Method for analyzing skiing motion sequence based on hidden Markov analysis

A technology of hidden Markov analysis and skiing sports, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as reducing the dimension of the attitude matrix and reducing the amount of calculation

A technology of hidden Markov analysis and skiing sports, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as reducing the dimension of the attitude matrix and reducing the amount of calculation

CN112347991AActive Publication Date: 2021-02-09BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Method for analyzing skiing motion sequence based on hidden Markov analysis
  • Method for analyzing skiing motion sequence based on hidden Markov analysis
  • Method for analyzing skiing motion sequence based on hidden Markov analysis

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Embodiment Construction

[0075] In order to better illustrate the purpose and advantages of the present invention, the content of the invention will be further described below in conjunction with the accompanying drawings and examples.

[0076] In order to verify the feasibility of the method, this embodiment chooses to collect continuous left and right turning motion posture data in skiing on an indoor skiing simulation platform (SkyTechSport ski&fit).

[0077] like figure 1 As shown, a method for analyzing skiing motion sequences based on Hidden Markov disclosed in this embodiment, the specific implementation steps are as follows:

[0078] Step 1: Add a window to the original attitude data and divide it into data frames on the time series. The original pose data of the human body is stored in the form of a table. The rows in the table represent time frames, and the columns represent body nodes. When the present invention is applied to skiing, the posture is mainly affected by the limbs and trunk, ...

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Abstract

The invention discloses a method for analyzing a skiing motion sequence based on hidden Markov, and belongs to the field of motion attitude data processing and analysis. The implementation method comprises the following steps: windowing original attitude data of a skier acquired by a sensor, dividing the original attitude data into data frames on a time sequence, and extracting a'symbolic value 'representing an attitude matrix by utilizing SVD and a matrix norm; building a probability transfer model of the skiing motion sequence based on hidden Markov, optimizing the probability transfer modelparameters of the skiing motion sequence by using the'symbolic value 'sequence on the time sequence, and calculating the optimal path of the hidden state of the skiing motion posture sequence. The probability of each skiing state at the next moment is calculated through the optimal hidden state path and the probability transfer model, then the skiing state at the next moment is predicted, the motion state of a skier can be evaluated according to the motion posture prediction data of skiing of the skier, and then the training effect on a skiing trainer is improved.

Description

technical field [0001] The invention relates to a method for analyzing skiing motion sequences based on Hidden Markov, and belongs to the field of motion posture data processing and analysis. Background technique [0002] The 24th Winter Olympic Games will be held in my country in 2022, and people's enthusiasm for ice and snow sports continues to rise. Whether it is an amateur or a professional athlete, it is necessary to obtain the action sequence during the skiing process during training, so as to analyze and guide their skiing process. The most important technology in the field of obtaining human motion posture sequences is motion capture technology. Many scholars at home and abroad have conducted a lot of research on obtaining human body motion posture sequences using existing motion capture technologies. The current mainstream motion capture methods are divided into There are three kinds of photoelectric capture, image capture and inertial sensor capture. In the field...

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Application Information

Patent Timeline
09 Feb 2021
Publication
CN112347991A
IPC
G06K9/00; G06K9/46; G06K9/62; G06N3/04; G06N3/08
CPC
G06N3/08; G06V30/2276; G06V10/44; G06N3/045; G06F18/2415
Inventors
费庆; 李佩璋