1. A live fitness method, comprising:

acquiring a coach body-building video of a coach through coach end equipment, and forwarding the coach body-building video to a plurality of student end equipment in real time for playing;

acquiring a student body-building video of a student through student-side equipment;

determining a current target action based on the coach fitness video;

acquiring a current action to be detected based on the student fitness video;

performing action identification and error correction based on the current target action and the current action to be detected to obtain student fitness data corresponding to the student fitness video; and

and providing the trainee fitness data of the plurality of trainee fitness videos acquired by the plurality of trainee end devices respectively to the coach end device for displaying.

2. The live fitness method of claim 1, wherein the trainee fitness data at least comprises error types of the current actions to be measured in the trainee fitness video, and the error types with the largest number in the trainee fitness data of the trainee fitness videos respectively acquired by the trainee end equipment are displayed to the trainer end equipment.

3. The live fitness method of claim 2, further comprising

Calling a pre-stored correction video corresponding to the error type according to the error type with the maximum number;

and sending the correction video to the trainee end equipment with the trainee fitness data of the error type for playing.

4. The live fitness method of claim 2, wherein the trainee fitness data further comprises a motion score for a current activity to be measured in the trainee fitness video, the motion score calculated based on a degree of match between the current activity to be measured and the current target activity, the live fitness method further comprising:

and calling the student fitness video acquired by the student end equipment of the student fitness data with the lowest action score in the student fitness data with the error type according to the error type with the largest number so as to correct the action of the coach.

5. The live fitness method of claim 4,

self the body image of training is obtained in the training body-building video to the cutout, acquires student's body image in the student body-building video of transferring, superposes student's body image in the training body-building video, and train end equipment broadcast superposes train's body image in the student body-building video of transferring, and in student end equipment that has this wrong type student body-building data in order to broadcast.

6. The live fitness method of claim 5, wherein after superimposing the video of the coach's body onto the retrieved video of the trainee's fitness, further comprising:

determining an overlapping position between the body image of the coach and the body image of the student;

mapping the overlapping location to a target location of a wearable device of a trainee, the wearable device including a plurality of tactile sensors;

such that the tactile sensor located at the target location of the trainee's wearable device provides a tactile signal to the trainee.

7. The live fitness method of any one of claims 1 to 6, wherein the trainee fitness data at least comprises an error type of the current action to be measured in the trainee fitness video and an action score of the current action to be measured in the trainee fitness video, and the trainee fitness data is displayed on the trainer device in a table form.

8. The live fitness method of any one of claims 1-6, wherein performing action recognition error correction based on the current target action and the current action to be tested comprises:

a. determining target actions, wherein the target actions at least comprise one target action stage, each target action stage is divided into a plurality of target part actions, the target part actions comprise actions of dividing 5 body parts according to the body parts and at least one random part action, and the body parts comprise: a left arm, a right arm, a left leg, a right leg, and a torso, the random part being comprised of selected at least two skeletal points in the body part,

the random part action at least corresponds to one or more process-oriented identification items, each identification item comprises an identification object, an identification parameter, an identification rule and a standard skeleton point coordinate base, the identification object comprises a vector formed by at least two skeleton points of the random part corresponding to the process-oriented identification items, and the standard skeleton point coordinate base stores standard coordinates of all the skeleton points in the target action according to the time sequence;

b. dividing the action to be tested into at least one action stage to be tested according to the time of the target action stage of the target action, and forming a matching group by the target action stage and the action stage to be tested corresponding to the time;

d. in each matching group, dividing the action stage to be detected into corresponding action of the part to be detected according to the action of the target part in the target action stage, and forming a part matching group by the action of the part to be detected in the action stage to be detected and the action of the target part in the corresponding target action stage;

e. for each part matching group, at least acquiring an identification item of random part action in target part action, acquiring a vector formed by at least two selected bone points in the action of the part to be detected according to the two-dimensional bone action model, and performing matching calculation on the vector of the random part action and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library so as to compare the vector with a vector threshold set by the identification parameters to acquire action identification feedback;

f. and integrating the action recognition feedback of at least one matching group to obtain the action recognition feedback of the action to be detected.

9. A live fitness system, comprising:

the training end equipment is used for acquiring training body-building videos of a training;

the student end equipment is used for collecting student body-building videos of the students;

the forwarding module is used for forwarding the coach fitness video to a plurality of student-side devices in real time for playing;

a determination module for determining a current target action based on the coach fitness video;

the first acquisition module is used for acquiring the current action to be detected based on the student fitness video;

the second acquisition module is used for carrying out motion recognition and error correction based on the current target motion and the current motion to be detected to acquire student fitness data corresponding to the student fitness video; and

and the sending module is used for providing the trainee fitness data of the plurality of trainee fitness videos acquired by the plurality of trainee end devices respectively to the coach end device for displaying.

10. An electronic device, characterized in that the electronic device comprises:

a processor;

a storage medium having stored thereon a computer program which, when executed by the processor, performs the live fitness method of any one of claims 1 to 8.

11. A storage medium having stored thereon a computer program which, when executed by a processor, performs the live fitness method of any one of claims 1 to 8.

Background

Currently live technology is rapidly developing and more users are doing fitness or other sports such as dance through live broadcasting.

However, in the current live broadcast technology, especially in the live body-building broadcast, the anchor (coach) cannot obtain the body-building information of the student, and the interaction mode between the anchor (coach) and the student is very limited, and the interaction can be performed only through the barrage.

Therefore, how to realize more information interaction and interaction between a main broadcaster (coach) and a student in a live broadcast fitness (including dance, sport, yoga and the like) in a technical mode is a technical problem to be solved urgently in the field.

Disclosure of Invention

In order to overcome the defects in the prior art, the invention provides a live broadcast fitness method, a live broadcast fitness system, electronic equipment and a storage medium, so that a student fitness video of a student is converted into student fitness data through a motion error correction recognition algorithm and displayed on coach end equipment, the student fitness data of a plurality of students can be simultaneously displayed on the coach end equipment, and a coach can intuitively obtain more student fitness data.

