CN111289076A - Human body physiological experiment system capable of automatically collecting human body basic data - Google Patents
Human body physiological experiment system capable of automatically collecting human body basic data Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
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- G01G19/44—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing persons
- G01G19/50—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing persons having additional measuring devices, e.g. for height
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1072—Measuring physical dimensions, e.g. size of the entire body or parts thereof measuring distances on the body, e.g. measuring length, height or thickness
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Abstract
The invention discloses a human body physiological experiment system capable of automatically collecting human body basic data, which relates to the field of physiological experiments and comprises an experiment table body, a pressure sensor, a depth camera and a processor, wherein the processor is in communication connection with the depth camera and the pressure sensor and is used for comprehensively analyzing user image data and pressure sensing data; the memory stores a height algorithm model and a pre-trained deep learning algorithm model; the processor comprises a weight analysis module, a height analysis module, a gender analysis module and an age analysis module. The height and the weight of a user are determined by a weight and height analysis module of a processor; and identifying the gender and the age of the user through a gender and age analysis module of the processor and a deep algorithm model in the memory. In addition, the standing position of the user is determined through the standing position analysis module, the user is ensured to keep a correct standing posture through the standing posture analysis module, and the accuracy of identifying the height, the gender and the age of the user is improved.
Description
Technical Field
The invention relates to the field of physiological experiments, in particular to a human body physiological experiment system capable of automatically collecting human body basic data.
Background
In medical function experiments, human physiology experiments are introduced. When a human body physiological experiment is performed, basic physiological data of a human body, such as height, weight, sex, age and the like, are often required to be obtained, and basic physiological indexes of the human body can be roughly estimated by using the basic data.
The existing human body physiological experiment system can only collect height and weight data of an experimenter, and a manual input mode is usually adopted for collecting gender and age data of the experimenter, so that the mode is relatively troublesome and poor in experience. Meanwhile, because the height of the human body is greatly related to the standing posture, the standing posture problem of an experimenter is not considered when the existing human body physiological experiment system collects the height data of the human body, and therefore the collected height data are inaccurate.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a human body physiological experiment system capable of automatically acquiring basic data of a human body, so that the data of the height and the weight of an experimenter can be automatically acquired, and meanwhile, the data of the sex and the age of the experimenter can be automatically acquired.
The purpose of the invention is realized by the following technical scheme:
a human body physiological experiment system capable of automatically collecting human body basic data comprises an experiment table body, a standing plate arranged at the bottom of the experiment table body, and a plurality of pressure sensors arranged below the standing plate and used for collecting user weight data;
the depth camera is arranged at the top of the experiment table body and used for collecting user image data;
the processor is in communication connection with the depth camera and the pressure sensor and is used for comprehensively analyzing the user image data and the pressure sensing data;
the memory is in communication connection with the processor and stores a height algorithm model and a pre-trained deep learning algorithm model;
the processor comprises a weight analysis module, a height analysis module, a gender analysis module and an age analysis module;
the weight analysis module is used for analyzing the pressure sensing data and determining the weight of the user;
the height analysis module is based on a height algorithm model and is used for analyzing the user image data to determine the height of the user;
the gender analysis module is based on a deep learning algorithm model and is used for analyzing the image data of the user and determining the gender of the user;
the age analysis module is based on a deep learning algorithm model and is used for analyzing the image data of the user and determining the age of the user.
Furthermore, the system also comprises a plurality of infrared probes in communication connection with the processor, wherein the infrared probes are arranged on the surface layer of the standing plate and used for identifying the outline of the sole of the user and further determining the position of the central line of the sole of the user; the surface of the standing plate is also provided with a standard line for preliminarily prompting a user to stand; the processor also comprises a standing position analysis module for analyzing whether the standing position of the user is correct and determining the central position of the heel of the user, and the analysis process comprises the following steps:
(1) setting a standard distance threshold value of a plantar midline and a standard line;
(2) acquiring the outline of the sole of a user, and analyzing the position of the midline of the sole of the user;
(3) calculating the distance between the central line of the sole of the user and the standard line, and outputting a station position adjustment prompt if the distance is greater than a standard distance threshold; if the distance is less than the standard distance threshold, analyzing the position of the heel center of the user.
