A somatosensory motor training method and system
By combining a cloud platform and an IPTV client in an IPTV network, the motion-sensing training system solves the latency and stability problems of remote motion-sensing training, achieves efficient motion scoring and display, and improves the user experience.
Patent Information
- Application Number
- CN202211531421.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-12-01
- Publication Date
- 2025-10-31
- Estimated Expiration
- 2042-12-01
AI Technical Summary
Existing motion-sensing training systems suffer from low latency, poor latency performance, and insufficient stability in remote environments, resulting in a poor user experience and an inability to achieve high standards of motion learning and training assessment.
Using IPTV networks for motion-sensing training, combined with cloud platform and IPTV client, it collects motion signals, identifies quasi-movement and secondary movement periods, generates video streams, and evaluates movement ratings based on the absolute value of position parameter differences, achieving low-latency and high-stability training, while also providing large-size display and scoring mechanisms.
It achieves low-latency and high-stability cloud-based motion-sensing training, ensuring the accuracy and reliability of scoring and improving the user experience, especially in scenarios with high requirements for sports training standards and assessment accuracy.
Smart Images
Figure CN115779394B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of motion sensing technology, and in particular to a motion sensing training method and system. Background Technology
[0002] Motion-sensing is a form of exercise that uses motion-sensing devices to collect motion signals from users' movements and guide them through corresponding exercises. However, some motion-sensing technologies use local software and lack multi-user, remote interaction capabilities; others use public networks (the Internet) for communication, resulting in slow response times, poor latency, and insufficient stability. This makes it difficult to ensure the proper functioning of remote motion-sensing exercises, severely impacting user experience and hindering the achievement of high-standard motion-sensing learning and training. Consequently, it becomes impossible to scientifically and accurately evaluate the training results. Summary of the Invention
[0003] To address at least one problem existing in the prior art, the purpose of this application is to provide a motion-sensing training method and system that not only enables low-latency and high-stability cloud-based motion-sensing training, but also implements a recognition strategy and scoring mechanism that takes into account rhythmic defects, thereby ensuring the reliability of cloud-based motion-sensing training and the accuracy of scoring. In addition, it can provide a larger display size, which greatly improves the user experience. The effect is particularly obvious for motion-sensing training with high requirements for training standards and high accuracy of assessment.
[0004] To achieve the above objectives, the motion-sensing training method provided in this application is applied to a motion-sensing training system, the system comprising a cloud platform for motion-sensing training, at least two IPTV clients, and at least two motion-sensing devices corresponding to the IPTV clients; the at least two IPTV clients include one teacher client and at least one student client; on the cloud platform, the method comprises:
[0005] Receive the first motion-sensing exercise training start command sent by the student client;
[0006] Based on the first somatosensory motion program, at least one target somatosensory signal in the first somatosensory motion is determined, at least one quasi-action period that generates the at least one target somatosensory signal is determined, and a secondary action period within a first duration before and after the quasi-action period is determined.
[0007] The system receives at least one training somatosensory signal corresponding to the at least one target somatosensory signal sent by the student client; performs response processing based on the at least one training somatosensory signal to generate a video stream, and sends it to a first IPTV client for playback on the IPTV corresponding to the first IPTV client; the first IPTV client includes a corresponding student client, or includes the corresponding student client and the teacher client;
[0008] Obtain the absolute value of the difference between the position parameters of each training somatosensory signal and the corresponding target somatosensory signal;
[0009] In response to the absolute value of the difference being less than or equal to a first threshold, and the training somatosensory signal being in the corresponding quasi-movement period, the movement rating is determined to be excellent; in response to the absolute value of the difference being less than or equal to the first threshold, and the training somatosensory signal being in the corresponding sub-movement period, the movement rating is determined to be beat downgraded.
[0010] Determine the excellence rate and rhythm degradation rate of all motion-sensing movements in this first motion-sensing exercise; determine the training score of each student client for this first motion-sensing exercise based on the excellence rate and the rhythm degradation rate;
[0011] The training score is sent to a second IPTV client so that it can be displayed on the IPTV corresponding to the second IPTV client; the second IPTV client includes the teacher client, or includes the teacher client and the corresponding student client.
[0012] Furthermore, the training score is determined in the following manner:
[0013]
[0014] Wherein, S is the training score; A is the excellence rate; B is the beat degradation rate; t1 is the duration of the quasi-action period; and t2 is the duration of the sub-action period.
[0015] Furthermore, prior to the step of receiving the training start command for the first somatosensory movement, the method further includes:
[0016] Receive the teaching start command sent by the teacher's client;
[0017] Receive first somatosensory motion information sent by the teacher client, the first somatosensory motion information including the at least one target somatosensory signal and its corresponding time point, the first duration, and the first threshold;
[0018] Based on the first somatosensory motion information, the first somatosensory motion program is generated.
[0019] Furthermore, the method further includes sending at least one of the excellence rate, the beat degradation rate, the motion information corresponding to the excellence level, and the motion information corresponding to the beat degradation level to the second IPTV client, so that it can be displayed on the IPTV corresponding to the second IPTV client.