According to one aspect of the invention, a live broadcast fitness method is provided, comprising:

acquiring a coach body-building video of a coach through coach end equipment, and forwarding the coach body-building video to a plurality of student end equipment in real time for playing;

acquiring a student body-building video of a student through student-side equipment;

determining a current target action based on the coach fitness video;

acquiring a current action to be detected based on the student fitness video;

performing action identification and error correction based on the current target action and the current action to be detected to obtain student fitness data corresponding to the student fitness video; and

and providing the trainee fitness data of the plurality of trainee fitness videos acquired by the plurality of trainee end devices respectively to the coach end device for displaying.

Optionally, the trainee fitness data at least includes the error type of the current action to be measured in the trainee fitness video, and the error type with the largest number in the trainee fitness data of the plurality of trainee fitness videos acquired by the plurality of trainee end devices is displayed to the trainer end device.

Optionally, also comprises

Calling a pre-stored correction video corresponding to the error type according to the error type with the maximum number;

and sending the correction video to the trainee end equipment with the trainee fitness data of the error type for playing.

Optionally, the trainee fitness data further includes a motion score of a current motion to be measured in the trainee fitness video, the motion score is calculated based on a matching degree between the current motion to be measured and the current target motion, and the live-broadcast fitness method further includes:

and calling the student fitness video acquired by the student end equipment of the student fitness data with the lowest action score in the student fitness data with the error type according to the error type with the largest number so as to correct the action of the coach.

Optionally, from scratch in the train body-building video and acquire the health image of train, scratch the health image that acquires the student from the student body-building video who transfers, stack the health image of student to in the train body-building video, and train end equipment broadcast, stack the health image of train to the student body-building video who transfers in, and in student end equipment that has this wrong type student body-building data plays in order to play.

Optionally, after the superimposing the body image of the coach to the called student fitness video, the method further includes:

determining an overlapping position between the body image of the coach and the body image of the student;

mapping the overlapping location to a target location of a wearable device of a trainee, the wearable device including a plurality of tactile sensors;

such that the tactile sensor located at the target location of the trainee's wearable device provides a tactile signal to the trainee.

Optionally, the trainee fitness data at least includes an error type of the current action to be measured in the trainee fitness video and an action score of the current action to be measured in the trainee fitness video, and the trainee fitness data is displayed on the trainer device in a tabular form.

Optionally, the performing, by the motion recognition and error correction based on the current target motion and the current motion to be detected includes:

a. determining target actions, wherein the target actions at least comprise one target action stage, each target action stage is divided into a plurality of target part actions, the target part actions comprise actions of dividing 5 body parts according to the body parts and at least one random part action, and the body parts comprise: a left arm, a right arm, a left leg, a right leg, and a torso, the random part being comprised of selected at least two skeletal points in the body part,

the random part action at least corresponds to one or more process-oriented identification items, each identification item comprises an identification object, an identification parameter, an identification rule and a standard skeleton point coordinate base, the identification object comprises a vector formed by at least two skeleton points of the random part corresponding to the process-oriented identification items, and the standard skeleton point coordinate base stores standard coordinates of all the skeleton points in the target action according to the time sequence;

b. dividing the action to be tested into at least one action stage to be tested according to the time of the target action stage of the target action, and forming a matching group by the target action stage and the action stage to be tested corresponding to the time;

d. in each matching group, dividing the action stage to be detected into corresponding action of the part to be detected according to the action of the target part in the target action stage, and forming a part matching group by the action of the part to be detected in the action stage to be detected and the action of the target part in the corresponding target action stage;

e. for each part matching group, at least acquiring an identification item of random part action in target part action, acquiring a vector formed by at least two selected bone points in the action of the part to be detected according to the two-dimensional bone action model, and performing matching calculation on the vector of the random part action and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library so as to compare the vector with a vector threshold set by the identification parameters to acquire action identification feedback;

f. and integrating the action recognition feedback of at least one matching group to obtain the action recognition feedback of the action to be detected.

According to another aspect of the present invention, there is also provided a live fitness system, comprising:

the training end equipment is used for acquiring training body-building videos of a training;

the student end equipment is used for collecting student body-building videos of the students;

the forwarding module is used for forwarding the coach fitness video to a plurality of student-side devices in real time for playing;

a determination module for determining a current target action based on the coach fitness video;

the first acquisition module is used for acquiring the current action to be detected based on the student fitness video;

the second acquisition module is used for carrying out motion recognition and error correction based on the current target motion and the current motion to be detected to acquire student fitness data corresponding to the student fitness video; and

and the sending module is used for providing the trainee fitness data of the plurality of trainee fitness videos acquired by the plurality of trainee end devices respectively to the coach end device for displaying.

According to still another aspect of the present invention, there is also provided an electronic apparatus, including: a processor; a storage medium having stored thereon a computer program which, when executed by the processor, performs the steps as described above.

According to yet another aspect of the present invention, there is also provided a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps as described above.

Compared with the prior art, the student fitness video of the student is converted into the student fitness data through the action error correction recognition algorithm and displayed on the coach end equipment, so that the student fitness data of a plurality of students can be displayed on the coach end equipment at the same time, and a coach can visually obtain more student fitness data.

Drawings

The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

FIG. 1 shows a flow diagram of a live fitness method according to an embodiment of the invention;

FIG. 2 shows a display of a trainer end device according to an embodiment of the invention;

FIG. 3 shows a schematic diagram of a live fitness system according to an embodiment of the present invention;

FIG. 4 shows a schematic diagram of a live fitness interaction, according to an embodiment of the invention;

FIG. 5 illustrates a flow diagram of motion recognition error correction according to an embodiment of the present invention;

FIG. 6 shows a schematic diagram of a bone model according to an embodiment of the invention;

figures 7 to 11 show schematic views of 5 body parts according to an embodiment of the invention;

FIG. 12 illustrates a comparison of a standard vector formed by bone points from a standard bone point coordinate base and a real-time acquisition vector in accordance with an embodiment of the present invention;

FIGS. 13 and 14 are diagrams illustrating an angle between the normal vectors formed by the bone points in the normal bone point coordinate base and an angle between the real-time collection vectors according to an embodiment of the present invention;

FIG. 15 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the disclosure.

