Intellectual analysis system for high jump technique

Abstract

An intellectual analysis system for high jump technique includes an analysis device 10, which integrates images and data collected by a drone 20, a side camera 30, a force detecting plate 40 and an el

Description

Background of the Disclosure 1. Field of the Disclosure

[0001] The present invention relates generally to an intellectual analysis system for high jump technique, and more particularly to one that uses a drone, a side camera, a forcedetecting plate, and an electromyography device to generate readily readable data charts through analysis, which enables precise adjustment of an athlete's physical movements and training methods for high jump. 2. Description of the Prior Art

[0002] High jump in track and field is a competitive sport that involves jumping over a horizontal bar to measure the height achieved. Participants utilize an appropriate running velocity on the jumping area, along with a single-leg takeoff, to clear the high jump bar. In the Fosbury flop technique, the final steps of the approach run are curved in an arc, and the correct execution of this curved approach run is crucial for the takeoff action. Through this type of approach run, athletes generate sufficient horizontal velocity and convert it into upward kinetic energy through the takeoff foot's pushing action against the ground. Simultaneously, athletes need to coordinate the bending angle of the takeoff foot's knee, as well as the upward movement of the non-takeoff foot and arms, while rotating and twisting around the body's longitudinal axis. By executing these instantaneous body movements, athletes can convert horizontal velocity into vertical velocity and clear the high jump bar during the clearance phase, thus achieving their desired high jump performance. It is important to note that each movement is interconnected, and it is not simply about maximizing horizontal velocity or pushing force. On the contrary, these factors should be coordinated with the athlete's overall explosive muscle power and the fluidity of their body movements, in order to meet the requirements of overall coordination and achieve better high jump results.

[0003] The conventional teaching method of high jump in track and field follows the principles of sports instruction. It breaks down the complete technique into multiple independent teaching segments and then combines them one by one to form the complete technique. For example, learning arc running technique, approach and takeoff technique, takeoff technique, over bar technique, and landing technique. Each technique can be further divided into several practice steps. Typically, we use a few instructional methods for repetitive practice, and if incorrect movements occur, they are corrected by experienced coaches. Therefore, in traditional teaching, coaches play a dominant role. If coaches cannot effectively identify the key problems, it will take a lot of time to engage in less effective foundational training.

[0004] To overcome the shortcomings of the conventional teaching method, many training processes incorporate video recording. After training, the videos can be played in slow motion for repeated viewing, allowing coaches and athletes to more accurately identify problems. However, solely relying on video observation still cannot fully discover all the problems, especially in sports with numerous movement segments like high jump, where this method appears to be insufficiently scientific and comprehensive. The experience of coaches still accounts for a significant proportion of teaching outcomes. This is precisely the problem that this invention aims to improve. We primarily collect more comprehensive images and data through various scientific methods, reducing reliance on coach experience, and making it easier for athletes to achieve their training goals.

[0005] In light of this, the inventor, with many years of experience in manufacturing, development, and design of related products, has meticulously designed and carefully evaluated in relation to the above-mentioned goals. As a result, this invention has finally achieved practicality and feasibility. Summary of the Disclosure

[0006] The technical problem that the present invention is intended to solve is the above-mentioned shortcomings of the prior art, and therefore the present invention provides an intellectual analysis system for high jump technique.