[0020] Furthermore, the method also includes: after determining the training score of the first somatosensory movement for each student client, storing the identification information of the target somatosensory signal of the beat downgrade movement in this training in the rating history of the first somatosensory movement of the corresponding student client.
[0021] Furthermore, before receiving the at least one training somatosensory signal sent by the student client, the method further includes:
[0022] Retrieve the rating history of the first motion-sensing exercise from the corresponding student's client.
[0023] Based on the identification information in the rating history, the target somatosensory signal of the beat downgrade action in the previous first somatosensory movement is determined, and corresponding beat reminder information is generated;
[0024] During the quasi-action period and the secondary action period of the target somatosensory signal of the beat degradation movement, the corresponding beat reminder information is sent to the training screen of the corresponding student client.
[0025] Furthermore, the IPTV client is multiple, including at least two student clients; the method further includes:
[0026] Receive the composite image command sent by the teacher's client;
[0027] According to the composite screen instruction, the training screens of the at least two student clients after the response processing are composited.
[0028] A synthesized video stream is generated based on the synthesized training footage;
[0029] The synthesized video stream is sent to the teacher's client and the at least two student clients.
[0030] Furthermore, the IPTV client is multiple, including at least two student clients; the method further includes:
[0031] Receive the composite screen command sent by the at least two student clients;
[0032] According to the composite screen instruction, the training screens of the at least two student clients after the response processing are composited.
[0033] A synthesized video stream is generated based on the synthesized training footage;
[0034] The synthesized video stream is sent to the at least two student clients.
[0035] To achieve the above objectives, this application also provides a motion-sensing training system, comprising a cloud platform for motion-sensing training, at least two IPTV clients, and at least two motion-sensing devices corresponding to the IPTV clients; the at least two IPTV clients include one teacher client and at least one student client; wherein,
[0036] The at least two motion-sensing devices are used to collect training motion-sensing signals and send the training motion-sensing signals to the corresponding IPTV client;
[0037] The at least one student client is used to receive the training start command and training motion signal of the first motion sensing movement, and send them to the cloud platform.
[0038] The cloud platform is used for:
[0039] Receive the training start command sent by the student client;
[0040] Based on the first somatosensory motion program, at least one target somatosensory signal in the first somatosensory motion is determined, at least one quasi-action period that generates the at least one target somatosensory signal is determined, and a secondary action period within a first duration before and after the quasi-action period is determined.
[0041] The system receives at least one training somatosensory signal corresponding to the at least one target somatosensory signal sent by the student client; performs response processing based on the at least one training somatosensory signal to generate a video stream; and sends the video stream to the at least one student client for playback on the IPTV corresponding to the at least one student client.
[0042] Obtain the absolute value of the difference between the position parameters of each training somatosensory signal and the corresponding target somatosensory signal;
[0043] In response to the absolute value of the difference being less than or equal to a first threshold, and the training somatosensory signal being in the corresponding quasi-movement period, the movement rating is determined to be excellent; in response to the absolute value of the difference being less than or equal to the first threshold, and the training somatosensory signal being in the corresponding sub-movement period, the movement rating is determined to be beat downgraded.
[0044] Determine the excellence rate and rhythm degradation rate of all motion-sensing movements in this first motion-sensing exercise; determine the training score of each student client for this first motion-sensing exercise based on the excellence rate and the rhythm degradation rate;
[0045] The training score is sent to the teacher's client so that it can be displayed on the IPTV corresponding to the teacher's client.
[0046] The teacher's client is used to receive the training scores and display them on the corresponding IPTV.
[0047] The at least one student client is also configured to receive the video stream for playback on the corresponding IPTV.
[0048] The motion-sensing training method and system disclosed in this application not only enable low-latency and high-stability cloud-based motion-sensing training, but also implement a recognition strategy and scoring mechanism that takes into account rhythmic defects, thereby ensuring the reliability of cloud-based motion-sensing training and the accuracy of scoring. In addition, it can provide a larger display size, which greatly improves the user experience. The effect is particularly obvious for motion-sensing training with high requirements for training standards and assessment accuracy.
[0049] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application. Attached Figure Description
[0050] The accompanying drawings are provided to further illustrate the present application and form part of the specification. Together with the embodiments of the present application, they serve to explain the present application but do not constitute a limitation thereof. In the drawings:
[0051] Figure 1 This is a structural block diagram of a motion-sensing training system according to an embodiment of this application;
[0052] Figure 2 This is a flowchart of a motion-sensing training method according to an embodiment of this application;
[0053] Figure 3 This is a flowchart illustrating the method for receiving a training start command according to an embodiment of this application.
[0054] Figure 4 This is a flowchart of a beat reminder method according to an embodiment of this application. Detailed Implementation
[0055] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.
[0056] It should be understood that the steps described in the method embodiments of this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.
[0057] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.
[0058] It should be noted that the concepts of "first" and "second" mentioned in this application are used only to distinguish different devices, modules, units, data or categories, and are not used to limit the order or interdependence of the functions performed by these devices, modules, units, data or categories.