Fig. 16 schematically illustrates an electronic device in an exemplary embodiment of the disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams depicted in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

Referring initially to fig. 1, fig. 1 illustrates a flow diagram of a live fitness method according to an embodiment of the present invention.

Fig. 1 shows 6 steps in total:

step S11: acquiring a coach body-building video of a coach through coach end equipment, and forwarding the coach body-building video to a plurality of student end equipment in real time for playing;

step S12: acquiring a student body-building video of a student through student-side equipment;

step S13: determining a current target action based on the coach fitness video;

step S14: acquiring a current action to be detected based on the student fitness video;

step S15: performing action identification and error correction based on the current target action and the current action to be detected to obtain student fitness data corresponding to the student fitness video; and

step S16: and providing the trainee fitness data of the plurality of trainee fitness videos acquired by the plurality of trainee end devices respectively to the coach end device for displaying.

Therefore, the live broadcast fitness method provided by the invention converts the student fitness video of the student into student fitness data through the action error correction recognition algorithm, and displays the student fitness data on the coach end equipment, so that the student fitness data of a plurality of students can be displayed on the coach end equipment at the same time, and a coach can visually obtain more student fitness data.

In an embodiment of the present invention, the trainee fitness data at least includes an error type of a current action to be measured in the trainee fitness video and a motion score of the current action to be measured in the trainee fitness video, and the trainee fitness data is displayed on the trainer end device in a tabular form (as shown in fig. 2), so that the trainer end device does not need to display trainee fitness videos of a plurality of trainees, and can provide the trainee fitness data to the trainer only through characters, and the trainer can obtain more trainee fitness data at the same time and is more intuitive. The present invention is not limited to the tabular form or display layout of fig. 2.

In an embodiment of the invention, the trainee fitness data at least includes error types of the current actions to be measured in the trainee fitness video, and the error type with the largest number in the trainee fitness data of the trainee fitness videos respectively collected by the plurality of trainee end devices is displayed to the trainer end device. In the embodiment of fig. 2, the error type is the most error 2, so that the most error type of the current target action can be informed to the coach through highlighting, enlarged display, voice playing, etc., so that the coach can give key teaching and explanation through live broadcasting.

In the above embodiment of the present invention, the live broadcast fitness method may further include the following steps: calling a pre-stored correction video corresponding to the error type according to the error type with the maximum number; and sending the correction video to the trainee end equipment with the trainee fitness data of the error type for playing. The corrected video may be pre-recorded according to different error types and stored in the trainee end device/cloud platform, for example, so as to facilitate retrieval and playing when the error type is the most. The error correction video can be suspended at the corner of the live video of the student end equipment so as to avoid influencing the live video; the live video can be replaced to be played so as to carry out targeted error correction and avoid damage caused by action errors.

In the above embodiment of the present invention, the trainee fitness data further includes a motion score of a current motion to be measured in the trainee fitness video, the motion score is calculated based on a matching degree between the current motion to be measured and the current target motion, and the live broadcast fitness method further includes the following steps: and calling the student fitness video acquired by the student end equipment of the student fitness data with the lowest action score in the student fitness data with the error type according to the error type with the largest number so as to correct the action of the coach. Therefore, through the interaction between the coach and the student with the lowest score, the personnel participating in the live broadcast feel more involved and are closer to the experience of the gym group class. In this embodiment, before calling up the video of the trainee-side device, preferably, an inquiry option is provided, and the calling up is performed only when the trainee agrees to forward his video. In the embodiment, in order to eliminate the trainee who does not move on the trainee end device, the lowest action score can be set with a score lower limit, that is, only the trainee fitness video of the trainee with the lowest action score in the error type is called, wherein the student fitness video is larger than the score lower limit. The lower score limit may be set to 0 point, 5 points, 10 points, etc., for example, and the present invention is not limited thereto.

In the above embodiment of the present invention, the method may further include the following steps: self the body image of training is obtained in the training body-building video to the cutout, acquires student's body image in the student body-building video of transferring, superposes student's body image in the training body-building video, and train end equipment broadcast superposes train's body image in the student body-building video of transferring, and in student end equipment that has this wrong type student body-building data in order to broadcast. Specifically, referring to fig. 3 and 4, a body image of the coach 6 is obtained by matting from a coach body-building video of the coach 6, a body image of the student 7 is obtained by matting from the called student body-building video, the body image of the student 7 obtained by matting from the called student body-building video is superimposed on the video data of the coach 6, and is played in the display screen 1 of the coach-side device of the coach 6; the body image of the trainer 6 is superimposed on the video data of the trainee 7 and is played on the display screen 4 of the trainee's 7 trainee end device (the display screen 4 of the trainee end device described here can be the display screen 4 of all trainee end devices with this error type). Therefore, both students and coaches can have visual error correction experience, the calculated amount of the cutout and superposition modes is small, and the use in a live broadcast scene is facilitated.

In the above embodiment of the present invention, after the step of superimposing the body image of the trainer on the called fitness video of the trainee, the method may further include the following steps: determining an overlapping position between the body image of the coach and the body image of the student; mapping the overlapping location to a target location of a wearable device of a trainee, the wearable device including a plurality of tactile sensors; such that the tactile sensor located at the target location of the trainee's wearable device provides a tactile signal to the trainee. The tactile signal may be, for example, a weak current signal or other physical signal that mimics the sense of touch. Thereby, by further approximating the real-life fitness experience.

