module includes a first visual enhancement device with a first QR code on its front surface. The first visual enhancement device for the coach user captures a first motion image with the first QR code through a first mirror in the gym. The second visual enhancement device has a second QR code on its front surface. The second visual enhancement device for the guest user captures a second motion image with the second QR code through a second reflective surface in the guest room. The second QR code is mapped to the guest room where the second visual enhancement device is located. The server collects the first and second motion images. The image recognition module identifies the image based on the first motion image to obtain an image representing the coach user; The server will at least send an image representing the coach user to the second visual enhancement device; The first motion image and the second motion image are displayed simultaneously on the mobile terminal; The visual enhancement module utilizes the MoveNet pre-trained model to extract 17 skeletal key points of the human body from the first and second motion images, respectively; and establishes a mapping relationship between the skeletal key points in the first and second motion images and the surrounding local first and second motion images; it transforms the coordinates of the 17 skeletal key points in the first and second motion images, that is, transforms their coordinates to a coordinate system with a certain key point as the origin; it inputs the coordinate-transformed 17 human skeletal key points into a multilayer perceptron for training and similarity matching; it superimposes the local first motion images corresponding to the mismatched parts of the human skeletal key points in the first and second motion images to form the second motion image, and generates a service bill corresponding to the guest room based on the second QR code.

4. A cloud-based fitness device for guest rooms based on AR devices, characterized in that, include: processor; A memory in which executable instructions of the processor are stored; The processor is configured to perform the steps of the AR-based cloud fitness method for guest rooms according to claim 1 by executing the executable instructions.

5. A computer-readable storage medium for storing a program, characterized in that, When the program is executed by the processor, it implements the steps of the AR-based cloud fitness method for guest rooms as described in claim 1.

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