[0059] It should be noted that the terms "one" and "multiple" used in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless explicitly stated otherwise in the context, they should be understood as "one or more". "Multiple" should be understood as two or more.
[0060] The embodiments of this application will now be described in detail with reference to the accompanying drawings.
[0061] First, it should be noted that some related motion-sensing technologies use public network communication, resulting in slow response, poor low-latency performance, and insufficient stability. This makes it difficult to ensure the normal operation of remote motion-sensing, seriously affecting the user experience, and making it impossible to achieve high-standard motion-sensing learning and training, nor to scientifically and accurately evaluate the results of motion-sensing training.
[0062] Based on this, this application proposes a motion-sensing training method for IPTV (Internet Protocol Television, interactive network television). IPTV is a technology that utilizes broadband networks, integrating Internet, multimedia, and communication technologies to provide home users with various interactive services, including digital television. On the one hand, compared to the public network, the response speed of the IPTV intranet can be improved by more than 20%, and it has fewer nodes and a dedicated network, thus possessing the characteristics of high response, low latency, and high stability. On the other hand, compared to conventional mobile terminals or desktop computers, IPTV has the advantage of large-screen display. Currently, there is no motion-sensing training method applied to IPTV, let alone a motion-sensing training method with a highly accurate assessment strategy applied to IPTV.
[0063] It should be noted that the motion-sensing training method of this application is applied to a motion-sensing training system. Figure 1 This is a structural block diagram of a motion-sensing training system according to an embodiment of this application. (Reference) Figure 1 As shown, the motion-sensing training system 10 includes a cloud platform 11 for motion-sensing training, at least two IPTV clients 12, and at least two motion-sensing devices 13 corresponding to the IPTV clients 12. The at least two IPTV clients 12 are connected to the cloud platform 11 via an IPTV network. The at least two IPTV clients 12 include a teacher client 121 and at least one student client 122, wherein at least one student client 121 sends a first motion-sensing training start command to the cloud platform 11.
[0064] It is understood that in this embodiment of the application, the IPTV client 12 can be configured in IPTV or in the IPTV set-top box, and this application does not impose any specific restrictions on this.
[0065] Example 1
[0066] Figure 2 The flowchart of the motion-sensing training method according to an embodiment of this application will be referenced below. Figure 2 This application provides a detailed description of the motion-sensing training method. On the cloud platform, the method includes the following steps:
[0067] Step 201: Receive the first motion-sensing training start command sent by the student client.
[0068] It should be noted that motion-sensing exercise can include motion-sensing dance, motion-sensing gymnastics, motion-sensing fitness exercises, motion-sensing Tai Chi, etc. In essence, any exercise method that uses motion-sensing devices to collect motion signals from the user's movements to guide the user in completing the corresponding exercise can be considered motion-sensing exercise, and this application does not impose specific limitations on this. The "first motion-sensing exercise" here refers to a specific motion-sensing exercise, such as a specific set of motion-sensing fitness exercises.
[0069] Specifically, when a student user wants to start the first motion-sensing exercise, they can send a training start command from their corresponding student client to the cloud platform via the IPTV network. Upon receiving the training start command, the cloud platform will initiate the first motion-sensing exercise.
[0070] Step 202: Based on the first somatosensory motion program, determine at least one target somatosensory signal in the first somatosensory motion, at least one quasi-action period that generates at least one target somatosensory signal, and a secondary action period within a first duration before and after the quasi-action period.
[0071] Specifically, after starting the first somatosensory exercise, at least one target somatosensory signal for the somatosensory exercise training can be obtained from the first somatosensory exercise program, as well as the quasi-action period and sub-action period corresponding to each target somatosensory signal.
[0072] In this context, at least one target somatosensory signal refers to the somatosensory signal corresponding to all target movements in the first somatosensory exercise, which may include the coordinate parameters of the target movements, and can be input through the teacher's client. The quasi-movement period refers to the optimal somatosensory movement achievement period set in the first somatosensory exercise program. In a specific example, only when the limb is within the coordinate area corresponding to the target somatosensory signal during the quasi-movement period can the full score corresponding to that movement be obtained. The secondary movement period refers to the period during which the corresponding somatosensory movement is performed faster (within the first duration) or slower (within the first duration). In a specific example, the first duration can be input by the teacher's client or automatically generated based on parameters such as the rhythm of the first somatosensory exercise; during the secondary movement period, completing the corresponding somatosensory movement can obtain a partial score corresponding to that movement. For example, the quasi-movement period for one target somatosensory movement is from the 5th to the 6th second, the first duration is 0.5 seconds, and the secondary movement periods are from the 4.5th to the 5th second and from the 6th to the 6.5th second.
[0073] Step 203: Receive at least one training somatosensory signal sent by the student client, which corresponds to at least one target somatosensory signal; perform response processing based on at least one training somatosensory signal to generate a video stream, and send it to the first IPTV client so that it can be played on the IPTV corresponding to the first IPTV client.