According to another aspect of the present invention, there is also provided a live fitness system, see fig. 3. The live-broadcast fitness system may include a coach-side device (including the acquisition device 2 and the display device 1, the acquisition device 2 and the display device 1 may be independent devices or may be integrated together), a student-side device (including the acquisition device 5 and the display device 4, the acquisition device 5 and the display device 4 may be independent devices or may be integrated together), a forwarding module, a determining module, a first acquiring module, a second acquiring module, and a sending module (the forwarding module, the determining module, the first acquiring module, the second acquiring module, and the sending module may be integrated in the cloud server 3, for example, or may be partially integrated in the coach-side device and the student-side device, which is not limited in this respect).

The acquisition device 2 of the trainer end device is used for acquiring a trainer body-building video of a trainer, and the display device 1 is used for displaying an interface (only schematically) shown in fig. 2. The acquisition device 5 of the student end equipment is used for acquiring student body-building videos of the students, and the display device 4 is used for displaying trainer body-building videos. And the forwarding module is used for forwarding the coach fitness video to a plurality of student-side devices in real time for playing. The determining module is used for determining the current target action based on the coach body-building video. The first obtaining module is used for obtaining the current action to be tested based on the student fitness video. The second acquisition module is used for performing motion recognition and error correction based on the current target motion and the current motion to be detected, and acquiring student fitness data corresponding to the student fitness video. The sending module is used for providing the trainee fitness data of the plurality of trainee fitness videos acquired by the plurality of trainee end devices respectively to the trainer end device for displaying.

Therefore, the live broadcast fitness system provided by the invention converts the student fitness video of the student into student fitness data through the action error correction recognition algorithm, and displays the student fitness data on the coach end equipment, so that the student fitness data of a plurality of students can be displayed on the coach end equipment at the same time, and a coach can visually obtain more student fitness data.

Referring to fig. 5, fig. 5 shows a flow diagram of motion recognition error correction according to an embodiment of the invention. Fig. 5 shows a total of 5 steps:

first, step S110: determining a target action, wherein the target action at least comprises a target action stage, each target action stage is divided into a plurality of target part actions, and the target part actions comprise 5 body part actions divided according to body parts and at least one random part action.

In some embodiments, the target action may be determined by displaying a workout video. Specifically, the fitness video comprises a plurality of target actions, and each target action is associated with the playing time of the fitness video. In other embodiments, the user may directly select the target action.

Specifically, in the present case, 15 skeletal points are set for each human body (see fig. 6), and the 15 skeletal points are: head center 211, neck center (e.g., spinal center of neck) 212, torso center 213 (e.g., spinal center of torso), left shoulder joint point 221, left elbow joint point 222, left wrist joint point 223, right shoulder joint point 231, right elbow joint point 232, right wrist joint point 233, left hip joint point 241, left knee joint point 242, left ankle joint point 243, right hip joint point 251, right knee joint point 252, right ankle joint point 253.

In the present case, the 15 skeletal points are divided into five body parts by taking 3 skeletal points as units: the torso (see fig. 7), the left arm (see fig. 8), the right arm (see fig. 9), the left leg (see fig. 10), and the right leg (see fig. 11). Vectors are formed among the skeleton points in each body part, and included angles are formed among the vectors.

Specifically, the torso (see fig. 7) includes a head center 211, a spine center 212 of the neck, a spine center 213 of the torso, a first vector 214 formed from the head center 211 to the spine center 212 of the neck, a second vector 215 formed from the spine center 212 of the neck to the spine center 213 of the torso, a third vector 216 formed from the head center 211 to the spine center 213 of the torso, and an angle 217 formed by the first vector 214 and the second vector 215.

The left arm (see fig. 8) includes a left wrist joint point 223, a left elbow joint point 222, a left shoulder joint point 221, a first vector 224 formed from the left shoulder joint point 221 to the left elbow joint point 222, a second vector 225 formed from the left elbow joint point 222 to the left wrist joint point 223, a third vector 226 formed from the left shoulder joint point 221 to the left wrist joint point 223, and an angle 227 between the first vector 224 and the second vector 225.

The right arm (see fig. 9) includes a right wrist joint point 233, a right elbow joint point 232, a right shoulder joint point 231, a first vector 234 formed from the right shoulder joint point 231 to the right elbow joint point 232, a second vector 235 formed from the right elbow joint point 232 to the right wrist joint point 233, a third vector 236 formed from the right shoulder joint point 231 to the right wrist joint point 233, and an angle 237 between the first vector 234 and the second vector 235.

The left leg includes (see fig. 10) a left ankle joint point 243, a left knee joint point 242, a left hip joint point 241, a first vector 244 formed from left hip joint point 241 to left knee joint point 242, a second vector 245 formed from left knee joint point 242 to left ankle joint point 243, a third vector 246 formed from left hip joint point 241 to left ankle joint point 243, and an angle 247 between the first vector 244 and the second vector 245.

The right leg includes (see fig. 11) a right ankle joint point 253, a right knee joint point 252, a right hip joint point 251, a first vector 254 formed from right hip joint point 251 to right knee joint point 252, a second vector 255 formed from right knee joint point 252 to right ankle joint point 253, a third vector 256 formed from right hip joint point 251 to right ankle joint point 253, and an angle between the first vector 254 and the second vector 255.

Less representative joint points are set as skeleton points to reduce the amount of calculation in motion recognition and error correction.

The target action is broken down into five body parts: left arm, right arm, left leg, right leg and torso. Each body part comprises three skeletal points as shown in fig. 7 to 11, three vectors formed by the three skeletal points, and an included angle between two of the three vectors.

To increase the flexibility of motion recognition, the target motion may further comprise at least one random part motion, the random part being constituted by at least two selected bone points in said body part, such as selected bone points 212 and 223 in fig. 2, and the random part being formed by bone points 212 and 223. The random part is not limited to this, and any at least two bone points may form the random part, so that on the basis of five body parts, more dimensional motion recognition may be achieved.

