CN111339987B - Body-building data supervisory systems based on cloud calculates - Google Patents
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Abstract
The invention provides a body-building data supervision system based on cloud computing, which comprises: the system comprises a fitness data supervision cloud platform, a communication server, a plurality of fitness monitoring devices and an output module; and each fitness monitoring device carries out information interaction with the fitness data supervision cloud platform through the communication server. The system has the characteristics of simple structure, intellectualization, humanization, safety, reliability and the like. Through utilizing the body-building monitoring devices, can gather body-building equipment's motion state data and use this body-building equipment personnel's health data in real time, and then supervise the motion intensity and the health by body-building data supervision cloud platform to this personnel and carry out the analysis, and export this analysis result via output module, thereby can make the personnel that use this body-building equipment can in time know self motion intensity and health, and then make this personnel adjust the motion intensity of oneself according to self actual conditions, avoid unexpected the emergence.
Description
Technical Field
The invention relates to the technical field of cloud computing, in particular to a body building data supervision system based on cloud computing.
Background
With the increasing living standard and health concern of people, the public body building is moving towards the direction of specialization, scientification and refinement. The traditional fitness equipment has the defects of single function, poor interactivity and the like, and is difficult to meet the increasingly diversified fitness requirements of people in the network information age. In the society where new technologies are abundant today, there is an urgent need to meet the diversified fitness requirements of fitness users by using new technologies, such as cloud computing technology, so as to achieve the intelligence, humanization, scientificity, safety and reliability of fitness.
Disclosure of Invention
In order to solve the problems, the invention provides a body-building data supervision system based on cloud computing.
The purpose of the invention is realized by adopting the following technical scheme:
a cloud computing-based fitness data monitoring system, the system comprising: the system comprises a fitness data supervision cloud platform, a communication server, a plurality of fitness monitoring devices and an output module; each fitness monitoring device carries out information interaction with the fitness data supervision cloud platform through the communication server;
the fitness monitoring device is installed on the fitness equipment and used for acquiring motion state data of the fitness equipment and body condition data of personnel using the fitness equipment when the fitness equipment is used, and forwarding the acquired data to the fitness data supervision cloud platform through the communication server;
the fitness data supervision cloud platform is used for analyzing the current exercise intensity and the body state of the person according to the received data;
the output module is in communication connection with the body-building data supervision cloud platform and used for receiving and outputting the analysis result of the body-building data supervision cloud platform.
In an optional embodiment, the fitness data administration cloud platform comprises: a user information database; and the user information database is used for storing the face feature data of the user.
In an alternative embodiment, the fitness monitoring device comprises: the system comprises a login verification module and a data acquisition module;
the login verification module is used for verifying the identity of a person using the fitness equipment and transferring the data acquisition module to acquire data after the verification is passed;
the data acquisition module is used for acquiring the motion state data of the fitness equipment, acquiring the physical condition data of the personnel using the fitness equipment, and forwarding the acquired data to the fitness data supervision cloud platform through the communication server.
In an alternative embodiment, the login verification module comprises: the system comprises a face image acquisition unit, a face image processing unit, a feature extraction unit and an identity verification unit;
the face image acquisition unit is used for acquiring a face image of a person using the fitness equipment;
the face image processing unit is used for processing the face image;
the characteristic extraction unit is used for extracting the face characteristic data representing the identity of the person from the processed face image;
the identity verification unit is connected with the user information database and used for verifying the identity of the person according to the extracted face feature data and the face feature data stored in the user information database, and the data acquisition module is called to acquire data after the identity verification is passed.
In an alternative embodiment, the face image processing unit includes: an image smoothing subunit and an image segmentation subunit;
the image smoothing subunit is used for smoothing the face image to obtain a clean sub-image of the face image;
and the image segmentation subunit is used for segmenting the clean subimage to obtain a foreground subimage only containing the face data of the person using the fitness equipment.