Furthermore, the pressure sensors comprise a first pressure sensor for detecting left sole gravity data, a second pressure sensor for detecting left heel gravity data, a third pressure sensor for detecting right sole gravity data, and a fourth pressure sensor for detecting right heel gravity data; the weight analysis module determines the weight of the user according to the pressure sensing data of each pressure sensor; the processor also comprises a standing posture analysis module for analyzing whether the standing posture of the user is correct according to the gravity data collected by the pressure sensor, and the analysis flow is as follows:
(1) setting a standard difference range of the left foot gravity data and the right foot gravity data and a standard ratio range of the double sole gravity data and the double heel gravity data according to the standard standing posture;
(2) acquiring left sole gravity data, left heel gravity data, right sole gravity data and right heel gravity data through a first pressure sensor, a second pressure sensor, a third pressure sensor and a fourth pressure sensor respectively;
(3) adding the left foot sole gravity data and the left foot heel gravity data to obtain left foot gravity data, adding the right foot sole gravity data and the right foot heel gravity data to obtain right foot gravity data, adding the left foot sole gravity data and the right foot sole gravity data to obtain double foot sole gravity data, and adding the left foot heel gravity data and the right foot heel gravity data to obtain double foot heel gravity data;
(4) judging the difference value of the left foot gravity data and the right foot gravity data, and outputting a side-tipping and standing posture adjusting prompt if the difference value exceeds the standard difference value range; if the difference value does not exceed the standard difference value range, the ratio judgment is carried out on the gravity data of the soles and the gravity data of the heels, and if the ratio exceeds the standard ratio range, the pitching standing posture adjustment prompt is continuously output; and if the ratio does not exceed the standard ratio range, judging that the standing posture is standard, and controlling the depth camera to finish the acquisition of user image data by the processor.
Further, the processor further comprises an image enhancement module for removing noise in the user image data; and the image identification module is used for extracting the head highest point data and the face data in the enhanced user image data.
Further, the height algorithm model is as follows:
wherein H is the height of the user, H is the height of the depth camera, c is the length of a connecting line between the depth camera and the highest point of the head of the user, and d is the horizontal distance between the depth camera and the central position of the heel of the user.
Further, the pre-trained deep learning algorithm model process is as follows:
(1) inputting a plurality of contrast images with gender characteristics and age characteristics into a deep learning algorithm model for linear transformation;
(2) carrying out nonlinear transformation on the contrast image by using a deep learning algorithm model;
(3) and (4) using the deep learning algorithm model to perform downsampling on the contrast image after the nonlinear change to obtain a pre-trained deep algorithm model.
Further, the gender analysis module and the age analysis module are used for performing deep learning algorithm analysis on the user image data to determine the gender and the age of the human body, and the process is as follows:
(1) acquiring face data in user image data, and inputting the face data into a pre-trained deep learning algorithm model;
(2) and outputting the face data corresponding to the gender and the age to a result through a pre-trained deep learning algorithm model.
As described above, the human physiology experiment system capable of automatically collecting human basic data of the invention has the following beneficial effects:
1) according to the invention, the age and gender analysis module arranged in the processor is used for analyzing the human body image data acquired by the depth camera, so that the purpose of determining the gender and age of the experimenter is achieved. The depth camera enhances and identifies the collected human body image data through a digital image processing technology, so that the human body facial image data with higher definition and higher quality are obtained, and the fact that the human body facial image data can output more accurate gender and age data after entering a depth algorithm model is guaranteed.
2) In the conventional user height data acquisition process, the standing position and the standing posture of the user can influence the user, the standing position of the user is determined by the standing position analysis module and the user is prompted to stand at the experimental standing position, so that the influence of the standing position on the height data acquisition is avoided; the standing posture analysis module is used for determining the standing posture of the user and prompting the user to keep the standard standing posture, the phenomena of body side inclination and forward leaning and backward leaning (stooping and humpback) do not occur, the deep camera is favorable for collecting the image data of the user, the accuracy of collecting the height data of the user is improved, and meanwhile the reliability of the recognition result of the gender and the age is improved to a certain extent by the standard standing posture.
Drawings
FIG. 1 is a system diagram of a human physiology experiment system for automatically collecting human basic data according to the present invention;
FIG. 2 is a schematic structural diagram of an embodiment of the present invention;
FIG. 3 is a schematic diagram of height measurement according to an embodiment of the present invention;
in the figure, 1-depth camera, 2-display, 3-standard line, 4-standing plate.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1-3, the present invention provides a technical solution:
the utility model provides an automatic human physiology experiment system of human basic data of collection, including the laboratory bench body, set up in the degree of depth camera 1 at laboratory bench body top, set up in the board 4 of standing of laboratory bench body bottom, stand 4 surfaces of board and be provided with standard line 3, stand 4 top layers of board and evenly gather and have a plurality of infrared probe, stand 4 below of board and be provided with the first pressure sensor who is used for detecting left sole gravity data respectively, a second pressure sensor for detecting left heel gravity data, a third pressure sensor for detecting right sole gravity data, a fourth pressure sensor for detecting right heel gravity data.