[0074] Specifically, the motion-sensing device captures the student user's movements, generates training motion-sensing signals, and sends them to the corresponding student client. The student client then transmits the received training motion-sensing signals to the cloud platform. The cloud platform processes the training motion-sensing signals and generates a video stream, which is then sent to the first IPTV client. The first IPTV client can be the corresponding student client, allowing the student performing the first motion-sensing exercise to view their own training process. Alternatively, the first IPTV client can be both a student client and a teacher client, allowing teachers to simultaneously view the training process, simulating a training scenario and improving the user experience.
[0075] It should be noted that response processing can include responding to training motion signals and cloud rendering of training images. Specifically, it can involve responding to the received training motion signals based on the target motion signal, the quasi-response period, and the secondary response period, and then performing cloud rendering on the training images that have responded to the training motion signals.
[0076] Step 204: Obtain the absolute value of the difference between the position parameters of each training somatosensory signal and the corresponding target somatosensory signal.
[0077] Specifically, the position parameters of the target somatosensory signal can be the three-dimensional coordinates T(x1, y1, z1) of the target somatosensory action in the corresponding coordinate system, and the position parameters of the training somatosensory signal can be the three-dimensional coordinates S(x2, y2, z2) of the student user's training somatosensory action. The absolute values of the differences between the position parameters can be |x2-x1|, |y2-y1|, and |z2-z1|.
[0078] It is understood that the position parameters of the aforementioned somatosensory signals can be three-dimensional coordinates or coordinates of other dimensions, and this application does not impose any specific restrictions on them.
[0079] Step 205: In response to the absolute value of the difference being less than or equal to the first threshold and the training somatosensory signal being in the corresponding quasi-movement period, the movement rating is determined to be excellent; in response to the absolute value of the difference being less than or equal to the first threshold and the training somatosensory signal being in the corresponding sub-movement period, the movement rating is determined to be downgraded to beat.
[0080] In other words, when |x2-x1|≤D1, |y2-y1|≤D2, and |z2-z1|≤D3, meaning the movement meets the standard: if the training somatosensory signal beat also meets the standard, the corresponding movement is rated as excellent; if the training somatosensory signal beat is too fast or too slow, the corresponding movement is rated as beat-down. Therefore, by employing a low-latency, high-response communication method combined with a specialized strategy, it is possible not only to identify standard movements but also movements with poor beat quality.
[0081] It is understood that the first thresholds D1, D2 and D3 corresponding to different dimensional coordinates can be the same or different, and this application does not impose specific restrictions on this.
[0082] Step 206: Determine the excellence rate and rhythm downgrade rate of all motion-sensing movements in this first motion-sensing exercise; determine the training score of the first motion-sensing exercise on the student's client based on the excellence rate and rhythm downgrade rate.
[0083] Specifically, the success rate of motion-sensing movements can be determined in the following ways:
[0084] A = C A / C (1)
[0085] Where A represents the percentage of excellent students, and C represents the percentage of excellent students. A C represents the number of training somatosensory signals that received an excellent rating in this first somatosensory exercise, and C represents the total number of target somatosensory signals in this first somatosensory exercise.
[0086] The beat degradation rate of kinetic motion can be determined in the following ways:
[0087] B = C B / C (2)
[0088] Where B is the beat degradation rate, C B C represents the number of training somatosensory signals that received a beat downgrade rating in this first somatosensory exercise, and C represents the total number of target somatosensory signals in this first somatosensory exercise.
[0089] In this embodiment of the application, the training score can be determined in the following way:
[0090]
[0091] Where S is the training score; A is the excellence rate; B is the beat downgrade rate; t1 is the duration of the quasi-movement period; and t2 is the duration of the sub-movement period. In a specific example, when t1 = 0.8s, t2 = 0.5s, A = 50%, and B = 40%, S = 67.8 can be obtained through formula (3). From this formula, it can be seen that when the beat downgrade rate B is constant, the larger the ratio of the duration of the sub-movement period t2 to the duration of the quasi-movement period t1, the lower the score of the beat downgraded movement; the smaller the ratio of the duration of the sub-movement period t2 to the duration of the quasi-movement period t1, the higher the score of the beat downgraded movement. When t2 is infinitely close to zero, the score of a beat downgraded movement is infinitely close to the score of an excellent movement.
[0092] Step 207: Send the training score to the second IPTV client so that it can be displayed on the corresponding IPTV on the second IPTV client.
[0093] The second IPTV client includes a teacher's client, or a teacher's client and a corresponding student's client. That is, after a student completes the first motion-sensing exercise, the cloud platform calculates the training score using the above method and sends it to the teacher's client. Alternatively, it can send the score not only to the teacher's client but also to the student's client that sent the training start command, allowing the training score to be displayed on the corresponding client's IPTV.
[0094] In this embodiment of the application, the method further includes: sending at least one of the excellent rate, the beat degradation rate, the motion information corresponding to the excellent level and the motion information corresponding to the beat degradation level to the second IPTV client so that it can be displayed on the IPTV corresponding to the second IPTV client.