In an optional implementation manner, the smoothing processing is performed on the face image to obtain a clean sub-image of the face image, specifically:
(1) carrying out color mode conversion on the face image, and converting the face image into a Lab color mode;
(2) carrying out noise detection on pixel points in the face image in a Lab color mode to obtain a noise point set NP and a non-noise point set NNP;
(3) converting the face image from a Lab color mode to an RGB color mode, and calculating the estimation value of each channel value of the noise point in the face image in the RGB color mode by using the following formula;
in the formula, R ' (p), G ' (p) and B ' (p) are estimated values of an R channel value, a G channel value and a B channel value of a pixel point p respectively; r (p), G (p), B (p) are R channel value, G channel value, B channel value of the pixel point p respectively; int [. C]Is a value function, which represents rounding down, Ω is a sliding window of size N × N centered on pixel p, Rmax(Ω)、Rmin(Ω) are the maximum and minimum values of the R channel values within the sliding window Ω, respectively; gmax(Ω)、Gmin(Ω) are the maximum and minimum values of the G channel values within the sliding window Ω, respectively; b ismax(Ω)、Bmin(Ω) are the maximum and minimum values of the B channel values within the sliding window Ω, respectively;
removing pixel points p in a sliding window omega, and averaging R channel values of all noise points;
removing pixel points p in a sliding window omega, and averaging R channel values of all non-noise points; chi shape1Is a weight coefficient with a value range of [0,1 ]]；
(4) And (3) traversing all the noise points of the face image in the RGB color mode, processing all the channel values of all the noise points according to the step (3) to obtain processed noise points, wherein a set formed by the processed noise points and the non-noise points is a clean sub-image.
The invention has the beneficial effects that: the invention aims to provide a body-building data supervision system based on cloud computing, which has the characteristics of simple structure, intellectualization, humanization, safety, reliability and the like. Through utilizing the body-building monitoring devices, can gather body-building equipment's motion state data and use this body-building equipment personnel's health data in real time, and then supervise the motion intensity and the health by body-building data supervision cloud platform to this personnel and carry out the analysis, and export this analysis result via output module, thereby can make the personnel that use this body-building equipment can in time know self motion intensity and health, and then make this personnel adjust the motion intensity of oneself according to self actual conditions, avoid unexpected the emergence.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a block diagram of a frame of a fitness data monitoring system according to an embodiment of the present invention;
FIG. 2 is a block diagram of the frame of the fitness monitoring device 1 according to the present invention;
fig. 3 is a frame structure diagram of the face image processing unit 122 according to the embodiment of the present invention.
Reference numerals: the body-building monitoring device comprises a body-building monitoring device 1, a communication server 2, a body-building data supervision cloud platform 3, an output module 4, a login verification module 11, a data acquisition module 12, a face image acquisition unit 111, a face image processing unit 112, a feature extraction unit 113, an identity verification unit 114, an image smoothing subunit 1121 and an image segmentation subunit 1122.
Detailed Description
The invention is further described with reference to the following examples.
Fig. 1 illustrates a cloud computing-based fitness data supervision system comprising: the system comprises a plurality of fitness monitoring devices 1, a communication server 2, a fitness data supervision cloud platform 3 and an output module 4; wherein, each body-building monitoring device 1 carries out information interaction with body-building data supervision cloud platform 3 through communication server 2.
The fitness monitoring device 1 is installed on the fitness equipment and used for acquiring motion state data of the fitness equipment and body condition data of personnel using the fitness equipment when the fitness equipment is used, and forwarding the acquired motion state data and the body condition data to the fitness data supervision cloud platform 3 through the communication server 2; the fitness data supervision cloud platform 3 is used for receiving the data acquired by the fitness monitoring equipment 1 and analyzing the current exercise intensity and the physical state of the person according to the received data; and the output module 3 is in communication connection with the body-building data supervision cloud platform 3 and is used for receiving the analysis result of the body-building data supervision cloud platform 3 and outputting the analysis result.
The embodiment of the invention provides a body-building data supervision system based on cloud computing, and the system has the characteristics of simple structure, intellectualization, humanization, safety, reliability and the like. Through utilizing body-building monitoring devices 1, can gather body-building equipment's motion state data and use this body-building equipment personnel's health data in real time, and then supervise cloud platform 3 by body-building data and carry out the analysis to this personnel's exercise intensity and health, and export this analysis result via output module 4, thereby can make the personnel that use this body-building equipment can in time know self exercise intensity and health, and then make this personnel adjust self exercise intensity according to self actual conditions, avoid unexpected the emergence.
