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Human motion action analysis method based on time series regression prediction

A technology of regression prediction and human movement, applied in the direction of instruments, biological neural network models, character and pattern recognition, etc., to achieve the effect of high robustness, strong adaptability and high accuracy

Active Publication Date: 2020-04-03
河南财政金融学院 +1
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Problems solved by technology

[0005] In view of the above situation, in order to overcome the defects of the prior art, the purpose of the present invention is to provide a human body movement analysis method based on time series regression prediction, which solves the long-term prediction and evaluation of actions in the exercise training process and the excavation of the potential of athletes. The problem

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  • Human motion action analysis method based on time series regression prediction
  • Human motion action analysis method based on time series regression prediction
  • Human motion action analysis method based on time series regression prediction

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[0028] For the foregoing and other technical contents, features and effects of the present invention, please refer to the attachment below Figure 1 to Figure 5 It will be clearly presented in the detailed description of the embodiments. The structural content mentioned in the following embodiments are all referenced to the drawings in the specification.

[0029] A human motion analysis method based on time series regression prediction, which mainly includes rope skipping video collection, video data preprocessing, coordinate point acquisition, feature sequence construction, regression prediction model establishment, and model invocation. The specific steps are as follows:

[0030] Step 1: Collect the video stream data of the human body's frontal shaking and double-foot jump through the mobile phone, record and save the basic information of the target object, including name, gender, age, height, weight, and name of the action;

[0031] Step 2: Preprocess the video data, and estimate...

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Abstract

The invention discloses a human motion action analysis method based on time series regression prediction. The human motion action analysis method mainly comprises the steps of rope skipping video acquisition, video data preprocessing, coordinate point acquisition, feature sequence construction, regression prediction model establishment and model calling, collects video stream data in the process of forward shaking and double-leaping of a human body through a mobile phone, records and stores basic information of the target object, including names, genders, ages, heights, weights and made motionnames, constructs a regression prediction model by means of a deep neural network, solves the long-time motion analysis and motion potential discovery problems in the motion training process, and provides a reference basis for motion analysis and training strategy proposal in the motion process.

Description

Technical field [0001] The invention relates to the technical field of human movement analysis, in particular to a human movement analysis method based on time series regression prediction. Background technique [0002] In recent years, with the development and application of computer technology and machine learning, video-based time-series human motion analysis technology has rapidly emerged and has received widespread attention. At present, human motion analysis based on video time series is still a very challenging subject in computer vision, involving multiple subjects such as image processing, pattern recognition, and machine learning. It is used in intelligent monitoring, human-computer interaction, rehabilitation sports, and sports training. Evaluation and other fields have broad application prospects. [0003] Time series, also called dynamic series, is a series of numbers formed by the research objects arranged in the order of time. It is also the correspondence of the re...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06N3/049G06V40/23G06N3/045G06F18/214
Inventor 赵红领李润知崔莉亚刘皓东王菁
Owner 河南财政金融学院
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