[0095] The motion information corresponding to the excellent level can be at least one of the following: an image, video clip, and identification number of a motion motion rated as excellent; the motion information corresponding to the beat downgrade level can be at least one of the following: an image, video clip, and identification number of a motion motion rated as beat downgrade.
[0096] According to the embodiments of this application, the motion-sensing training method receives a training start command for a first motion-sensing exercise sent by a student client, and based on the first motion-sensing exercise program, determines at least one target motion-sensing signal in the first motion-sensing exercise, at least one quasi-action period that generates the at least one target motion-sensing signal, and a secondary action period within a first duration before and after the quasi-action period. It also receives at least one training motion-sensing signal sent by the student client corresponding to the at least one target motion-sensing signal, performs response processing based on the at least one training motion-sensing signal, generates a video stream, sends the video stream to a first IPTV client, and acquires each training... The system calculates the absolute value of the difference between the position parameters of the training motion-sensing signal and the corresponding target motion-sensing signal. When the absolute value of the difference is less than or equal to a first threshold and the training motion-sensing signal is within the corresponding quasi-action period, the action is rated as excellent. When the absolute value of the difference is less than or equal to the first threshold and the training motion-sensing signal is within the corresponding sub-action period, the action is rated as beat-down. Furthermore, by determining the excellent rate and beat-down rate of all motion-sensing actions corresponding to at least one training motion-sensing signal, and based on the excellent rate and beat-down rate, the training score of the first motion-sensing movement on the student client is determined, and the training score is sent to the second IPTV client. This system not only achieves low-latency, high-stability cloud-based motion-sensing training but also implements a strategy and scoring mechanism that takes into account beat-deficient movements, thus ensuring the reliability of cloud-based motion-sensing training and the accuracy of scoring. In addition, it provides a larger display size, significantly improving the user experience. The effect is particularly noticeable for motion-sensing training with high requirements for training standards and assessment accuracy.
[0097] In this embodiment of the application, before the step of receiving the training start command for the first somatosensory movement, refer to Figure 3 As shown, the method also includes the following steps:
[0098] In step 301, the teaching start command sent by the teacher's client is received.
[0099] In step 302, the teacher's client sends the first somatosensory motion information, which includes at least one target somatosensory signal and its corresponding time point, first duration and first threshold.
[0100] In step 303, a first somatosensory motion program is generated based on the first somatosensory motion information.
[0101] In other words, teachers can send a teaching start command through their client application and then demonstrate the first motion-sensing movement. At this time, the motion-sensing device corresponding to the teacher's client collects the motion signal of this first motion-sensing movement demonstration and sends it to the teacher's client. The teacher's client then sends this motion signal to the cloud platform. The cloud platform receives the motion signal and determines its corresponding time point. Thus, the cloud platform receives all the target motion signals and their corresponding time points in the first motion-sensing movement, and combines this with a first duration and a first threshold (which can be determined by input from the teacher's client application) to generate the first motion-sensing movement program for subsequent training by students.
[0102] In this embodiment of the application, the method further includes: after determining the training score of the first somatosensory movement of the student client, storing the identification information of the target somatosensory signal of each beat downgraded movement in this training in the rating history of the first somatosensory movement of the corresponding student client.
[0103] Furthermore, before receiving at least one training somatosensory signal sent by the student client, refer to Figure 4 As shown, the method also includes the following steps:
[0104] In step 401, the rating history of the first motion-sensing exercise of the corresponding student client is obtained.
[0105] In step 402, based on the identification information in the rating history, the target somatosensory signal of the beat downgrade action in the previous first somatosensory movement is determined, and the corresponding beat reminder information is generated.
[0106] In step 403, during the quasi-action period and the secondary action period of the target somatosensory signal of the beat-downgraded movement, the corresponding beat reminder information is sent to the training screen of the corresponding student client.
[0107] In other words, after each student user completes their first motion-sensing exercise training session, the cloud platform stores a corresponding rating history in the student's account. This history includes the identification information (such as an identification number) of the target motion signal for the beat-downgraded movement during that first session. The next time the student performs a first session of motion-sensing exercise, this identification information can be retrieved to determine the target motion signal for the beat-downgraded movement and generate corresponding beat reminders. Then, step 403 is executed to display beat reminders such as "faster" or "slower" on the student's IPTV during the quasi-movement period and subsequent movement period of the corresponding target motion signal. This achieves learning based on the previous motion-sensing exercise history, improving functionality and enhancing training effectiveness.
[0108] In this embodiment of the application, there are multiple IPTV clients, including at least two student clients; the method further includes: receiving a composite image instruction sent by a teacher client; performing composite processing on the training images of at least two student clients after response processing according to the composite image instruction; generating a composite image video stream based on the composite training images; and sending the composite image video stream to the teacher client and at least two student clients.
[0109] Specifically, the compositing process in this application can be splicing or picture-in-picture processing. In a specific example, compositing can be performed using FFmpeg (Fast Forward moving picture expert group). In a specific example, the scenario simulated in this embodiment could be a teacher remotely instructing or testing multiple students on their movements, or a teacher remotely rehearsing a movement program featuring multiple program members.