[0007] An analysis system includes an analysis device, a drone, a side camera, a force-detecting plate, and an electromyography device. The drone is linked to the analysis device, and is adapted to shoot images above an athlete, wherein the analysis device identifies a dynamic position of a head of the athlete through an image recognition method, and the analysis device generates a linear approach velocity, an curved approach trajectory, and a horizontal mid-air displacement trajectory based on the head of the athlete. The side camera is linked to the analysis device and installed on a lateral side of a high jump bar for image shooting, wherein the analysis device identified a relative position of the athlete and the high jump bar through the image recognition method, and obtains a vertical height of the athlete through analysis. The force-detecting plate is linked to the analysis device and placed at a ground area in front of the high jump bar, where in the force-detecting plate obtains a ground-pushing pressure and a pushing-against-ground time point when a takeoff foot of the athlete pushes against a ground, and the analysis device determines that the pushing-against-ground time point is a start point of the horizontal mid-air displacement trajectory. The electromyography device is linked to the analysis device, and is wirelessly connected to a first electromyography unit, a second electromyography unit, and a third electromyography unit, wherein the first electromyography unit is fixed to a posterior muscle of a thigh of the takeoff foot of the athlete, the second electromyography unit is fixed to a posterior muscle of a lower leg of the takeoff foot of the athlete, and the third electromyography unit is fixed to an anterior muscle of the thigh of the takeoff foot of the athlete, the electromyography device generates electromyography signals since the pushing-against-ground time point, and the analysis device analyses the electromyography signals to obtain a force generation sequence and a force-lasting period while the takeoff foot of the athlete pushes against the ground. The analysis device is adapted to further convert the linear approach velocity, the curved approach trajectory, the horizontal mid-air displacement trajectory, the force generation sequence, the force-lasting period, and the ground-pushing pressure into a data graph.

[0008] In an embodiment, the analysis device is linked to a data aggregation platform set up on the internet, and the data aggregation platform utilizes an loT (Internet of Things) technique to gather and analyze body characteristic data of multiple athletes and data graphs generated during their trainings, whereby any one of the athletes is allowed to find an optimal decision model corresponding to the body characteristic data of their own; the body characteristic data of the athletes includes gender, length of each limb, height, weight, age, training age, best performance, and dominant foot.

[0009] In an embodiment, the analysis device includes a display interface; the display interface shows a cross mark therein, and the side camera is installed with the cross mark align with the high jump bar; the analysis device is adapted to combine the image recognition method and a display ratio calculation to obtain a vertical height of the athlete.

[0010] In an embodiment, the analysis device includes a display interface; the display interface shows a horizontal scale and a vertical scale; when the drone shoot images in air, the drone utilized the image recognition method to look for a position of the high jump bar, and automatically keeps the horizontal scale of the display interface to overlap with the high jump bar; furthermore, the analysis device obtains data of the linear approach velocity, the curved approach trajectory, and the horizontal mid-air displacement trajectory by analyzing the horizontal scale and the vertical scale.

[0011] In an embodiment, the first electromyography unit, the second electromyography unit, and the third electromyography unit of the electromyography device are installed on a sock, wherein the sock is adapted to be wom in a manner that the first electromyography unit, the second electromyography unit and the third electromyography unit directly contact skin areas corresponding to the muscles of the takeoff foot.

[0012] In an embodiment, the first electromyography unit, the second electromyography unit, and the third electromyography unit of the electromyography device are respectively fixed to adhesive patches, and are directly attached to skin areas corresponding to the muscles of the takeoff foot through the adhesive patches.

[0013] In an embodiment, the electromyography device further includes a fourth electromyography unit and a fifth electromyography unit; the fourth electromyography unit is provided at a waist of the athlete, and is adapted to measure a rotation status of the athlete after the takeoff foot pushing against the ground; the fifth electromyography unit is provided at an arm of the athlete on a side opposite to that of the takeoff foot, and is adapted to measure how the arm is being lifted after the takeoff foot of the athlete pushing against the ground.

[0014] In an embodiment, the fourth electromyography unit is attached to the waist of the athlete in an adhesive manner, and the fifth electromyography unit is provided on an elastic arm band.

[0015] In an embodiment, the intellectual analysis system further includes a head band, wherein the head band is provided with a LED light, which assists the drone to capture the dynamic position of the head of the athlete.

[0016] In an embodiment, the intellectual analysis system further includes a gyroscope, wherein the gyroscope is linked to the analysis device, and is fixed to a non-takeoff foot of the athlete, whereby to obtain a lifting velocity of the non-takeoff foot after the takeoff foot pushing against the ground.