The device also comprises a processor which is in communication connection with the depth camera 1, the first pressure sensor, the second pressure sensor, the third pressure sensor, the fourth pressure sensor and the infrared probe; the memory is in communication connection with the processor and stores a height algorithm model and a pre-trained deep learning algorithm model; a display 2 and a speaker communicatively coupled to the processor.
The processor comprises a weight analysis module, a height analysis module, a gender analysis module and an age analysis module.
The standard line 3 is used for preliminarily prompting a user to stand; the infrared probe is used for identifying the outline of the sole of the user so as to determine the position of the midline of the sole of the user.
The processor also comprises a standing position analysis module for analyzing whether the standing position of the user is correct and determining the central position of the heel of the user, and the analysis process comprises the following steps:
(1) setting a standard distance threshold value of the plantar midline and the standard line 3;
(2) acquiring a user plantar contour, and analyzing the position of the plantar midline of the user according to the position of the plantar midline of the standard plantar contour;
(3) calculating the relative distance between the plantar midline of the user and the standard line 3, and if the distance is greater than the standard distance threshold and the plantar midline of the user is in front of the standard line 3, outputting a prompt of 'position error, please retreat' to a loudspeaker; if the distance is larger than the standard distance threshold value and the midline of the sole of the user is behind the standard line 3, outputting a prompt of 'position error, please forward' to a loudspeaker; if the distance is smaller than the standard distance threshold value, the central position of the heel of the user is analyzed and stored in the memory, and meanwhile, the user enters a standing posture analysis module to begin to analyze whether the standing posture of the user is correct.
The standing posture analysis module is used for analyzing whether the standing posture of the user is correct according to the gravity data collected by the pressure sensor, and the analysis flow is as follows:
(1) setting a standard difference range according to the difference range of the left foot gravity data and the right foot gravity data under the standard standing posture condition; setting a standard ratio range according to the ratio of the gravity data of the sole of the double feet and the gravity data of the heel of the double feet under the standard standing posture condition;
(2) respectively acquiring left sole gravity data, left heel gravity data, right sole gravity data and right heel gravity data of a user, which are uploaded by a first pressure sensor, a second pressure sensor, a third pressure sensor and a fourth pressure sensor;
(3) adding the left foot sole gravity data and the left foot heel gravity data of a user to obtain left foot gravity data, adding the right foot sole gravity data and the right foot heel gravity data of the user to obtain right foot gravity data, adding the left foot sole gravity data and the right foot sole gravity data of the user to obtain double foot sole gravity data, and adding the left foot heel gravity data and the right foot heel gravity data of the user to obtain double foot heel gravity data;
(4) the left foot gravity data and the right foot gravity data of the user are subjected to difference value judgment, and if the difference value exceeds the standard difference value range and the value of the left foot gravity data is larger than that of the right foot gravity data, the 'standing posture error and body leaning to the left' are output to a loudspeaker; if the difference value exceeds the standard difference value range and the value of the left foot gravity data is smaller than the value of the right foot gravity data, outputting ' standing posture error and body don't skew to the right ' to a loudspeaker; if the difference value does not exceed the standard difference value range, the ratio judgment is carried out on the gravity data of the soles and the heels, and if the ratio exceeds the standard ratio range: if the ratio of the sole gravity data value to the heel gravity data value is larger than the maximum value of the standard ratio range, the standing posture error and the forward leaning do not need to be reported; if the ratio of the sole gravity data value to the heel gravity data value is smaller than the minimum value of the standard ratio range, the station posture error is reported, and no backward leaning is required. If the ratio does not exceed the standard ratio range, the user stands upright, the processor controls the depth camera to finish the collection of user image data, the collected user image data is an image of the user in the standard standing upright, the influence of the standing position close to the front/back, the standing position side-tipping and the standing position pitching on the height calculation result is reduced, and the reliability of the gender and age identification result is improved to a certain extent by the standard standing upright.
The weight analysis module is used for determining the weight of the user according to the pressure sensing data of each pressure sensor, and the analysis process is as follows: and adding the left sole gravity data, the left heel gravity data, the right sole gravity data and the right heel gravity data to obtain the user weight and outputting the user weight to the display 2.