In an optional embodiment, the body-building data supervision cloud platform 3 is in communication connection with a user terminal, and a person using the body-building device can learn an analysis result of the person from the body-building data supervision cloud platform 3 through the user terminal, so that the person can remotely check the exercise intensity and the body state of the person, the person is convenient and quick to use, and the person can also acquire historical exercise data of the person through the user terminal to know historical exercise information of the person.
In an alternative embodiment, the fitness data curation cloud platform 3 comprises: a user information database; the user information database is used for storing the face feature data of the user.
In an alternative embodiment, referring to fig. 2, the fitness monitoring device 1 comprises: a login verification module 11 and a data acquisition module 12.
The login verification module 11 is used for verifying the identity of a person using the fitness equipment and transferring the data acquisition module 12 to acquire data after the verification is passed;
the data acquisition module 12 is used for acquiring the motion state data of the fitness equipment, acquiring the physical condition data of the personnel using the fitness equipment, and forwarding the acquired data to the fitness data supervision cloud platform 3 through the communication server 2.
The identity of the person using the corresponding fitness equipment is verified by the login verification module 11, after the verification is passed, the data acquisition module 12 can be moved to acquire data, the data acquisition module 12 only acquires data when needed, the service life of the data acquisition module 12 is prolonged, then data acquisition is performed after the verification is passed, the acquired data can be stored below the corresponding person, the corresponding person can conveniently check the own exercise data and body state, and the one-to-one correspondence between the person and the exercise data is realized.
In an alternative embodiment, the login authentication module 11 comprises: a face image acquisition unit 111, a face image processing unit 112, a feature extraction unit 113 and an identity verification unit 114.
The face image acquisition unit 111 is used for acquiring a face image of a person using the fitness equipment;
a face image processing unit 112, configured to process the face image;
a feature extraction unit 113, configured to extract, from the processed face image, face feature data representing the identity of the person;
and the identity verification unit 114 is connected with the user information database and is used for verifying the identity of the person according to the received face feature data and the face feature data stored in the user information database, and invoking the data acquisition module 12 to acquire data after the verification is passed.
In an alternative embodiment, referring to fig. 3, the face image processing unit 112 includes: an image smoothing subunit 1121 and an image segmentation subunit 1122;
an image smoothing subunit 1121, configured to perform smoothing processing on the face image to obtain a clean sub-image of the face image;
an image segmentation subunit 1122, configured to segment the clean sub-image to obtain a foreground sub-image that only includes the face data of the person using the fitness device.
In an optional embodiment, the smoothing processing is performed on the face image to obtain a clean sub-image of the face image, specifically:
(1) carrying out color mode conversion on the face image, and converting the face image into a Lab color mode;
(2) carrying out noise detection on pixel points in the face image in a Lab color mode to obtain a noise point set NP and a non-noise point set NNP;
(3) converting the face image from a Lab color mode to an RGB color mode, and calculating the estimation value of each channel value of the noise point in the face image in the RGB color mode by using the following formula;
in the formula, R ' (p), G ' (p) and B ' (p) are estimated values of an R channel value, a G channel value and a B channel value of a pixel point p respectively; r (p), G (p), B (p) are R channel value, G channel value, B channel value of the pixel point p respectively; int [. C]Is a value function, which represents rounding down, Ω is a sliding window of size N × N centered on pixel p, Rmax(Ω)、Rmin(Ω) are the maximum and minimum values of the R channel values within the sliding window Ω, respectively; gmax(Ω)、Gmin(Ω) are the maximum and minimum values of the G channel values within the sliding window Ω, respectively; b ismax(Ω)、Bmin(Ω) are the maximum and minimum values of the B channel values within the sliding window Ω, respectively;
removing pixel points p in a sliding window omega, and averaging R channel values of all noise points;
removing pixel points p in a sliding window omega, and averaging R channel values of all non-noise points; chi shape1Is a weight coefficient with a value range of [0,1 ]]Wherein, when the pixel point p is removed in the sliding window omega and other pixel points are all non-noise points, x10; when the pixel point p is removed in the sliding window omega and other pixel points are noise points, the x is determined11 is ═ 1; excluding the above two cases, preferably, χ1＝0.25。
(4) And (3) traversing all the noise points of the face image in the RGB color mode, processing all the channel values of all the noise points according to the step (3) to obtain processed noise points, wherein a set formed by the processed noise points and the non-noise points is a clean sub-image.