[0017] Other purposes, advantages and novel features of the present invention will be more apparent from the following detailed description with related accompanying drawings. Brief Description of the Drawings

[0018] The present invention will be best understood by referring to the following detailed description of one illustrative embodiment in conjunction with the accompanying drawings, in which

[0019] FIG. 1 is a schematic view showing the structure of the present invention;

[0020] FIG. 2 is a schematic view showing where the electromyography device is installed;

[0021] FIG. 3 is a schematic view showing an image taken by the drone;

[0022] FIG. 4 is a schematic view showing an image taken by the side camera;

[0023] FIG. 5 is a schematic view showing a measuring display of the electromyography device;

[0024] FIG. 6 is a schematic view showing a data graph; and

[0025] FIG. 7 is a schematic view showing another physical implementation of the present invention. Detailed Description

[0026] To facilitate the understanding of those skilled in the art, the present invention will be further described below with embodiments and accompanying drawings. The contents mentioned in the embodiments are not intended to limit the present invention.

[0027] As shown in FIG. 1 to FIG. 6, an intellectual analysis system for high jump technique includes an analysis device 10, a drone 20, a side camera 30, a force-detecting plate 40, and an electromyography device 50, wherein a drone 20 is linked to the analysis device 10, and is adapted to shoot images (or videos) above a head of the athlete and transmit the images to the analysis device 10. After that, the analysis device 10 utilizes an image recognition method to mark a position of the head of the athlete in every frame of the images or videos, whereby the analysis device 10 could identify a dynamic position of the head of the athlete through the image recognition method. In this way, the analysis device 10 could calculate or depict a linear approach velocity, an curved approach trajectory, and a horizontal mid-air displacement trajectory based on the dynamic position of the head of the athlete. The linear approach velocity and curved approach trajectory are horizontal velocities generated by the athlete during their approach run, followed by a smooth transition from ground contact to takeoff. After takeoff, these horizontal velocities are converted into vertical velocity, allowing the athlete to execute their movements in mid-air. However, excessive horizontal velocity and lack of smoothness in movements can impede the complete conversion of horizontal velocity into vertical velocity, resulting in horizontal mid-air displacement. Therefore, the analysis device 10 in conjunction with the drone 20 could capture the linear approach velocity, the curved approach trajectory, and the horizontal mid-air displacement. This enables a general assessment of whether the athlete successfully convert their horizontal velocity into vertical velocity, as well as whether they require assistance in adjusting their stride length and speed variations during both linear and curved approaches.

[0028] The side camera 30 is linked to the analysis device 10 and is installed on a lateral side of a high jump bar A for shooting images or videos. The side camera 30 transmits the images or videos to the analysis device 10, and then the analysis device 10 utilizes the image recognition method to analyze a position change of the torso of the athlete through the frames of the images or videos, whereby to capture a highest position of the athlete above the high jump bar A. In other words, the analysis device 10 utilizes the image recognition method to identify a relative position of the athlete and the high jump bar A and obtains a vertical height through analysis. The vertical height is the primary condition whether the athlete cleared the high jump bar A. Therefore, by using the side camera 30 to obtain a precise vertical height, it would be able to straight-forwardly determine the improvement in height after adjusting the velocities, movements, or torso positions, whereby to justify the training results and to make adjustments.

[0029] The force-detecting plate 40 is linked to the analysis device 10 and is placed on a ground area in front of the high jump bar A, allowing the athlete to perform their ground-pushing movement on the force-detecting plate 40. The force-detecting plate 40 obtains a ground-pushing pressure and a pushing-against-ground time point generated by the takeoff foot of the athlete, wherein the ground-pushing pressure is the key factor of converting the horizontal velocity into the vertical velocity. Increasing the ground-pushing force could directly improve the vertical height and reduce the horizontal mid-air displacement; however, this would also increase the reaction force exerted on the takeoff foot. Furthermore, the smoothness and collaboration of torso movements while takeoff should be also taken into consideration. Therefore, the optimal ground-pushing pressure could only be determined by considering additional data conditions. In addition, the analysis device 10 could use the pushing-against-ground time point as a start point of the horizontal mid-air displacement trajectory, reducing noises to make the analysis device 10 generate more accurate data.