The processor also comprises an image enhancement module and an image identification module. The image enhancement module carries out light compensation, gray level transformation, histogram equalization, normalization, geometric correction, filtering and sharpening on the collected user image data, so that the definition of the user image is improved; the image recognition module performs calculation analysis on the position, the face shape and the head shape of the five sense organs of the face of the enhanced user image data based on a face recognition algorithm, extracts head peak data and face data, sends the head peak data to a height analysis module of the processor, and sends the face data to a gender analysis module and an age analysis module of the processor.
The height algorithm model stored in the memory is:
wherein H is the height of the user, H is the lens mounting height of the depth camera 1, c is the connecting line length between the lens of the depth camera 1 and the highest point of the head of the user, and d is the horizontal distance between the lens of the depth camera 1 and the central position of the heel of the user.
The height analysis module is based on a height algorithm model:
for analyzing the user image data, determining the height of the user and outputting to the display 2.
The pre-trained deep learning algorithm model stored in the memory comprises the following steps:
(1) inputting a plurality of contrast images with gender characteristics and age characteristics into a deep learning algorithm model for linear transformation;
(2) carrying out nonlinear transformation on the contrast image by using a deep learning algorithm model;
(3) and (4) using the deep learning algorithm model to perform downsampling on the contrast image after the nonlinear change to obtain a pre-trained deep algorithm model.
The gender analysis module, age analysis module be used for carrying out deep learning algorithm analysis to user's image data, confirm human sex, human age, its flow is:
(1) acquiring face data in user image data, and inputting the face data into a pre-trained deep learning algorithm model;
(2) the face data is output to the display 2 according to gender and age through a pre-trained deep learning algorithm model.
Claims (7)
1. The utility model provides an automatic human physiology experiment system of human basic data of collection, includes the laboratory bench body, sets up in the board of standing of laboratory bench body bottom, sets up in a plurality of pressure sensor that stand board below is used for gathering user's weight data, its characterized in that:
the system also comprises a depth camera used for collecting user image data;
the processor is in communication connection with the depth camera and the pressure sensor and is used for comprehensively analyzing the user image data and the pressure sensing data;
the memory is in communication connection with the processor and stores a height algorithm model and a pre-trained deep learning algorithm model;
the processor comprises a weight analysis module, a height analysis module, a gender analysis module and an age analysis module;
the weight analysis module is used for analyzing the pressure sensing data and determining the weight of the user;
the height analysis module is based on a height algorithm model and is used for analyzing the user image data to determine the height of the user;
the gender analysis module is based on a deep learning algorithm model and is used for analyzing the image data of the user and determining the gender of the user;
the age analysis module is based on a deep learning algorithm model and is used for analyzing the image data of the user and determining the age of the user.
2. The human body physiological experiment system for automatically acquiring the basic data of the human body according to claim 1, characterized in that: the system also comprises a plurality of infrared probes in communication connection with the processor, wherein the infrared probes are arranged on the surface layer of the standing plate and used for identifying the outline of the sole of the user and further determining the position of the central line of the sole of the user; the surface of the standing plate is also provided with a standard line for preliminarily prompting a user to stand; the processor also comprises a standing position analysis module for analyzing whether the standing position of the user is correct and determining the central position of the heel of the user, and the analysis process comprises the following steps:
(1) setting a standard distance threshold value of a plantar midline and a standard line;
(2) acquiring the outline of the sole of a user, and analyzing the position of the midline of the sole of the user;
(3) calculating the distance between the central line of the sole of the user and the standard line, and outputting a station position adjustment prompt if the distance is greater than a standard distance threshold; if the distance is less than the standard distance threshold, analyzing the position of the heel center of the user.
