Skiing motion real-time posture recognition feedback system based on multi-source sensor

Abstract

The invention discloses a skiing motion real-time posture recognition feedback system based on a multi-source sensor, which comprises a snowboard and a skiing helmet, a boot is mounted on the skiing helmet, two pressure sensors are arranged at the bottom of the boot, a posture sensor and a speed sensor are arranged on the snowboard, and the pressure sensors are connected with the pressure sensors. A camera, a central processing unit and a loudspeaker are arranged on the side wall of the skiing helmet, the signal output ends of the attitude sensor, the pressure sensor, the speed sensor and the camera are all in electric signal connection with the signal input end of the central processing unit, and the signal output end of the central processing unit is in electric signal connection with the signal input end of the loudspeaker. In the skiing process, according to the recognized camber of the racing track and the posture speed of the skier, whether standard turning can be achieved or not is automatically predicted, an athlete is prompted through voice in advance, suggestions are given, adjustment can be conducted in time in the movement process, video analysis after racing is not needed, and the analysis efficiency is better.

Description

Technical Field

[0001] The present invention relates to the technical field of sports equipment, and in particular to a real-time posture recognition and feedback system for skiing based on multi-source sensors. Background Art

[0002] The intelligence of skiing equipment helps to enhance the fun of skiing and assist skiers in training. Therefore, it is of great significance to evaluate the action quality of skiers through artificial intelligence technology and multi-source sensor technology.

[0003] The related invention results that have been publicly disclosed mainly focus on the solutions of built-in sensing systems for skiing equipment. However, there are relatively few invention results on how to automatically predict whether a skier can turn in a standard manner during skiing by using the data captured by the sensing system. Summary of the Invention

[0004] The present invention provides a real-time posture recognition and feedback system for skiing based on multi-source sensors to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A real-time posture recognition and feedback system for skiing based on multi-source sensors includes a ski board and a ski helmet. Boots are installed on the ski helmet. Two pressure sensors are provided at the bottom of the boots. An attitude sensor and a speed sensor are provided on the ski board. A camera, a central processor, and a speaker are provided on the side wall of the ski helmet. The signal output ends of the attitude sensor, the pressure sensor, the speed sensor, and the camera are electrically connected to the signal input end of the central processor. The signal output end of the central processor is electrically connected to the signal input end of the speaker;

[0007] A curve skiing feedback unit is provided in the central processor. The curve skiing feedback unit can actively predict whether a skier can complete a curve in a standard posture. If not, a suggestion is sent to the skier through the speaker.

[0008] Preferably, the two pressure sensors are distributed and installed at the toe and heel of the boots.

[0009] Preferably, the construction method of the curve skiing feedback unit is as follows:

[0010] First step, obtain a data set composed of a large number of skiers under different skiing curves. Each sample in the data set includes five indicators: skiing speed, skiing posture, center of gravity position, snow track curvature, and whether it is possible to pass the curve in a standard skiing posture;

[0011] Step 2: Train a machine learning model using the dataset. The machine learning model takes four metrics, namely the standardized skiing speed, skiing posture, center of gravity position, and snow track curvature, as inputs, and takes whether it is possible to turn with a standard skiing posture as the output. Through supervised learning, a curve skiing feedback unit is obtained.

[0012] Preferably, in the first step, the dataset is divided into a training set and a validation set. Multiple machine learning models are trained simultaneously using the training set, and the performance accuracy of different machine learning models is evaluated using the validation set. The machine learning model with the best performance is selected to construct a snow melting rate prediction model.

[0013] Preferably, in the second step, the selected machine learning model is at least one of logistic regression, linear discriminant analysis, K-nearest neighbor, naive Bayes, support vector machine, random forest, and neural network.

[0014] Preferably, for the target athlete whose skiing curve posture needs to be determined, before turning on the snow track, first measure four metrics, namely skiing speed, skiing posture, center of gravity position, and snow track curvature. After standardizing them, input them into the curve skiing feedback unit to obtain a prediction result of whether it is possible to turn with a standard skiing posture. If not, a suggestion is sent to the skier through a speaker.

[0015] Preferably, the suggestions sent to the skier through the speaker include: pay attention to decelerating, pay attention to decelerating, adjust the center of gravity position.

[0016] Preferably, the skiing speed and skiing posture are respectively detected in real time by a speed sensor and a posture sensor and fed back to the central processing unit.

[0017] Preferably, the camera identifies the snow track curvature by taking images of the snow track and feeds it back to the central processing unit.

