Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

Xie, Jun; Chen, Guohua; Liu, Shuang

doi:10.3389/fnbot.2021.621196

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…e most direct and primary exercise experience for users of intelligent badminton is the accuracy of dynamic counting [14]. Once there is a counting error in action recognition, sports users' enthusiasm in using the app will be affected, and their participation initiative will be demotivated.…”

Section: Accuracy Recognitionmentioning

confidence: 99%

[Retracted] Design and Research of the AI Badminton Model Based on the Deep Learning Neural Network

Chen

2022

Journal of Mathematics

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In view of the fact that it is difficult for existing algorithms to identify the movements of a player in an accurate way, this paper puts forward an artificial intelligence (AI) motion model on the basis of the deep learning neural network instruction set architecture (ISA). Firstly, a mobile neural network (MNN) inference engine was utilized to create a new AI sports project-side intelligent practice model. Under this model, a movement can be segmented into a series of decomposition movements, which are recognized and judged separately for the purpose of measuring the entire movement. In order to test its feasibility, the study compares the MNN inference engine with the traditional reasoning engine in terms of their algorithmic capabilities and compares the results obtained through this algorithm and traditional online motion app. Research shows that, in the MNN of the AI sports project proposed in this paper, the datasets of action recognition exceed the results of other inference engines, characterized by lightweight, high performance, and accessibility. Research also demonstrates that the AI sports project model can adapt to the needs of sports projects with a variety of themes and improve the accuracy of movement recognition details.

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Section: Accuracy Recognitionmentioning

confidence: 99%

[Retracted] Design and Research of the AI Badminton Model Based on the Deep Learning Neural Network

Chen

2022

Journal of Mathematics

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“…The air force on badminton can actually be divided into three different directional components, respectively, the badminton flight process by the various resistance X, to prevent the badminton due to the influence of gravity and vertical downward landing lift Y and make the badminton deviated the lateral force Z [9]. During flight, the force on the badminton is related to several factors, which are the area of the badminton and the pressure of the air flow [10]. The force on the badminton is positively related to both of these factors, so the following equation can be derived.…”

Section: Analysis Of the Forces During The Flight Of Badmintonmentioning

confidence: 99%

Research on badminton path tracking algorithm based on machine vision

Ye,

Huang,

Chen

2023

International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023)

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“…The linear motion model assumes that the velocity of the objective remains constant. Based on this assumption, the velocity smoothness and position smoothness of the objective are mainly considered when constructing the linear motion model [14].…”

Section: Motion Modelmentioning

confidence: 99%

Analysis of Badminton Movement Cognition Algorithm Based on Track Linear Capture

Wang

Hu²

2022

Wireless Communications and Mobile Computing

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At present, more and more sports science and technology are being explored and applied in competitive sports. The birth and popularization of video tracking and capturing technology have provided more fair and just perspectives for many sports events. Track linear capture can replay the player’s behavior in real time, the flight path of the badminton can be analyzed in 3D stereoscopic analysis, and the ball’s motion trajectory can be calculated more accurately. In this paper, an objective trajectory tracking and prediction model is constructed based on the motion cognition algorithm, and the motion characteristics of the objective are extracted from the limited historical trajectory of the objective to achieve more accurate trajectory tracking. Then, the trajectory tracking model is applied to the objective tracking framework to obtain ideal objective tracking results. At the same time, in order to make use of the interaction between scene information and objective, this paper improves the trajectory tracking model. The trajectory prediction model based on neural network is constructed, which learns the pedestrian motion characteristics from the pedestrian trajectory data of the target tracking scene offline and uses its “memory” online to generate the implicit depth motion characteristics of the target according to the limited historical information of the target. It also predicts the most likely location of the future target and calculates the motion similarity between the targets. Finally, a simulation experiment platform is built to prove the effectiveness of the trajectory tracking model and objective tracking algorithm proposed in this paper. Through the research results of this paper, it can play a role in verifying the referee’s judgment on the penalty of some key balls, which is more conducive to maintaining the fairness of the game, and more helpful for athletes to optimize their exercise results according to scientific basis, and has the function of improving their performance.

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Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

Cited by 11 publications

References 31 publications

[Retracted] Design and Research of the AI Badminton Model Based on the Deep Learning Neural Network

[Retracted] Design and Research of the AI Badminton Model Based on the Deep Learning Neural Network

Research on badminton path tracking algorithm based on machine vision

Analysis of Badminton Movement Cognition Algorithm Based on Track Linear Capture

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