CN111067573A - Fat thickness detection device - Google Patents
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- CN111067573A CN111067573A CN201911415836.7A CN201911415836A CN111067573A CN 111067573 A CN111067573 A CN 111067573A CN 201911415836 A CN201911415836 A CN 201911415836A CN 111067573 A CN111067573 A CN 111067573A
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- A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
- A61B8/0833—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving detecting or locating foreign bodies or organic structures
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Abstract
The invention discloses a fat thickness detection device, which comprises: determining fat thickness ranges of different detection positions based on an artificial intelligent model; the ultrasonic detection probe is used for being placed in a region to be detected; the signal receiving and transmitting circuit is used for activating the ultrasonic detection probe to send an ultrasonic signal to the region to be detected and receiving a detection signal from the region to be detected, and the detection signal is used for analyzing the current fat thickness information of the region to be detected; the signal processing module I is used for sending the detection signal in the fat thickness range in the region to be detected to a terminal device through a signal sending module, and displaying the current fat thickness information of the region to be detected through the terminal device. The technical scheme has the beneficial effects that the ultrasonic probe can be detected only by placing the ultrasonic probe at the position to be detected of the skin in the detection process, the operation is simple and convenient, and the overall cost of the device is low.
Description
Technical Field
The invention relates to the technical field of ultrasonic detection, in particular to a fat thickness detection device.
Background
The most direct method for measuring the fat content of the human body in the prior art is a skin fold thickness measurement method, which has higher operation requirement and larger error. Another method for automatically measuring fat thickness mainly comprises a body fat scale. The body fat scale calculates the body fat content by measuring the electrical impedance of different components of the human body. However, most of the numbers given by the body fat scale are calculated according to empirical formulas, the calculation is not accurate, the given whole fat content of the human body cannot reflect the local fat content, and in addition, the body fat scale is large in size, complicated in use procedure, more important, high in price and generally limited to large-scale mechanisms such as gymnasiums and the like.
Disclosure of Invention
In view of the above problems of the conventional detection device, a detection device which is easy to operate, easy to use and low in cost is provided.
The specific technical scheme is as follows:
a fat thickness detection apparatus, comprising: determining fat thickness ranges of different detection positions based on an artificial intelligent model;
the ultrasonic detection probe is used for being placed in a region to be detected;
the signal receiving and transmitting circuit is used for activating the ultrasonic detection probe to send an ultrasonic signal to the region to be detected and receiving a detection signal from the region to be detected, and the detection signal is used for analyzing the current fat thickness information of the region to be detected;
the signal processing module I is used for sending the detection signal in the fat thickness range in the region to be detected to a terminal device through a signal sending module, and displaying the current fat thickness information of the region to be detected through the terminal device.
Preferably, the ultrasonic detection probe is embedded in a cylinder.
Preferably, the ultrasonic detection probe is a single crystal ultrasonic probe.
Preferably, the artificial intelligence model is trained by a vector product machine learning algorithm and a constructed database.
Preferably, the signal sending module is a wireless transmission module.
Preferably, the wireless transmission module is a bluetooth module.
Preferably, the terminal device includes:
the signal processing module II is used for processing the received detection signal to form fat thickness information corresponding to the region to be detected;
and the display module is used for displaying the fat thickness information of the region to be detected.
Preferably, the ultrasonic testing device further comprises a power module for providing working voltage for the ultrasonic testing probe, the signal receiving and transmitting circuit and the signal processing module.
The beneficial effects of the above technical scheme are: in the detection process, the ultrasonic probe is only required to be arranged at the position to be detected of the skin, so that the detection can be carried out, the operation is simple and convenient, and the overall cost of the device is low.
Drawings
FIG. 1 is a schematic structural diagram of an embodiment of a fat thickness detecting apparatus according to the present invention;
fig. 2 is a schematic structural diagram of another embodiment of a fat thickness detection device according to the present invention.
The reference numbers of the above figures represent:
1. an ultrasonic detection probe; 2. a signal receiving and transmitting circuit; 3. a first signal processing module; 4. a signal transmitting module; 5. a terminal device; 51. a second signal processing module; 52. a display module 52.
Detailed Description
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
It should be noted that the embodiments described below and the technical features in the embodiments may be combined with each other without conflict.
The technical scheme of the invention provides a fat thickness detection device.
As shown in fig. 1, an embodiment of a fat thickness detecting apparatus includes: determining fat thickness ranges of different detection positions based on an artificial intelligent model;
the ultrasonic detection probe 1 is used for being placed in a region to be detected;
the signal receiving and transmitting circuit 2 is used for activating the ultrasonic detection probe 1 to send an ultrasonic signal to a region to be detected and receiving a detection signal from the region to be detected, and the detection signal is used for analyzing the current fat thickness information of the region to be detected;
the signal processing module I3 is used for sending a detection signal in the fat thickness range in the region to be detected to a terminal device 5 through the signal sending module 4, and displaying the current fat thickness information of the region to be detected through the terminal device 5.
