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Running behavior recognition method based on convolutional neural network and acceleration sensor

A convolutional neural network and acceleration sensor technology, applied in the field of intelligent identification software, can solve problems such as inability to accurately identify replacement running, and achieve the effect of cost advantages, good cost advantages, and high accuracy.

Active Publication Date: 2022-07-29
SOUTHWEST PETROLEUM UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] In order to overcome the defects and deficiencies in the above-mentioned prior art, the present invention provides a running behavior recognition method based on a convolutional neural network and an acceleration sensor. Run and wait for running cheating issues

Method used

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  • Running behavior recognition method based on convolutional neural network and acceleration sensor
  • Running behavior recognition method based on convolutional neural network and acceleration sensor
  • Running behavior recognition method based on convolutional neural network and acceleration sensor

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Embodiment 1

[0086] As a preferred embodiment of the present invention, refer to the accompanying image 3 As shown, this embodiment discloses a running behavior recognition method based on a convolutional neural network and an acceleration sensor, including the following steps:

[0087] S1. After the running starts, obtain the data of the three-axis acceleration sensor in the device carried by the user in real time;

[0088] S2. Sampling the data obtained in step S1 at a set time interval t; continuously collect sampling data in a time period T, and continuously collect sampling data in multiple time periods T, until the running ends;

[0089] S3. Perform fitting and resampling on the sampled data in multiple time periods T continuously collected in step S2 through cubic spline interpolation, and each time period T corresponds to a time series with a fixed length of L, and multiple For multiple time series corresponding to time period T, each time series is taken as a sample data to obta...

Embodiment 2

[0113] As another preferred embodiment of the present invention, refer to the accompanying figure 1 , attached figure 2 and attached image 3 As shown, this embodiment discloses a running behavior recognition method based on a convolutional neural network and an acceleration sensor. In this embodiment, the method is configured as a smart phone App, or a plug-in module running in various college running apps.

[0114] The data of the three-axis acceleration sensor in the smartphone can be acquired in real time. The data consists of a total of 6 features (the x, y, and z-axis components of the two acceleration vectors) including the gravitational acceleration and the two acceleration vectors that do not include the gravitational acceleration. By calculating the modulo length of these two vectors, 2 additional features are obtained and merged with 6 features, the resulting data has a total of 8 features.

[0115] Sampling at a time interval of 50ms, a time series composed of...

Embodiment 3

[0137] As another preferred embodiment of the present invention, this embodiment can be used as a specific application example to the above-mentioned Embodiment 1 and Embodiment 2. After the method is integrated into a mobile phone APP, the specific use process is as follows:

[0138] (1) Students open the APP software and start running and punching in;

[0139] (2) The software starts to collect the current three-axis acceleration data every 50ms, and calculates its modulo lengths respectively to obtain 8 features;

[0140] (3) When a total of 5s of data is collected, a time series data is obtained, and the time series is fitted by the cubic interpolation method and re-sampled to obtain time series data with a length of 64;

[0141] (4) Input the time series into the convolutional neural network SKSNet;

[0142] (5) Record the behavior detection results obtained by the model;

[0143] (6) If the student does not click to finish running, repeat steps (1) to (5);

[0144] (7...

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Abstract

The invention discloses a running behavior recognition method based on a convolutional neural network and an acceleration sensor, and relates to the technical field of intelligent recognition software. According to the running behavior recognition method, sampling is carried out at set time intervals based on data of a three-axis acceleration sensor, data of set duration are continuously collected, fitting and resampling are carried out on the data through a cubic spline interpolation method, and a time sequence with the fixed length is obtained and serves as sample data; processing according to the above mode to obtain a plurality of sample data, and inputting the sample data into a convolutional neural network model to recognize the motion state of the sample data; if the recognition result reaches a set threshold value, similarity comparison is conducted on the running motion state information and data submitted by other users, and whether the errand behavior exists or not is judged. The method can be applied and integrated in running software and is used for judging whether cheating behaviors exist in running clock-in, and it is ensured that college students can complete running clock-in tasks with quality and quantity guaranteed.

Description

technical field [0001] The invention relates to the technical field of intelligent identification software, and more particularly to a running behavior identification method based on a convolutional neural network and an acceleration sensor. Background technique [0002] According to the "China Youth Sports Development Report (2015)", the physical quality of college students is relatively weak and shows a downward trend year by year, and 30% of them cannot meet the requirements of passing the sports test. In order to cope with this problem, many colleges and universities have launched "running punch cards", which use running apps to set time limits and speed limits for running, so as to urge students to exercise to achieve the purpose of enhancing physical fitness. But at the same time, in order to avoid exercising, college students have derived cheating methods in an endless stream, such as using virtual software to locate, clocking in while riding, and clocking in skateboa...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G01P15/18
CPCG06N3/08G01P15/18G06N3/045G06F18/2193G06F18/22G06F18/2415G06F18/253
Inventor 胡瑞婷敬亚霖石峻峰林馨怡翟天泰
Owner SOUTHWEST PETROLEUM UNIV
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