[0110] In remote testing scenarios, the synthesized video stream can be sent only to the teacher's client, allowing the teacher to see the simultaneous testing status of multiple students in real time. This not only enables the implementation of a strategy and scoring mechanism that takes into account rhythmic defects, but also allows for simultaneous, real-time remote exercise testing for multiple students, offering advantages such as saving manpower and time for exercise proctoring and ensuring test confidentiality.
[0111] In remote teaching scenarios, a composite video stream can be sent to both the teacher's client and the students' clients who are currently participating in a fitness class. This allows both the teacher and students involved in the exercise to see the synchronized movements of multiple students in real time through the composite video stream. This not only enables the implementation of a strategy and scoring mechanism that addresses rhythmic errors but also ensures a smooth learning experience for multiple students simultaneously.
[0112] In scenarios involving remote program rehearsals, the composite video stream can be sent to both the teacher's client and the student's client, which is currently rehearsing a particular fitness exercise. This allows the teacher and other participants to see the synchronized movements of multiple members in real time through the composite video stream. This not only enables the implementation of a strategy and scoring mechanism that addresses rhythmic errors, but also provides a superior visual experience for multiple participants rehearsing simultaneously, further enhancing the user experience.
[0113] In this embodiment of the application, there are multiple IPTV clients, including at least two student clients; the method further includes: receiving a composite image instruction sent by at least two student clients; performing composite processing on the training images of at least two student clients after response processing according to the composite image instruction; generating a composite image video stream based on the composite processing training images; and sending the composite image video stream to at least two student clients.
[0114] Specifically, this embodiment can be used in scenarios where multiple students train simultaneously. Specifically, when multiple students need to train simultaneously, after sending a training start command for the first motion-sensing movement to the cloud platform, a composite image command can be sent, and then the training for the first motion-sensing movement can begin. During the training process, students can see each other's training footage on their corresponding IPTV, satisfying the high interactivity needs of student users.
[0115] In summary, the motion-sensing training method according to the embodiments of this application involves receiving a training start command for a first motion-sensing exercise sent by a student client, and based on the first motion-sensing exercise program, determining at least one target motion-sensing signal in the first motion-sensing exercise, at least one quasi-action period that generates the at least one target motion-sensing signal, and a secondary action period within a first duration before and after the quasi-action period. It also involves receiving at least one training motion-sensing signal corresponding to the at least one target motion-sensing signal sent by the student client, performing response processing based on the at least one training motion-sensing signal, generating a video stream, sending the video stream to a first IPTV client, and obtaining each... The system calculates the absolute value of the difference between the position parameters of a training motion signal and a corresponding target motion signal. It determines an excellent motion rating when the absolute value of the difference is less than or equal to a first threshold and the training motion signal is within the corresponding quasi-motion period. Conversely, it determines a beat-down rating when the absolute value of the difference is less than or equal to the first threshold and the training motion signal is within the corresponding sub-motion period. Furthermore, it determines the excellent rate and beat-down rate of all motion movements corresponding to at least one training motion signal, and based on these rates, determines the training score for the first motion movement on the student client, sending the training score to the second IPTV client. This system not only achieves low-latency, high-stability cloud-based motion training but also incorporates a strategy and scoring mechanism that addresses beat-deficient movements, ensuring the reliability and accuracy of cloud-based motion training. Additionally, it provides a larger display size, significantly improving the user experience. The system is particularly effective for motion training that demands high standards and accuracy in assessment.
[0116] Example 2
[0117] This application provides a motion-sensing training system, referenced... Figure 1As shown, the motion-sensing training system 10 includes a cloud platform 11 for motion-sensing training, at least two IPTV clients 12, and at least two motion-sensing devices 12 corresponding to the IPTV clients 12; the at least two IPTV clients 12 include a teacher client 121 and at least one student client 122.
[0118] At least two motion-sensing devices 13 are used to collect training motion-sensing signals and send the training motion-sensing signals to the corresponding IPTV client 12.
[0119] At least one student client 122 is used to receive the training start command and training motion signal of the first motion sensing movement and send them to the cloud platform terminal 11.
[0120] The cloud platform terminal 11 is used for: receiving training start commands sent by student clients 122; determining at least one target somatosensory signal in the first somatosensory motion program, at least one quasi-action period that generates at least one target somatosensory signal, and a secondary action period within a first duration before and after the quasi-action period, based on the first somatosensory motion program; receiving at least one training somatosensory signal sent by student clients 122 corresponding to at least one target somatosensory signal; performing response processing based on at least one training somatosensory signal to generate a video stream; sending the video stream to at least one student client 122 for playback on the IPTV corresponding to at least one student client 122; and acquiring each training... The absolute value of the difference between the position parameters of the training somatosensory signal and the corresponding target somatosensory signal is determined; in response to the absolute value of the difference being less than or equal to a first threshold, and the training somatosensory signal being in the corresponding quasi-action period, the action rating is determined to be excellent; in response to the absolute value of the difference being less than or equal to the first threshold, and the training somatosensory signal being in the corresponding sub-action period, the action rating is determined to be downgraded; the excellent rate and the downgrade rate of all somatosensory movements in this first somatosensory exercise are determined; the first somatosensory exercise training score of student client 122 is determined based on the excellent rate and the downgrade rate; the training score is sent to teacher client 121 so that it can be displayed through the IPTV corresponding to teacher client 121.