In the above technical scheme, when performing detection, the ultrasonic detection probe 1 may be placed in a region to be detected of human skin, and a proper amount of coupling agent is coated on the region to be detected, the detection device is started, the ultrasonic probe performs ultrasonic detection (based on 1-dimensional ultrasonic signals), after a detection signal (i.e., reflected waves of a fat-muscle interface)) is obtained, the detection signal in a fat thickness range in the region to be detected is sent to a terminal device 5 through the signal processing module one 3 through the signal sending module 4, and the terminal device 5 displays the fat thickness information of the current region to be detected.
In a preferred embodiment, the ultrasonic testing probe 1 is embedded in a cylinder.
In the prior art, an ultrasonic probe-based fat thickness measuring instrument measures the thickness of a fat layer based on a two-dimensional image using the principle of ultrasonic M (moving) mode imaging. However, the device is complicated to operate and requires the user to hold the probe and slide it over the skin surface. Any tilt, incoherency operation can cause large errors in the results. In addition, the device is costly and requires professional operation
In this embodiment, the ultrasonic detection probe 1 is embedded in a cylinder, and the outer surfaces of the two are aligned in parallel, so as to increase the contact area with the skin surface in use, thereby reducing the angle inclination caused by the small surface area of the ultrasonic probe, and improving the accuracy of measurement.
In a preferred embodiment, the ultrasonic testing probe 1 is a single crystal ultrasonic probe.
In a preferred embodiment, the artificial intelligence model is trained by a vector product machine learning algorithm and a constructed database.
In the above technical solution, the artificial intelligence model is placed in the terminal device, wherein the step of constructing the artificial intelligence model is as follows:
establishing a data analysis database, wherein the database comprises basic information of a plurality of individuals;
inputting a plurality of individual basic information, wherein the basic information of each individual specifically comprises sex, age, height and weight, drinking or drinking, smoking or not, diabetes, exercise days and time per week, occupation, measurement of fat thickness of thigh outer side, upper arm outer side, waist and abdomen part, and the like;
an artificial intelligence model for distinguishing fat thickness ranges is trained based on the database by adopting a machine learning algorithm of local Weighted Linear regression LWLR (local Weighted Linear regression). Firstly, collecting the above information (marked as x) of 3000 volunteers, acquiring the ultrasonic images of the navel, waist, upper arm, thigh and other parts of each volunteer by using professional ultrasonic equipment, reading the thickness (namely label) of the fat layer of each part by a professional sonographer with a high-level job, inputting the acquired data into an LWLR model for training, and finally obtaining an artificial intelligent model for fat thickness measurement.
Through a large amount of training, a model which is best fitted with data is obtained, and an applicable linear regression model can be trained more quickly and better by utilizing algorithms (such as a least square method, a gradient descent method and the like) and tools (SPSS). The essence is to solve for the weight θ of each feature argument.
(T is the transpose in algebraic operations).
In the training process, feature selection, fitting optimization and the like need to be considered.
The final goal is to determine each weight (parameter) θ or to approximate the true weight (parameter) θ by an algorithm.
The artificial intelligence model can determine the fat thickness range of a certain region to be detected of an individual after inputting the number basic information of the individual, and after the ultrasonic detection probe 1 is placed in the region to be detected, the signal processing module I3 can process the returned detection signal, namely, the detection signal in the fat thickness range in the region to be detected is sent to a terminal device 5 through the signal sending module 4;
because the signal processing module 3 can distinguish the position of the fat thickness of the region to be detected, namely the effective signal range, the hardware circuit does not need to send all collected signals to the mobile phone, but sends the signals in the fat thickness range back, thereby greatly reducing the requirements of the signals on transmission bandwidth, reducing power consumption and final cost. On the other hand, since our database contains data for multiple sites of an individual, it can be used for fat layer thickness measurement for multiple sites throughout the body without being affected by different anatomical structures at different sites.
It should be noted that the artificial intelligence model may be placed in the terminal device, and after the terminal device inputs the number basic information of the individual, the range of the fat thickness of a certain region to be detected of the individual may be determined, and the range information of the fat thickness of each detection region is sent to the detection device, so that the signal processing unit one 3 performs the above processing operation on the detection signal.
In a preferred embodiment, the signal sending module 4 is a wireless transmission module.
In a preferred embodiment, the wireless transmission module is a bluetooth module.
In the above technical scheme, the wireless transmission module can select other modules having wireless transmission function besides the bluetooth module, such as an infrared transmission module, a WIFI module, and the like.
In a preferred embodiment, as shown in fig. 2, the terminal device 5 includes:
the second signal processing module 51 is used for processing the received detection signal to form fat thickness information corresponding to the region to be detected;
the display module 52 is used for displaying the fat thickness information of the region to be detected.