The random part action at least corresponds to one or more process-oriented identification items, and each identification item comprises an identification object, an identification parameter, an identification rule and a standard skeleton point coordinate library. In the identification item corresponding to the process, the identification object comprises a vector formed by at least two bone points of the random part. The identification parameters include a set vector threshold. The identification rule includes that the similarity between a vector (identification object) formed by at least two bone points of the random part and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library is required to be greater than or equal to a set vector threshold (identification parameter) in the motion process, and if the similarity between the vector (identification object) formed by at least two bone points of the random part and the standard vector formed by corresponding standard coordinates in the standard bone point coordinate library is smaller than the set vector threshold (identification parameter), an error is reported (the reported error can be stored as the identification parameter in advance).

In a specific embodiment, the vector of the random part motion and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library are subjected to matching calculation to be compared with a vector threshold set by the identification parameter through the following steps:

calculating standard bone point sittingStandard vector formed by corresponding standard coordinates in standard libraryVector of motion of random partCosine of angle θ between:

(Vector)and vectorThe cosine value of the included angle theta is used for comparing with the vector threshold value set by the identification parameter. For example, when bone points 212 and 223 form random sites, the vectorAnd vectorThe vectors formed by the bone points 212 and 223 acquired in real time and the vectors formed by the bone points 212 and 223 in the standard bone point coordinate base are respectively.

Further, in the invention, two-dimensional video data or three-dimensional video data can be acquired in real time according to the function of a camera equipped for the equipment. If the two-dimensional video data collected in real time generates a two-dimensional skeleton motion model, and the coordinates in the standard skeleton point coordinate library can be three-dimensional coordinates, the method further comprises the step of judging whether the corresponding standard coordinates in the standard skeleton point coordinate library are two-dimensional coordinates or not before the matching calculation. And if so, matching and calculating the vector of the random part action and a standard vector formed by corresponding standard coordinates in a standard skeleton point coordinate library. If not, converting the corresponding standard coordinates in the standard skeleton point coordinate library into two-dimensional coordinates, and performing matching calculation with the vector of the random part action.

In a specific embodiment, for the process-oriented identification item corresponding to the random part motion, the identification parameter may further include an initial amplitude threshold and an achievement amplitude threshold, where the initial amplitude threshold is used to determine whether the motion of the part to be detected starts; the achievement amplitude threshold is used for judging whether the action of the part to be detected is finished or not. Specifically, the starting amplitude and the achievement amplitude are based on the position on the action time axis. In specific implementation, the frame number can be used to determine the initial amplitude and the achieved amplitude. For example: assuming that an action has 20 frames of data in the standard bone point coordinate library, assuming that the initial amplitude threshold is set to 0.2, the amplitude threshold is set to 0.8. then the action is determined to start when the matching degree of any frame data between the random part action of the actual action of the user and the 0 th to 4 th (i.e. 20 x 0.2) frames in the standard bone point coordinate library is the highest (within the vector threshold range). When the user action is started and the random part action fails to match with the standard skeleton point coordinate library in the motion process, once the matching degree of any frame data between the random part action of the user action and the 16 th (namely 20 x 0.8) -20 th frames in the standard skeleton point coordinate library is the highest (within the range of the vector threshold), the action is determined to be achieved. The foregoing is merely an illustrative description of implementations of the invention and is not intended to be limiting thereof.

In a specific embodiment, the random part motion further corresponds to one or more distance-oriented recognition items. For the distance-oriented recognition item, the recognition object comprises the distance between at least two bone points of the random part. The identification parameter sets a distance threshold. The identification rule comprises that the identification object of the action of the part to be detected is always larger than or equal to the range of the distance threshold set by the identification parameter in the motion process. In the distance recognition, when the recognition object moving at the part to be detected is always greater than or equal to the distance threshold value set by the recognition parameter in the moving process, the movement is achieved; and when the identification object moving at the part to be detected is smaller than the distance threshold set by the identification parameter in the moving process, an error is reported. In the negative distance recognition, when the recognition object moving at the part to be detected is greater than or equal to the distance threshold set by the recognition parameter at any time in the moving process, an error is reported.

The above description is only illustrative of the embodiment of random part motion recognition error correction in the present invention, and the present invention is not limited thereto. The following will describe an embodiment mode of recognition and error correction of body part motion in the present invention.

The at least one body part action corresponds to one or more process-oriented or displacement-oriented recognized terms. Each identification item comprises an identification object, an identification parameter and an identification rule, wherein the identification object comprises at least one of the three skeleton points of the part action; at least one of the three vectors; and one or more of an angle between two of the three vectors.

The process-oriented identification item needs to be matched with the vector collected in real time through a standard skeleton point coordinate library so as to judge whether the identification item is met. The standard bone point coordinate library stores the coordinates of at least one bone point of the part motion in a time sequence with a sampling frequency. For example, for the left leg movement of the push-up, at least the coordinates of the bone points 221, 222, and 223 of the left arm are stored in time series at a sampling frequency of 5 times/second, whereby the first vector 224 and the second vector 225 (and the angle 227) formed by the bone points 221, 222, and 223 can be known.

Specifically, the identification items facing the process comprise track identification, negative track identification and hold identification; the identification items facing displacement include displacement identification and negative displacement identification.

And the track identification is used for identifying whether the part moves according to a preset track, and if the part does not move according to the preset track, an error is prompted. The identification object comprises at least one vector in the three vectors and/or an included angle between two vectors in the three vectors. The identification parameter sets one or more threshold values corresponding to the identification object. The threshold value comprises a vector threshold value of the three vectors and an included angle threshold value of the included angle, and the identification parameter determines to adopt the vector threshold value and/or the included angle threshold value according to the identification object.