[0121] Teacher client 121 is used to receive training scores and display them on the corresponding IPTV.
[0122] At least one student client 122 is also used to receive video streams for playback on the corresponding IPTV.
[0123] In the embodiments of this application, the training score is determined in the following manner:
[0124]
[0125] Where S is the training score; A is the excellence rate; B is the beat degradation rate; t1 is the duration of the quasi-movement period; and t2 is the duration of the sub-movement period.
[0126] In the embodiments of this application, the teacher client 121 is further configured to send first motion sensing information. The cloud platform client 11 is further configured to: receive a teaching start command sent by the teacher client 121; receive the first motion sensing information sent by the teacher client 121, the first motion sensing information including at least one target motion sensing signal and its corresponding time point, first duration and first threshold; and generate a first motion sensing program based on the first motion sensing information.
[0127] In the embodiments of this application, the cloud platform 11 is further configured to: send at least one of the following: excellent rate, beat degradation rate, motion sensing information corresponding to the excellent level, and motion sensing information corresponding to the beat degradation level, to the teacher client 122 for display on the teacher client 122. The teacher client 122 is further configured to: receive at least one of the following: excellent rate, beat degradation rate, motion sensing information corresponding to the excellent level, and motion sensing information corresponding to the beat degradation level, and send it to the corresponding IPTV for display via the corresponding IPTV.
[0128] In the embodiments of this application, the cloud platform 11 is further configured to: after determining the first somatosensory training score of the student client 122, store the identification information of the target somatosensory signal of the beat downgrade action in this training in the rating history of the corresponding student client 122.
[0129] Furthermore, the cloud platform 11 is also used to: obtain the rating history of the previous first motion-sensing exercise of the corresponding student client 122; determine the target motion-sensing signal of the beat downgrade movement in the previous first motion-sensing exercise based on the identification information in the rating history, and generate corresponding beat reminder information; and send the corresponding beat reminder information to the training screen of the corresponding student client 122 during the quasi-movement period and the secondary movement period of the target motion-sensing signal of the beat downgrade movement.
[0130] In the embodiments of this application, there are multiple IPTV clients 12, including at least two student clients 122. The teacher client 121 is further configured to: send a composite image instruction to the cloud platform 11. The cloud platform 11 is further configured to: receive the composite image instruction sent by the teacher client 121; perform composite processing on the training images of the at least two student clients 122 after response processing according to the composite image instruction; generate a composite image video stream based on the composite processing training images; and send the composite image video stream to the teacher client 121 and the at least two student clients 122. The teacher client 121 and the at least two student clients 122 are further configured to: receive the composite image video stream sent by the cloud platform 11.
[0131] In the embodiments of this application, there are multiple IPTV clients 12, including at least two student clients 122. The at least two student clients 122 are further configured to: send a composite image instruction to the cloud platform 11. The cloud platform 11 is further configured to: receive the composite image instruction sent by the at least two student clients 122; perform composite processing on the training images of the at least two student clients 122 after response processing according to the composite image instruction; generate a composite image video stream based on the composite processing training images; and send the composite image video stream to the at least two student clients 122. The at least two student clients 122 are further configured to: receive the composite image video stream sent by the cloud platform 11.
[0132] It should be noted that the explanation of the motion-sensing training method in the above embodiments also applies to the motion-sensing training system in this embodiment, and will not be repeated here.
Claims
1. A motion-sensing training method, applied to a motion-sensing training system, the system comprising a cloud platform for motion-sensing training, at least two IPTV clients, and at least two motion-sensing devices corresponding to the IPTV clients; The at least two IPTV clients include one teacher client and at least one student client; On the cloud platform, the method includes: Receive the first motion-sensing exercise training start command sent by the student client; Based on the first somatosensory motion program, at least one target somatosensory signal in the first somatosensory motion is determined, at least one quasi-action period that generates the at least one target somatosensory signal is determined, and a secondary action period within a first duration before and after the quasi-action period is determined. The system receives at least one training somatosensory signal corresponding to the at least one target somatosensory signal sent by the student client; performs response processing based on the at least one training somatosensory signal to generate a video stream, and sends it to a first IPTV client for playback on the IPTV corresponding to the first IPTV client; the first IPTV client includes a corresponding student client, or includes the corresponding student client and the teacher client; Obtain the absolute value of the difference between the position parameters of each training somatosensory signal and the corresponding target somatosensory signal; In response to the absolute value of the difference being less than or equal to a first threshold, and the training somatosensory signal being in the corresponding quasi-movement period, the movement rating is determined to be excellent; in response to the absolute value of the difference being less than or equal to the first threshold, and the training somatosensory signal being in the corresponding sub-movement period, the movement rating is determined to be beat downgraded. Determine the excellence rate and rhythm degradation rate of all motion-sensing movements in this first motion-sensing exercise; determine the training score of each student client for this first motion-sensing exercise based on the excellence rate and the rhythm degradation rate; The training score is sent to a second IPTV client for display on the corresponding IPTV service; the second IPTV client includes the teacher's client, or includes both the teacher's client and the corresponding student's client; the training score is determined in the following manner: Wherein, S is the training score; A is the excellence rate; B is the beat degradation rate; t1 is the duration of the quasi-action period; and t2 is the duration of the sub-action period.