Has the advantages that: in the embodiment, the channel values of the noise points are estimated by using the formula to obtain the estimated values of the noise points in the channel values, so that the noise removal work of the noise points is realized.
In an optional embodiment, the noise detection is performed on the pixel points in the face image in the Lab color mode to obtain a noise point set NP and a non-noise point set NNP, specifically: judging whether pixel points in the face image are noise points according to the following judgment conditions:
in the formula, Tth、NthRespectively are preset threshold values; thetaqCentered on a pixel point q,A sliding window size of 3 × 3;
is a sliding window thetaqThe number of adjacent pixel points of the pixel point q; l (q), A (q), B (q) are the value of the L component, the value of the A component and the value of the B component of the pixel point q respectively, Ld(q)、Ad(q)、Bd(q) is the value of the L component, the value of the a component, and the value of the B component, respectively, for the d-th pixel, where d is 1,2, …,
σL、σA、σBthe variance value of the L component, the variance value of the A component and the variance value of the B component of the face image respectively, NUM (integer) represents the number of elements meeting the condition in brackets, α1、α2、α3Is a weight coefficient, which satisfies α1+α2+α3＝1。
When the pixel point q simultaneously meets the two judgment conditions, the pixel point is a noise point, and the pixel point q is added into a noise point set NP; otherwise, the pixel point q is put into the non-noise point set NNP;
and traversing all pixel points in the face image in the Lab color mode to obtain a noise point set NP and a non-noise point set NNP.
Has the advantages that: due to the influence of a plurality of external environments such as illumination, the acquired face image contains noise, and the existence of the noise not only affects the image quality of the acquired face image, but also affects the subsequent verification of the identity of the person, so that the noise in the face image needs to be removed to improve the image quality. In the embodiment, the noise detection is performed on each pixel point in the face image to obtain the noise point set and the non-noise point set, so that only the noise point needs to be processed in subsequent noise removal, and the denoising efficiency is improved. When the noise detection is carried out on the pixel point, the relation between the value of each component of the pixel point and the value of each component of the adjacent pixel point is considered, whether the pixel point is the noise point or not is further determined, the noise detection is carried out by using the method, convenience and rapidness are achieved, the pixel point can be rapidly detected, and the efficiency and the precision of the noise detection are improved.
In an optional embodiment, the segmenting the clean sub-image to obtain a foreground sub-image only containing face data of a person using the fitness equipment specifically includes:
(1) setting a detection window with the size of S multiplied by T, aligning a central pixel point of the detection window with a pixel point pt to be detected, and calculating a detection value of the pixel point pt by using the following formula;
in the formula, D _ V (pt) is the detection value of the pixel point pt, x and y are respectively the abscissa and the ordinate of the pixel point ps, wherein the pixel point ps is positioned in the detection window,
is the gaussian weighted euclidean distance of the pixel pt and the pixel ps,
g (pt) is the gray scale value of the pixel point pt,
the gray value of all pixel points in the detection window is the average value;
(2) comparing the detection value of the calculated detection value of the pixel point pt with a preset detection threshold, if the detection value of the pixel point pt is larger than the preset detection threshold, the pixel point pt is a characteristic pixel point, otherwise, the pixel point pt is a background pixel point;
and traversing all pixel points of the clean subimage to obtain all characteristic pixel points, and splicing the obtained characteristic pixel points to obtain the foreground subimage only containing the face data of the personnel using the fitness equipment.
Has the advantages that: in the above embodiment, the detection values of the pixel points in the clean subimage are calculated, and the obtained detection values are compared with the preset detection threshold value, so that all the characteristic pixel points of the clean subimage are obtained, and all the obtained characteristic pixel points are spliced, so that the segmentation operation of the clean subimage is realized.
In an alternative embodiment, the data acquisition module 12 is composed of a plurality of sensor nodes and sink nodes, and the plurality of sensor nodes and sink nodes form a wireless sensor network. The sensor nodes are used for collecting data and transmitting the collected data to the sink nodes, and the sink nodes receive the data collected by the sensor nodes, compress the data and then send the data to the communication server 2.