In the above technical solution, the terminal device 5 may be a device with display and data processing functions, such as a smart phone, a tablet computer, and the like.
In a preferred embodiment, the ultrasonic testing device further comprises a power supply module for supplying operating voltages to the ultrasonic testing probe 1, the signal receiving and transmitting circuit 2 and the signal processing module one 3.
In the above technical solution, the power module may select a rechargeable battery, such as a lithium battery.
In a specific embodiment, the ultrasonic detection probe 1, the signal receiving and transmitting circuit 2, the signal processing module one 3 and the signal transmitting module 4 can be integrally arranged in a shell as a body, wherein the ultrasonic detection probe 1 is connected with the module in the body by adopting a signal wire with a preset length so as to conveniently place the ultrasonic detection probe 1. It should be noted that each module may be disposed on a circuit board, wherein the circuit board may be disposed within the housing.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (8)
1. A fat thickness detection device, comprising: determining fat thickness ranges of different detection positions based on an artificial intelligent model;
the ultrasonic detection probe is used for being placed in a region to be detected;
the signal receiving and transmitting circuit is used for activating the ultrasonic detection probe to send an ultrasonic signal to the region to be detected and receiving a detection signal from the region to be detected, and the detection signal is used for analyzing the current fat thickness information of the region to be detected;
the signal processing module I is used for sending the detection signal in the fat thickness range in the region to be detected to a terminal device through a signal sending module, and displaying the current fat thickness information of the region to be detected through the terminal device.
2. The fat thickness detecting device according to claim 1, wherein the ultrasonic detection probe is embedded in a cylinder.
3. The fat thickness detecting apparatus according to claim 1, wherein the ultrasonic detection probe is a single crystal ultrasonic probe.
4. The fat thickness detection device of claim 1, wherein the artificial intelligence model is trained by a machine learning algorithm of a vector product and a constructed database.
5. The fat thickness detection device according to claim 1, wherein the signal transmission module is a wireless transmission module.
6. The fat thickness detection device according to claim 5, wherein the wireless transmission module is a Bluetooth module.
7. The fat thickness detection apparatus according to claim 1, wherein the terminal device includes:
the signal processing module II is used for processing the received detection signal to form fat thickness information corresponding to the region to be detected;
and the display module is used for displaying the fat thickness information of the region to be detected.
8. The fat thickness detecting device according to claim 1, further comprising a power supply module for supplying an operating voltage to the ultrasonic detection probe, the signal receiving and transmitting circuit and the signal processing module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911415836.7A CN111067573A (en) | 2019-12-31 | 2019-12-31 | Fat thickness detection device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911415836.7A CN111067573A (en) | 2019-12-31 | 2019-12-31 | Fat thickness detection device |
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| CN111067573A true CN111067573A (en) | 2020-04-28 |
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Citations (6)
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| CN2852950Y (en) * | 2006-01-04 | 2007-01-03 | 高宏伟 | Dopper ultrasonic extremity detection probe through cranium |
| CN103126726A (en) * | 2011-11-25 | 2013-06-05 | 重庆海扶医疗科技股份有限公司 | Fat thickness measuring device |
| CN106255456A (en) * | 2016-04-18 | 2016-12-21 | 深圳市汇顶科技股份有限公司 | Mobile terminal and fat data measuring method |
| CN107203701A (en) * | 2017-07-24 | 2017-09-26 | 广东工业大学 | A kind of measuring method of fat thickness, apparatus and system |
| CN207152582U (en) * | 2017-02-24 | 2018-03-30 | 广东工业大学 | Body fat measuring and analysis system |
| CN107928670A (en) * | 2017-11-20 | 2018-04-20 | 广东里田科技有限公司 | The apparatus for measuring fat and fat measurement method interacted with mobile terminal |
-
2019
- 2019-12-31 CN CN201911415836.7A patent/CN111067573A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2852950Y (en) * | 2006-01-04 | 2007-01-03 | 高宏伟 | Dopper ultrasonic extremity detection probe through cranium |
| CN103126726A (en) * | 2011-11-25 | 2013-06-05 | 重庆海扶医疗科技股份有限公司 | Fat thickness measuring device |
| CN106255456A (en) * | 2016-04-18 | 2016-12-21 | 深圳市汇顶科技股份有限公司 | Mobile terminal and fat data measuring method |
| CN207152582U (en) * | 2017-02-24 | 2018-03-30 | 广东工业大学 | Body fat measuring and analysis system |
| CN107203701A (en) * | 2017-07-24 | 2017-09-26 | 广东工业大学 | A kind of measuring method of fat thickness, apparatus and system |
| CN107928670A (en) * | 2017-11-20 | 2018-04-20 | 广东里田科技有限公司 | The apparatus for measuring fat and fat measurement method interacted with mobile terminal |
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