2. The somatosensory motion training method according to claim 1, characterized in that, Before the step of receiving the training start command for the first somatosensory movement, the method further includes: Receive the teaching start command sent by the teacher's client; Receive first somatosensory motion information sent by the teacher client, the first somatosensory motion information including the at least one target somatosensory signal and its corresponding time point, the first duration, and the first threshold; Based on the first somatosensory motion information, the first somatosensory motion program is generated.
3. The somatosensory motion training method according to claim 1, characterized in that, The method further includes sending at least one of the excellent rate, the beat degradation rate, the motion information corresponding to the excellent level, and the motion information corresponding to the beat degradation level to the second IPTV client so that it can be displayed on the IPTV corresponding to the second IPTV client.
4. The somatosensory motion training method according to claim 1, characterized in that, The method further includes: after determining the training score of the first somatosensory movement for each student client, storing the identification information of the target somatosensory signal of the beat downgrade movement in this training in the rating history of the first somatosensory movement of the corresponding student client.
5. The somatosensory motion training method according to claim 4, characterized in that, Before receiving the at least one training somatosensory signal sent by the student client, the method further includes: Retrieve the rating history of the first motion-sensing exercise from the corresponding student's client. Based on the identification information in the rating history, the target somatosensory signal of the beat downgrade action in the previous first somatosensory movement is determined, and corresponding beat reminder information is generated; During the quasi-action period and the secondary action period of the target somatosensory signal of the beat degradation movement, the corresponding beat reminder information is sent to the training screen of the corresponding student client.
6. The somatosensory motion training method according to claim 1, characterized in that, The IPTV client is multiple, including at least two student clients; the method further includes: Receive the composite image command sent by the teacher's client; According to the composite screen instruction, the training screens of the at least two student clients after the response processing are composited. A synthesized video stream is generated based on the synthesized training footage; The synthesized video stream is sent to the teacher's client and the at least two student clients.
7. The somatosensory motion training method according to claim 1, characterized in that, The IPTV client is multiple, including at least two student clients; the method further includes: Receive the composite screen command sent by the at least two student clients; According to the composite screen instruction, the training screens of the at least two student clients after the response processing are composited. A synthesized video stream is generated based on the synthesized training footage; The synthesized video stream is sent to the at least two student clients.
8. The somatosensory motion training method according to any one of claims 1-7, characterized in that, The IPTV client is configured in the IPTV or in the IPTV set-top box.
9. A motion-sensing training system, characterized in that, This includes a cloud platform for motion-sensing training, at least two IPTV clients, and at least two motion-sensing devices corresponding to the IPTV clients; the at least two IPTV clients include one teacher client and at least one student client; wherein, The at least two motion-sensing devices are used to collect training motion-sensing signals and send the training motion-sensing signals to the corresponding IPTV client; The at least one student client is used to receive the training start command and training motion signal of the first motion sensing movement, and send them to the cloud platform. The cloud platform is used for: Receive the training start command sent by the student client; Based on the first somatosensory motion program, at least one target somatosensory signal in the first somatosensory motion is determined, at least one quasi-action period that generates the at least one target somatosensory signal is determined, and a secondary action period within a first duration before and after the quasi-action period is determined. The system receives at least one training somatosensory signal corresponding to the at least one target somatosensory signal sent by the student client; performs response processing based on the at least one training somatosensory signal to generate a video stream; and sends the video stream to the at least one student client for playback on the IPTV corresponding to the at least one student client. Obtain the absolute value of the difference between the position parameters of each training somatosensory signal and the corresponding target somatosensory signal; In response to the absolute value of the difference being less than or equal to a first threshold, and the training somatosensory signal being in the corresponding quasi-movement period, the movement rating is determined to be excellent; in response to the absolute value of the difference being less than or equal to the first threshold, and the training somatosensory signal being in the corresponding sub-movement period, the movement rating is determined to be beat downgraded. Determine the excellence rate and rhythm degradation rate of all motion-sensing movements in this first motion-sensing exercise; determine the training score of each student client for this first motion-sensing exercise based on the excellence rate and the rhythm degradation rate; The training score is determined in the following manner: Where S is the training score; A is the excellence rate; B is the beat degradation rate; t1 is the duration of the quasi-action period; and t2 is the duration of the sub-action period. The training score is sent to the teacher's client so that it can be displayed on the IPTV corresponding to the teacher's client. The teacher's client is used to receive the training scores and display them on the corresponding IPTV. The at least one student client is also configured to receive the video stream for playback on the corresponding IPTV.
Citation Information
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