[0018] Preferably, the center of gravity position is detected by two pressure sensors installed at the toe and heel of the boot. The central processing unit detects whether the center of gravity position is at the toe or the heel in real time according to the comparison of the pressure magnitudes of the two pressure sensors at the toe and heel.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] During the skiing process of the present invention, according to the identified curvature of the track and the posture and speed of the skier himself, it automatically predicts whether it is possible to turn standardly, and gives a voice prompt and suggestions to the athlete in advance, so that adjustments can be made in time during the movement process, without the need to analyze the video after the game, and the analysis efficiency is better.

[0021] The above description is only an overview of the technical solution of the present invention. In order to understand the technical means of the present invention more clearly and be implemented in accordance with the content of the specification, the following describes the present invention in detail with reference to the preferred embodiments of the present invention and the accompanying drawings. The specific implementation manners of the present invention are given in detail by the following embodiments and their accompanying drawings. Brief Description of the Drawings

[0022] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0023] Figure 1 is a schematic structural diagram of a snowboard and a ski helmet proposed by the present invention;

[0024] Figure 2 is a schematic diagram of a real-time posture recognition and feedback system for skiing based on multi-source sensors proposed by the present invention;

[0025] Figure 3 is a schematic diagram of a method for constructing a curve skiing feedback unit proposed by the present invention.

[0026] In the drawings, the list of components represented by each reference numeral is as follows:

[0027] 1, snowboard; 2, boots; 3, camera; 4, ski helmet; 5, attitude sensor; 6, pressure sensor; 7, speed sensor; 8, speaker; 9, central processing unit. Detailed Description of the Preferred Embodiments

[0028] The principles and features of the present invention will be described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention. In the following paragraphs, the present invention will be described more specifically by way of example with reference to the accompanying drawings. It should be noted that the drawings are all in a very simplified form and use non-precise scales, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0030] Please refer to Figures 1 to 3, in the embodiment of the present invention, a real-time posture recognition and feedback system for skiing based on multi-source sensors includes a ski board 1 and a ski helmet 4. A boot 2 is installed on the ski helmet 4. Two pressure sensors 6 are provided at the bottom of the boot 2, and the two pressure sensors 6 are distributed and installed at the toe and heel of the boot 2. An attitude sensor 5 and a speed sensor 7 are provided on the ski board 1. A camera 3, a central processor 9, and a speaker 8 are provided on the side wall of the ski helmet 4. The signal output ends of the attitude sensor 5, the pressure sensor 6, the speed sensor 7, and the camera 3 are electrically connected to the signal input end of the central processor 9, and the signal output end of the central processor 9 is electrically connected to the signal input end of the speaker 8;

[0031] A curve skiing feedback unit is provided in the central processor. The curve skiing feedback unit can actively predict whether a skier can complete a curve in a standard posture. If not, a suggestion is sent to the skier through the speaker 8;

[0032] The construction method of the curve skiing feedback unit is as follows:

[0033] First step: Obtain a data set composed of a large number of skiers under different ski curves. Each sample in the data set includes five indicators: skiing speed, skiing posture, center of gravity position, snow track curvature, and whether it is possible to pass the curve in a standard skiing posture. Among them, the data set is divided into a training set and a validation set. Multiple machine learning models are trained simultaneously using the training set, and the performance accuracy of different machine learning models is evaluated using the validation set. The machine learning model with the best performance is selected to construct a snow melting rate prediction model;

[0034] Second step: Use the data set to train the machine learning model. The machine learning model takes four indicators of skiing speed, skiing posture, center of gravity position, and snow track curvature after standardization as inputs, and whether it is possible to pass the curve in a standard skiing posture as the output. The curve skiing feedback unit is obtained through supervised learning. The selected machine learning model is at least one of logistic regression, linear discriminant analysis, K-nearest neighbor, naive Bayes, support vector machine, random forest, and neural network.

[0035] Among them, the skiing speed and skiing posture are respectively detected in real time by the speed sensor 7 and the attitude sensor 5 and fed back to the central processor 9; the camera 3 identifies the snow track curvature by taking pictures of the snow track and feeds it back to the central processor 9;

[0036] The center of gravity position is detected by two pressure sensors 6 installed at the toe and heel of the boot 2. The central processor 9 detects whether the center of gravity position is at the toe or the heel in real time according to the comparison of the pressure magnitudes of the two pressure sensors 6 at the toe and heel.

[0037] For a target athlete who needs to determine whether the skiing posture is standard, before turning on the snow track, first measure four indicators: skiing speed, skiing posture, center of gravity position, and snow track curvature. After standardizing them, input them into the curved skiing feedback unit to obtain a prediction result on whether it is possible to turn with a standard skiing posture. If not, advice will be sent to the skier through the speaker 8. The advice sent to the skier through the speaker 8 includes: pay attention to decelerating, pay attention to decelerating, adjust the center of gravity position.