Specifically, the vector threshold and the included angle threshold are used to determine whether the vectors (and included angles) collected in real time match the standard vectors (and included angles between the standard vectors) formed by the standard bone points in the standard bone point coordinate library. For example, referring to FIG. 12, for the vector threshold, the vectors from skeleton point 222 to skeleton point 293 of a body part motion are collected in real timeFinding corresponding bone points 222 to 223 corresponding to the time in a standard bone point coordinate library according to the time to form a vectorComputing vectors in a library of standard skeletal point coordinatesVector of body part motion acquired in real timeCosine of angle θ between:

(Vector)and vectorThe cosine value of the included angle theta (cosine value is-1 to 1) is used for comparing with the vector threshold value set by the identification parameter. The vector threshold may be set to 0.8, the corresponding vectorAnd vectorWhen the cosine value of the included angle theta is greater than or equal to 0.8, the two vectors are considered to be matched. The vector can be determined by comparing the vector threshold with the calculated cosine value Whether it is within the vector threshold.

For example, in the embodiment of setting the included angle threshold, the standard bone point coordinate library stores at least standard bone points in time sequence, and the included angle between the standard vector and the standard vector formed by the standard bone points can be used. The first vector and the second vector of the body part motion can calculate the included angle between the vectors according to the two vectors or directly store the included angle in a standard skeleton point coordinate library. Referring to fig. 13 and 14, the angle threshold is used to compare the ratio α/β of the angle 297 α between the first vector 294 (bone point 292 to bone point 291) and the second vector 295 (bone point 292 to bone point 293) of the motion of the real-time acquired site with the angle 227 β between the first vector 224 (bone point 222 to bone point 221) and the second vector 225 (bone point 222 to bone point 223) of the standard bone point coordinate library at the corresponding time to determine whether the angle of the motion of the real-time acquired site is within the range of the angle threshold. The vector threshold may be set to 0.8, with a corresponding vector threshold of 0.8 to 1. The vector threshold may also be set directly to a range of 0.8 to 1. A comparison may be made based on the angle threshold and the calculated angle ratio to determine whether the angle between the first vector and the second vector is within the vector threshold.

Furthermore, the identification parameters of the track identification also comprise an initial amplitude threshold value and an achievement amplitude threshold value, wherein the initial amplitude threshold value is used for judging whether the part action starts or not, and the achievement amplitude threshold value is used for judging whether the part action finishes or not to complete achievement of the amplitude. Specifically, the starting amplitude and the achievement amplitude are based on the position on the action time axis. In specific implementation, the frame number can be used to determine the initial amplitude and the achieved amplitude. For example: assuming that an action has 20 frames of data in the standard bone point coordinate library, assuming that the initial amplitude threshold is set to 0.2, the amplitude threshold is achieved to be 0.8. then the action is considered to begin when the matching degree of any frame of data between the actual action of the user and the 0 th-4 th (i.e. 20 x 0.2) frames in the standard bone point coordinate library is the highest (within the vector threshold). When the action of the user is started and the matching with the standard bone point coordinate base is not failed in the motion process, once the matching degree of any frame data between the action of the user and the 16 th (namely 20 x 0.8) to 20 th frames in the standard bone point coordinate base is the highest (within the range of the vector threshold value), the action is determined to be achieved. The foregoing is merely an illustrative description of implementations of the invention and is not intended to be limiting thereof.

The recognition rules of the track recognition include achievement rules and optionally different error rules corresponding to the set recognition objects and recognition parameters. The achievement rule of the track recognition is that the recognition object of the part action starts from the position represented by the initial amplitude threshold value and the recognition objects are all within the set vector threshold value and/or included angle threshold value; when the recognition objects of the part action reach the position represented by the amplitude threshold value from the position represented by the initial amplitude threshold value, the recognition objects are all within the set vector threshold value and/or included angle threshold value; and the recognition objects of the part action reach the position represented by the achievement amplitude threshold value and are all within the set vector threshold value and/or included angle threshold value. Different error rules for track identification include: an out of corresponding vector threshold error (e.g., the large arm or thigh represented by vector one is out of threshold); an angle threshold error is exceeded (e.g., an angle at the elbow or an angle at the knee represented by the angle exceeds a threshold); and insufficient amplitude error. The identification rule with the amplitude being not wrong enough is that the identification object of the part action starts from the position represented by the initial amplitude threshold value and the identification objects are all within the set vector threshold value and/or included angle threshold value; when the recognition objects of the part action reach the position represented by the amplitude threshold value from the position represented by the initial amplitude threshold value, the recognition objects are all within the set vector threshold value and/or included angle threshold value; and the recognition objects of the part action do not reach the position represented by the achievement amplitude threshold value and are all within the set vector threshold value and/or included angle threshold value.

And the negative track identification is used for identifying whether the part moves according to a preset track, and if the part moves according to the preset track, an error is prompted. For negative trajectory recognition, which is similar to trajectory recognition, the recognition object comprises at least one of the three vectors and/or an angle between two of the three vectors (preferably, an angle between the first vector and the second vector). And setting one or more thresholds for the identification parameters of the negative track identification, wherein the thresholds comprise vector thresholds of the three vectors and an included angle threshold of the included angle, and the identification parameters determine to adopt the vector thresholds and/or the included angle thresholds according to the identification object. The negative track recognition is different from the track recognition in that the negative track recognition achievement rule is as follows: the identification object of the part action starts from the position represented by the initial amplitude threshold value and is within the set vector threshold value and/or included angle threshold value; when the recognition objects of the part action reach the position represented by the amplitude threshold value from the position represented by the initial amplitude threshold value, the recognition objects are all within the set vector threshold value and/or included angle threshold value; the recognition objects of the part action reach the position represented by the achievement amplitude threshold value and are all within the set vector threshold value and/or included angle threshold value; and there is currently a state in which recognition other than negative recognition and hold recognition is in progress (in other words, the trajectory or displacement amplitude is growing). When the rule is reached, a track error is prompted. In other words, if the recognition object is not always within the threshold range set by the recognition parameter during the movement of the body part, and the motion of the part represented by the recognition object generates a trajectory and/or displacement during the movement, an error will not be presented.