[0030] The electromyography device 50 is linked to the analysis device 10 and is wirelessly connected to a first electromyography unit 51, a second electromyography unit 52, and a third electromyography unit 53, wherein the first electromyography unit 51 is fixed to a posterior muscle of a thigh of the takeoff foot of the athlete, the second electromyography unit 52 is fixed to a posterior muscle of a lower leg of the takeoff foot of the athlete, and the third electromyography unit 53 is fixed to an anterior muscle of the thigh of the takeoff foot of the athlete. The electromyography device 50 starts to generate electromyography signal since the pushing-against-ground time point, and so-called electromyography signals are biological electrical signals (potential differences) that record muscle activity, obtained by guiding electrodes on the surface of the muscles. These signals are then amplified and analogized to generate the resulting electromyography signals. The analysis device 10 analyzes the electromyography signals to obtain a force generation sequence and a force-lasting period while the takeoff foot of the athlete pushes against the ground. In other words, the force generation sequence is the time points when the first electromyography unit 51, the second electromyography unit 52 and the third electromyography unit 53 generate signals, and the force-lasting period is the periods when the first electromyography unit 51, the second electromyography unit 52, and the third electromyography unit 53 keeps generating signals. In practical use, different parts of the muscles of the athlete would sequentially generate force after the athlete completes their approaches, and the takeoff would be completed with the sudden force of the takeoff foot. Different force generation sequences and different force-lasting periods would subtly affect the smoothness of the movements and force transition after takeoff. Based on experiments, an optimal force generation sequence would be the first electromyography unit 51, the second electromyography unit 52, and the third electromyography unit 53, while an optimal force-lasting period is that the second electromyography unit 52 lasts the longest and the first electromyography unit 51 lasts the shortest. Through the use of the electromyography device 50, it is possible to analyze in detail the force exerted by the athlete during pushing against the ground. Based on this analysis, muscle force adjustments could be made and targeted training for specific muscle groups could be conducted. The training plan could be further enhanced by incorporating data obtained from the drone 20, the side camera 30, and the force-detecting plate 40.

[0031] Please refer to FIG. 2 and FIG. 7, the first electromyography unit 51, the second electromyography unit 52 and the third electromyography unit 53 of the electromyography device 50 are installed on a sock 56, wherein the athlete wears the sock 56 so that the first electromyography unit 51, the second electromyography unit 52, and the third electromyography unit 53 directly contact skin areas corresponding to the muscles of the takeoff foot, which is convenient and could be reusable. Alternatively, the first electromyography unit 51, the second electromyography unit 52, and the third electromyography unit 53 of the electromyography device 50 are all respectively fixed to adhesive patches 57, and the electromyography units 51-53 are directly attached to the skin areas corresponding to the muscles of the takeoff foot through the adhesive patches 57. This allows for precise installation of the electromyography units 51-53, while also reducing discomfort for the athlete.

[0032] As shown in FIG. 7, the electromyography device 50 further includes a fourth electromyography unit 54 and a fifth electromyography unit 55, wherein the fourth electromyography unit 54 is attached to a waist of the athlete and measures a rotation status of the athlete after the takeoff foot pushing against the ground. The fifth electromyography unit 55 is provided on an arm on a side opposite to that of the takeoff foot of the athlete with an elastic arm band 551, and measures how the arm is being lifted after the takeoff foot of the athlete pushing against the ground. Right after the athlete takeoffs by pushing against the ground, they should immediately lift legs, rotate the body, and lift the arms, so that the athlete could successfully convert the horizontal velocity into the vertical velocity. Therefore, by additional detecting the electromyography signals of the waist and the arm through the fourth electromyography unit 54 and the fifth electromyography unit 55, it would be possible to further analyze whether the next movement is immediately performed right after takeoff, and whether the rotation of the body and the lifting of the arms need to take further trainings. Furthermore, the elastic arm band 551 could be easily put on. In summary, the analysis device 10 incorporates the images and data of the drone 20, the side camera 30, the force-detecting plate 40, and the electromyography device 50, and the analysis device 10 coverts the linear approach velocity, the curved approach trajectory, the horizontal mid-air displacement trajectory, the force generation sequence, the force-lasting period, and the ground-pushing pressure into data graphs. With these data graphs, the vertical height of the athlete could be precisely improved, and the physical movements and the training methods of the athlete for high jump in track and field could be accurately adjusted.