[0038] As described above, it is only the preferred embodiment of the present invention and does not impose any form of limitation on the present invention. Any ordinary technician in the industry can smoothly implement the present invention as shown in the accompanying drawings of the specification and described above. However, any equivalent changes such as slight modifications, decorations, and evolutions made by those skilled in the art within the scope of the technical solution of the present invention using the technical content disclosed above are equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications, and evolutions made to the above embodiments based on the substantial technology of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A real-time posture recognition and feedback system for skiing based on multi-source sensors, characterized in that, It includes a snowboard (1) and a ski helmet (4). A boot (2) is mounted on the ski helmet (4). Two pressure sensors (6) are provided at the bottom of the boot (2). An attitude sensor (5) and a speed sensor (7) are provided on the snowboard (1). A camera (3), a central processor (9), and a speaker (8) are provided on the side wall of the ski helmet (4). The signal output ends of the attitude sensor (5), the pressure sensor (6), the speed sensor (7), and the camera (3) are all electrically connected to the signal input end of the central processor (9). The signal output end of the central processor (9) is electrically connected to the signal input end of the speaker (8); A curve skiing feedback unit is provided in the central processor. The curve skiing feedback unit can actively predict whether a skier can complete a curve in a standard posture. If not, a suggestion is sent to the skier through the speaker (8).

2. The real-time posture recognition and feedback system for skiing based on multi-source sensors according to claim 1, characterized in that The two pressure sensors (6) are distributed and installed at the toe and heel of the boot (2).

3. The real-time posture recognition and feedback system for skiing based on multi-source sensors according to claim 2, wherein, The construction method of the curve skiing feedback unit is as follows: First step: Obtain a dataset composed of a large number of skiers under different ski curves. Each sample in the dataset includes five indicators: skiing speed, skiing attitude, center of gravity position, snow track curvature, and whether it can pass the curve in a standard skiing posture; Second step: Use the dataset to train a machine learning model. The machine learning model takes the four indicators of skiing speed, skiing attitude, center of gravity position, and snow track curvature after standardization as inputs, and whether it can pass the curve in a standard skiing posture as the output, and obtains a curve skiing feedback unit through supervised learning.

4. The real-time posture recognition and feedback system for skiing based on multi-source sensors according to claim 3, characterized in that, In the first step, the dataset is divided into a training set and a validation set. Multiple machine learning models are trained simultaneously using the training set, and the performance accuracy of different machine learning models is evaluated using the validation set. The machine learning model with the best performance is selected to construct a snow melting rate prediction model.

5. The real-time posture recognition and feedback system for skiing based on multi-source sensors according to claim 4, characterized in that In the second step, the selected machine learning model is at least one of logistic regression, linear discriminant analysis, K-nearest neighbor, naive Bayes, support vector machine, random forest, and neural network.

6. The real-time posture recognition and feedback system for skiing based on multi-source sensors according to claim 5, characterized in that, For the target skier who needs to determine whether the skiing curve is in a standard posture, before passing the curve on the snow track, first measure the four indicators of skiing speed, skiing attitude, center of gravity position, and snow track curvature, standardize them, and input them into the curve skiing feedback unit to obtain a prediction result of whether it can pass the curve in a standard skiing posture. If not, a suggestion is sent to the skier through the speaker (8).

7. The real-time posture recognition and feedback system for skiing based on multi-source sensors according to claim 6, characterized in that The suggestions sent to the skier through the speaker (8) include: Pay attention to decelerating, Pay attention to decelerating, Adjust the center of gravity position.

8. A real-time posture recognition and feedback system for skiing based on multi-source sensors according to claim 7, characterized in that, The skiing speed and skiing attitude are respectively detected in real time by the speed sensor (7) and the attitude sensor (5) and fed back to the central processor (9).

9. The real-time posture recognition and feedback system for skiing based on multi-source sensors according to claim 8, characterized in that, The camera (3) identifies the snow track curvature by taking pictures of the snow track and feeds it back to the central processor (9).

10. A real-time skiing posture recognition and feedback system based on multi-source sensors according to claim 9, characterized in that, The position of the center of gravity is detected by two pressure sensors (6) installed at the toe and heel of the boot (2). The central processing unit (9) detects in real time whether the center of gravity position is at the toe or the heel according to the comparison of the pressure magnitudes of the two pressure sensors (6) at the toe and the heel.

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