The hold recognition is used to identify whether the motion of the part is kept in a certain state (for example, kept upright or kept at a bending angle) during the motion, and if the motion is not kept in the state, an error is prompted. The identification object kept identified comprises at least one vector in the three vectors and/or an included angle between two vectors in the three vectors. And setting one or more thresholds according to the identification parameters, wherein the thresholds comprise vector thresholds of the three vectors and an included angle threshold of the included angle, and the identification parameters determine to adopt the vector thresholds and/or the included angle thresholds according to the identification object. The achievement rule for keeping identification is: the recognition target of the part motion is always within the set vector threshold and/or included angle threshold. If the achievement rule of the keeping identification is not reached, an error corresponding to the keeping identification is prompted.

For displacement recognition and negative displacement recognition, although the displacement recognition and the negative displacement recognition are described as recognition items facing displacement instead of object, the displacement recognition and the negative displacement recognition actually need to recognize whether the part action is in a continuous motion state, and if the part action is not in the continuous motion state, the recognition is interrupted, and an error is directly prompted; or to re-identify from the current location.

And the displacement identification is used for judging whether the identified object reaches the preset displacement direction and displacement distance, and if not, prompting an error. The recognition object of the displacement recognition includes one of three bone points. Preferably, one skeletal point of the site action is specified. The identification parameters set displacement distance, displacement direction (the displacement direction can be mapped to the positive direction of the X axis, the negative direction of the X axis, the positive direction of the Y axis and the negative direction of the Y axis in the two-dimensional coordinates, and the specific displacement direction does not need to be calculated) and initial amplitude threshold values. The starting amplitude threshold of the displacement is a value in the range of 0 to 1. For example, the starting amplitude threshold may be set to 0.2 and represent that the site action or displacement recognition begins when the displacement of a given bone point exceeds 20% of the set displacement distance. The recognition rules of displacement recognition include an achievement rule and optionally different error rules. The achievement rule of the displacement identification is that the moving direction of the appointed bone point is consistent with the displacement direction set in the identification parameter, and the displacement distance of one continuous motion is more than or equal to the displacement distance set in the identification parameter. Different error rules include that when the displacement of the specified bone point does not exceed the initial amplitude threshold value, the initial action amplitude is not enough; and if the displacement amplitude of the appointed bone point exceeds the initial amplitude threshold value, the moving direction of the appointed bone point is consistent with the displacement direction set in the identification parameter, and the displacement distance of one continuous motion is less than the displacement distance set in the identification parameter, the achievement amplitude is not enough.

And the negative displacement identification is used for judging whether the identified object reaches the preset displacement direction and displacement distance, and if so, prompting an error. Similar to displacement recognition, the recognition object includes one of three bone points. Preferably, one skeletal point of the site action is specified. The identification parameters set displacement distance, displacement direction (the displacement direction can be mapped to the positive direction of the X axis, the negative direction of the X axis, the positive direction of the Y axis and the negative direction of the Y axis in the two-dimensional coordinates) and initial amplitude threshold values. The achievement rule of the negative displacement recognition is that the moving direction of the specified bone point coincides with the displacement direction set in the recognition parameter, the displacement distance of one continuous motion is equal to or greater than the displacement distance set in the recognition parameter, and there is a state in which recognition other than the negative recognition and the hold recognition is currently in progress (in other words, the trajectory or the displacement amplitude is increasing). When the rule is reached, a track error is prompted. In other words, if the recognition object does not move in the displacement direction set by the recognition parameter or the movement distance is greater than the displacement distance set by the recognition parameter during the movement of the body part, no error is indicated.

In the above embodiments, the difficulty factor may be increased, for example, the product of the difficulty factor and the achievement condition for each action may be used as the achievement condition for actions with different difficulties.

The identification item is set for at least one part action of an action, the at least one part action and the identification item of the at least one part action are used as an action file of the action, and the action file and the action number are stored in the standard action database in a correlation mode.

In one embodiment, for a deep squat action, it sets the identification terms for the torso, left leg and right leg. The identification items of the trunk include a hold identification and a displacement identification. In the trunk keeping identification, the identification object is only a first vector from the head center to the spine center of the neck, the parameters of the first vector are set correspondingly, and a standard skeleton point coordinate base of skeleton points of the trunk in the deep squatting process is stored for subsequent matching. When the first vector of the trunk acquired in real time exceeds the threshold value of the first vector, the body is not kept upright, and an error is prompted. Here, due to the characteristics of the trunk, when the first vector from the center of the head to the center of the spine of the neck remains upright, the second vector from the center of the spine of the neck to the center of the spine of the trunk can be generally directly determined to also remain upright, and only a threshold value of one vector is set, so as to reduce the subsequent calculation amount and improve the subsequent real-time error correction efficiency.

In the displacement recognition of the trunk, the recognition object is a skeleton point at the center of the spine of the trunk, and the corresponding recognition parameters are a predetermined displacement distance and a predetermined displacement direction (the direction is the negative direction of the Y axis) of the skeleton point. When the spine center of the torso moves more than a predetermined distance in the negative Y-axis direction, this identification of the motion of the part is indicated. If the spine center of the trunk does not move along the Y-axis negative direction for more than a preset displacement distance, the amplitude of the part motion is not enough.

The left leg is provided with negative displacement recognition for reminding the deep squatting middle knee not to exceed the toe. In the negative displacement recognition of the left leg, the recognition target is a joint point of the left knee, and the recognition parameters are a predetermined displacement distance, a predetermined displacement direction (the direction is the positive X-axis direction), and a start amplitude threshold. When the left knee moves more than a preset displacement distance along the positive direction of the X axis, the prompt shows that the part moves wrongly. When the left knee does not move more than the predetermined displacement distance in the positive X-axis direction, this recognition of the motion of the part is achieved. The identification item of the right leg is the same as that of the left leg, and is not described herein.