The sensor node includes: one or more of a heart rate sensor, a blood pressure sensor, a pulse sensor, a height and weight sensor, a pressure sensor, an angle sensor, a speed sensor, a distance measuring sensor and a tension sensor.
It should be noted that what kind of sensor is specifically set can be set according to the type of the exercise device and the needs of the exerciser.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (1)
1. A cloud computing-based fitness data monitoring system, comprising: the system comprises a fitness data supervision cloud platform, a communication server, a plurality of fitness monitoring devices and an output module; each fitness monitoring device carries out information interaction with the fitness data supervision cloud platform through the communication server;
the fitness monitoring device is installed on the fitness equipment and used for acquiring motion state data of the fitness equipment and body condition data of personnel using the fitness equipment when the fitness equipment is used, and forwarding the acquired data to the fitness data supervision cloud platform through the communication server;
the fitness data supervision cloud platform is used for analyzing the current exercise intensity and the body state of the person according to the received data;
the output module is in communication connection with the fitness data supervision cloud platform and is used for receiving and outputting the analysis result of the fitness data supervision cloud platform;
the fitness data supervision cloud platform comprises: a user information database; the user information database is used for storing the face feature data of the user;
the fitness monitoring device comprises: the system comprises a login verification module and a data acquisition module;
the login verification module is used for verifying the identity of a person using the fitness equipment and transferring the data acquisition module to acquire data after the verification is passed;
the data acquisition module is used for acquiring the motion state data of the fitness equipment, acquiring the physical condition data of personnel using the fitness equipment, and forwarding the acquired data to the fitness data supervision cloud platform through the communication server;
the login verification module comprises: the system comprises a face image acquisition unit, a face image processing unit, a feature extraction unit and an identity verification unit;
the face image acquisition unit is used for acquiring a face image of a person using the fitness equipment;
the face image processing unit is used for processing the face image;
the characteristic extraction unit is used for extracting the face characteristic data representing the identity of the person from the processed face image;
the identity verification unit is connected with the user information database and used for verifying the identity of the person according to the extracted face feature data and the face feature data stored in the user information database, and moving the data acquisition module to acquire data after the identity verification is passed;
the face image processing unit includes: an image smoothing subunit and an image segmentation subunit;
the image smoothing subunit is used for smoothing the face image to obtain a clean sub-image of the face image;
the image segmentation subunit is used for segmenting the clean subimage to obtain a foreground subimage only containing face data of the person using the fitness equipment;
the smoothing processing is performed on the face image to obtain a clean subimage of the face image, and specifically the smoothing processing is performed on the face image:
(1) carrying out color mode conversion on the face image, and converting the face image into a Lab color mode;
(2) carrying out noise detection on pixel points in the face image in a Lab color mode to obtain a noise point set NP and a non-noise point set NNP;
(3) converting the face image from a Lab color mode to an RGB color mode, and calculating the estimation value of each channel value of the noise point in the face image in the RGB color mode by using the following formula;
in the formula, R ' (p), G ' (p) and B ' (p) are estimated values of an R channel value, a G channel value and a B channel value of a pixel point p respectively; r (p), G (p), B (p) are R channel value, G channel value, B channel value of the pixel point p respectively; int [. C]Is a value function, which represents rounding down, Ω is a sliding window of size N × N centered on pixel p, Rmax(Ω)、Rmin(Ω) are the maximum and minimum values of the R channel values within the sliding window Ω, respectively; gmax(Ω)、Gmin(Ω) are the maximum and minimum values of the G channel values within the sliding window Ω, respectively; b ismax(Ω)、Bmin(Ω) are the maximum and minimum values of the B channel values within the sliding window Ω, respectively;
removing pixel points p in a sliding window omega, and averaging R channel values of all noise points;
removing pixel points p in a sliding window omega, and averaging R channel values of all non-noise points; chi shape1Is a weight coefficient with a value range of [0,1 ]]；
(4) And (3) traversing all the noise points of the face image in the RGB color mode, processing all the channel values of all the noise points according to the step (3) to obtain processed noise points, wherein a set formed by the processed noise points and the non-noise points is a clean sub-image.
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