[0033] Please refer to FIG. 1 to FIG. 6, the analysis device 10 includes a display interface 11, wherein the display interface 11 shows a horizontal scale 12 and a vertical scale 13. When the drone 20 shoots images in air, the image recognition method is used to look for a location of the high jump bar A, and the drone 20 would automatically keep the horizontal scale 12 of the display interface 11 overlaps with the high jump bar A. With a length ratio of the high jump bar A, the analysis device 11 could analyze the horizontal scale 12 and the vertical scale 13 to obtain the digital data of the linear approach velocity, the curved approach trajectory, and the horizontal mid-air displacement trajectory. The display interface 11 of the analysis device 10 shows a cross mark 14 therein, and the side camera 30 is installed with the cross mark 14 aligned with the high jump bar A. The analysis device 10 combines the image recognition method and the vertical height of the athlete obtained with the display ratio calculation. In summary, the linear approach velocity of the athlete could be calculated based on time, the change of the position of the athlete, and the display ratio; furthermore, the curved approach trajectory, the horizontal mid-air displacement trajectory, and the vertical height could be converted into corresponding values in a manner referring to the display ratio, resulting in easily interpretable image data.

[0034] As shown in FIG. 1 and FIG. 7, the drone 20 further includes a head band 21, and the head band 21 is provided with a LED light 22, which assists the drone 20 to shoot the dynamic position of the head of the athlete, whereby to enhance the identification of the moving trajectory. The analysis device 10 is further linked to a gyroscope 60, which is fixed to a non-takeoff foot of the athlete to measure relative swing degrees in X, Y, and Z axes of the non-takeoff foot of the athlete while doing high jump, whereby to obtain a lifting velocity of the non-takeoff foot after the takeoff foot pushing against the ground.

[0035] As shown in FIG. 1, the analysis device 10 is linked to a data aggregation platform 15 set up on the internet, and the data aggregation platform 15 utilizes an loT (Internet of Things) technique to gather and analyze body characteristic data of multiple athletes and data graphs generated during their trainings, whereby any one of the athletes is allowed to find an optimal decision model corresponding to the body characteristic data of their own. The body characteristic data of the athletes includes gender, length of each limb, height, weight, age, training age, best performance, and dominant foot, so that any one of the athletes could find the most suitable high jump method for their selves. On the other hand, the data aggregation platform 15 records every high jump record of the athletes. Through the analysis and comparison of these high jump records, it is possible to determine the improvement effect of each foundational training session on the athletes. The analysis system takes into account both efficient training and progressive enhancement.

Claims

1. An intellectual analysis system for high jump technique, comprising:an analysis device (10);a drone (20), which is linked to the analysis device (10), and is adapted to shoot images above an athlete, wherein the analysis device (10) identifies a dynamic position of a head of the athlete through an image recognition method, and the analysis device (10) generates a linear approach velocity, an curved approach trajectory, and a horizontal mid-air displacement trajectory based on the head of the athlete;a side camera (30) linked to the analysis device (10) and installed on a lateral side of a high jump bar (A) for image shooting, wherein the analysis device (10) identified a relative position of the athlete and the high jump bar (A) through the image recognition method, and obtains a vertical height of the athlete through analysis;a force-detecting plate (40) linked to the analysis device (10) and placed at a ground area in front of the high jump bar (A), where in the force-detecting plate (40) obtains a ground-pushing pressure and a pushing-against-ground time point when a takeoff foot of the athlete pushes against a ground, and the analysis device (10) determines that the pushing-against-ground time point is a start point of the horizontal mid-air displacement trajectory; andan electromyography device (50), which is linked to the analysis device (10), and is wirelessly connected to a first electromyography unit (51), a second electromyography unit (52), and a third electromyography unit (53), wherein the first electromyography unit (51) is fixed to a posterior muscle of a thigh of the takeoff foot of the athlete, the second electromyography unit (52) is fixed to a posterior muscle of a lower leg of the takeoff foot of the athlete, and the third electromyography unit (53) is fixed to an anterior muscle of the thigh of the takeoff foot of the athlete, the electromyography device (50) generates electromyography signals since the pushing-against-ground time point, and the analysisdevice (10) analyses the electromyography signals to obtain a force generation sequence and a force-lasting period while the takeoff foot of the athlete pushes against the ground;wherein the analysis device (10) is adapted to further convert the linear approach velocity, the curved approach trajectory, the horizontal mid-air displacement trajectory, the force generation sequence, the force-lasting period, and the ground-pushing pressure into a data graph.