In some embodiments, the stages may be divided for each action. For example, for deep squats, squats and uprisals may be divided into two stages. In some embodiments, the movement of the back and forth for squatting, push-up, etc. can be set and identified for only one course in the middle of the back and forth. For example, the setting of the identification item and the identification error correction are only carried out on the action of squatting deeply; the setting of the identification item and the identification error correction are only carried out on the action during the push-up and the push-up, thereby further reducing the calculation amount of the action identification and increasing the real-time performance of the error correction.

Step S120: and dividing the action to be detected into at least one action stage to be detected according to the time of the target action stage of the target action, and forming a matching group by the target action stage and the action stage to be detected with corresponding time.

Specifically, the motion to be detected and a two-dimensional bone motion model or a three-dimensional bone motion model generated by the collected two-dimensional video data or three-dimensional video data are obtained. In one embodiment, the sampling frequency of the real-time acquisition may be equal to the sampling frequency in the standard bone point coordinate library, or the sampling frequency of the real-time acquisition may be greater than the sampling frequency in the standard bone point coordinate library. When the sampling frequency acquired in real time can be greater than the sampling frequency in the standard bone point coordinate base, a plurality of data of the vector in the same time range are matched and calculated with one data in the vector formed by the bone points in the standard bone point coordinate base.

Specifically, for example, the target action is a deep squat, and is divided into two target action stages: squat and rise, the squat time being 2 seconds and the rise time being 2 seconds. According to time, correspondingly dividing the action to be tested into two action stages to be tested: squat down and rise up. And forming a matching group by the target action stage corresponding to squatting and the action stage to be tested, and forming a matching group by the target action stage corresponding to rising and the action stage to be tested.

Step S130: in each matching group, the action stage to be tested is divided into corresponding action of the part to be tested according to the action of the target part in the target action stage, and the action of the part to be tested in the action stage to be tested and the action of the target part in the corresponding target action stage form a part matching group.

For example, the motion phase to be measured is divided into five motion parts of a left arm, a right arm, a left leg, a right leg and a trunk. If the left arm, the right arm, the trunk and a random part of the target action stage are provided with identification items, taking the action of the part to be detected of the left arm and the action of the target part as a part matching group; taking the motion of the part to be measured of the right arm and the motion of the target part as a part matching group; taking the motion of the part to be detected of the trunk and the motion of the target part as a part matching group; the motion of the part to be measured of the random part and the motion of the target part are used as a part matching group.

Step S140: and for each part matching group, at least acquiring an identification item of random part action in target part action, acquiring a vector formed by at least two selected bone points in the action of the part to be detected according to the two-dimensional bone action model, and performing matching calculation on the vector of the random part action and a standard vector formed by corresponding standard coordinates in a standard bone point coordinate library so as to compare the vector with a vector threshold set by the identification parameters to acquire action identification feedback. I.e. identification and error correction according to the content of the different identification items as described in step S110 above.

In one embodiment, each of the fitness videos has a video file, the video file includes a number of a target action in the fitness video and a playing time of the target action, and the step S110 further includes: when the target action is played, searching a standard action database for a target action file of the number of the target action, wherein the target action file and the number of the target action are stored in the standard action database in a correlation manner, and each target action file comprises a target action stage of the target action, a target part action and an identification item corresponding to the target part action.

Step S150: and integrating the action recognition feedback of at least one matching group to obtain the action recognition feedback of the action to be detected.

In some embodiments, the target actions include at least a plurality of target action phases having a sequential order, and in step S150, when the action recognition feedback of the previous target action phase and the corresponding action phase to be tested is that the action is not achieved, the action recognition feedback of the action achieved by the subsequent target action phase and the corresponding action phase to be tested is invalid.

In addition, the present invention does not limit the execution sequence of each step, for example, the step S120 and the step S130 are executed simultaneously, and those skilled in the art can implement more modifications, which are not described herein again.

The identification error correction algorithm is simple in operation and can cope with the identification speed required in the live broadcast scene.

Compared with the prior art, the student fitness video of the student is converted into the student fitness data through the action error correction recognition algorithm and displayed on the coach end equipment, so that the student fitness data of a plurality of students can be displayed on the coach end equipment at the same time, and a coach can visually obtain more student fitness data.

In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which when executed by, for example, a processor, may implement the steps of the live fitness method described in any of the above embodiments. In some possible embodiments, aspects of the present invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present invention described in the live fitness method section above of this specification, when the program product is run on the terminal device.

Referring to fig. 15, a program product 300 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The 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 electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + +, C #, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the tenant computing device, partly on the tenant device, as a stand-alone software package, partly on the tenant computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing devices may be connected to the tenant computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

Engineering programs for performing the operations of the present invention may be built in any combination of one or more programming Integrated Development Environments (IDE), game Development engines, such as Unity3D, Unreal, Visual Studio, and the like.

In an exemplary embodiment of the present disclosure, there is also provided an electronic device, which may include a processor, and a memory for storing executable instructions of the processor. Wherein the processor is configured to perform the steps of the live fitness method of any of the above embodiments via execution of the executable instructions.

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 16. The electronic device 600 shown in fig. 16 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 16, the electronic device 600 is embodied 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 that connects the various system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.

Wherein the memory unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention described in the live fitness method section above in this specification. For example, the processing unit 610 may perform the steps as described in fig. 1.

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

The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 630 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a tenant to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the 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 systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above-mentioned live fitness method according to the embodiments of the present disclosure.

Compared with the prior art, the student fitness video of the student is converted into the student fitness data through the action error correction recognition algorithm and displayed on the coach end equipment, so that the student fitness data of a plurality of students can be displayed on the coach end equipment at the same time, and a coach can visually obtain more student fitness data.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.