2. The intellectual analysis system of claim 1, wherein the analysis device (10) is linked to a data aggregation platform (15) set up on the internet, and the data aggregation platform (15) utilizes an loT (Internet of Things) technique to gather and analyze body characteristic data of multiple athletes and data graphs generated during their trainings, whereby any one of the athletes is allowed to find an optimal decision model corresponding to the body characteristic data of their own; the body characteristic data of the athletes includes gender, length of each limb, height, weight, age, training age, best performance, and dominant foot.

3. The intellectual analysis system of claim 1, wherein the analysis device (10) comprises a display interface (11); the display interface (11) shows a cross mark (14) therein, and the side camera (30) is installed with the cross mark (14) align with the high jump bar (A); the analysis device (10) is adapted to combine the image recognition method and a display ratio calculation to obtain a vertical height of the athlete.

4. The intellectual analysis system of claim 1, wherein the analysis device (10) comprises a display interface (11); the display interface (11) shows a horizontal scale (12) and a vertical scale (13); when the drone (20) shoot images in air, the drone (20) utilized the image recognition method to look for a position of the high jump bar (A), and automatically keeps the horizontal scale (12) of the display interface (11) to overlap with the high jump bar (A); furthermore, the analysis device (10) obtains data of the linear approach velocity, the curved approach trajectory, and the horizontal mid-air displacement trajectory by analyzing the horizontal scale (12) and the vertical scale (13).

5. The intellectual analysis system of claim 1, wherein the first electromyography unit (51), the second electromyography unit (52), and the third electromyography unit (53) of the electromyography device (50) are installed on a sock (56), wherein the sock (56) is adapted to be worn in a manner that the first electromyography unit (51), the second electromyography unit (52) and the third electromyography unit (53) directly contact skin areas corresponding to the muscles of the takeoff foot.

6. The intellectual analysis system of claim 1, wherein the first electromyography unit (51), the second electromyography unit (52), and the third electromyography unit (53) of the electromyography device (50) are respectively fixed to adhesive patches (57), and are directly attached to skin areas corresponding to the muscles of the takeoff foot through the adhesive patches (57).

7. The intellectual analysis system of claim 1, wherein the electromyography device (50) further comprises a fourth electromyography unit (54) and a fifth electromyography unit (55); the fourth electromyography unit (54) is provided at a waist of the athlete, and is adapted to measure a rotation status of the athlete after the takeoff foot pushing against the ground; the fifth electromyography unit (55) is provided at an arm of the athlete on a side opposite to that of the takeoff foot, and is adapted to measure how the arm is being lifted after the takeoff foot of the athlete pushing against the ground.

8. The intellectual analysis system of claim 7, wherein the fourth electromyography unit (54) is attached to the waist of the athlete in an adhesive manner, and the fifth electromyography unit (55) is provided on an elastic arm band (551).

9. The intellectual analysis system of claim 1, further comprising a head band (21), wherein the head band (21) is provided with a LED light, which assists the drone (20) to capture the dynamic position of the head of the athlete.

10. The intellectual analysis system of claim 1, further comprising a gyroscope (60), wherein the gyroscope (60) is linked to the analysis device (10), and is fixed to a non-takeofffoot of the athlete, whereby to obtain a lifting velocity of the non-takeoff foot after the takeoff foot pushing against the ground.

Images