3. The human physiology experiment system for automatically collecting human basic data according to claim 1 or 2, characterized in that: the pressure sensors comprise a first pressure sensor for detecting left sole gravity data, a second pressure sensor for detecting left heel gravity data, a third pressure sensor for detecting right sole gravity data and a fourth pressure sensor for detecting right heel gravity data; the weight analysis module determines the weight of the user according to the pressure sensing data of each pressure sensor;
the processor also comprises a standing posture analysis module for analyzing whether the standing posture of the user is correct according to the gravity data collected by the pressure sensor, and the analysis flow is as follows:
(1) setting a standard difference range of the left foot gravity data and the right foot gravity data and a standard ratio range of the double sole gravity data and the double heel gravity data according to the standard standing posture;
(2) acquiring left sole gravity data, left heel gravity data, right sole gravity data and right heel gravity data through a first pressure sensor, a second pressure sensor, a third pressure sensor and a fourth pressure sensor respectively;
(3) adding the left foot sole gravity data and the left foot heel gravity data to obtain left foot gravity data, adding the right foot sole gravity data and the right foot heel gravity data to obtain right foot gravity data, adding the left foot sole gravity data and the right foot sole gravity data to obtain double foot sole gravity data, and adding the left foot heel gravity data and the right foot heel gravity data to obtain double foot heel gravity data;
(4) judging the difference value of the left foot gravity data and the right foot gravity data, and outputting a side-tipping and standing posture adjusting prompt if the difference value exceeds the standard difference value range; if the difference value does not exceed the standard difference value range, the ratio judgment is carried out on the gravity data of the soles and the gravity data of the heels, and if the ratio exceeds the standard ratio range, the pitching standing posture adjustment prompt is continuously output; and if the ratio does not exceed the standard ratio range, judging that the standing posture is standard, and controlling the depth camera to finish the acquisition of user image data by the processor.
4. The human body physiological experiment system for automatically acquiring the human body basic data according to claim 2, characterized in that: the processor further comprises an image enhancement module for removing noise in the user image data; and the image identification module is used for extracting the head highest point data and the face data in the enhanced user image data.
5. The human physiology experiment system for automatically collecting human basic data according to claim 4, characterized in that: the height algorithm model is as follows:
wherein H is the height of the user, H is the height of the depth camera, c is the length of a connecting line between the depth camera and the highest point of the head of the user, and d is the horizontal distance between the depth camera and the central position of the heel of the user.
6. The human physiology experiment system for automatically collecting human basic data according to claim 4, characterized in that: the pre-trained deep learning algorithm model process comprises the following steps:
(1) inputting a plurality of contrast images with gender characteristics and age characteristics into a deep learning algorithm model for linear transformation;
(2) carrying out nonlinear transformation on the contrast image by using a deep learning algorithm model;
(3) and (4) using the deep learning algorithm model to perform downsampling on the contrast image after the nonlinear change to obtain a pre-trained deep algorithm model.
7. The human body physiological experiment system for automatically acquiring the human body basic data according to claim 6, wherein: the gender analysis module, age analysis module be used for carrying out deep learning algorithm analysis to user's image data, confirm human sex, human age, its flow is:
(1) acquiring face data in user image data, and inputting the face data into a pre-trained deep learning algorithm model;
(2) and outputting the face data corresponding to the gender and the age to a result through a pre-trained deep learning algorithm model.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110705421A (en) * | 2019-09-25 | 2020-01-17 | 浙江鸿泉电子科技有限公司 | Body type data processing method and device |
| CN112906512A (en) * | 2021-02-03 | 2021-06-04 | 北京海迩西医疗科技有限公司 | Method, device and storage medium for determining human body joint |
| CN113288120A (en) * | 2021-05-27 | 2021-08-24 | 江苏盖睿健康科技有限公司 | Height measuring device and height measuring method capable of eliminating tiptoe standing error |
| CN113288087A (en) * | 2021-06-25 | 2021-08-24 | 成都泰盟软件有限公司 | Virtual-real linkage experimental system based on physiological signals |
| WO2022042200A1 (en) * | 2020-08-27 | 2022-03-03 | 华为技术有限公司 | Human body composition detection method and system, and electronic device and storage medium |
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Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102175356A (en) * | 2011-02-22 | 2011-09-07 | 中国科学院合肥物质科学研究院 | Device and method for measuring gait touch information |
| CN105286871A (en) * | 2015-11-27 | 2016-02-03 | 西安交通大学 | Video processing-based body height measurement method |
| CN106108907A (en) * | 2016-06-17 | 2016-11-16 | 合肥工业大学 | A kind of sole pressure distribution detector |
| CN106236562A (en) * | 2016-09-21 | 2016-12-21 | 云南中医学院 | The biological health information management of intelligence foot and health care decision system Treatment decsion and the method for feedback improvements |
| US20170109571A1 (en) * | 2010-06-07 | 2017-04-20 | Affectiva, Inc. | Image analysis using sub-sectional component evaluation to augment classifier usage |
| US20170319148A1 (en) * | 2016-05-04 | 2017-11-09 | Mimitec Limited | Smart mirror and platform |
| CN107392681A (en) * | 2017-09-12 | 2017-11-24 | 中山大学 | A kind of height and weight measuring system and method based on Internet of Things |
| CN107704851A (en) * | 2017-10-30 | 2018-02-16 | 歌尔股份有限公司 | Character recognition method, Public Media exhibiting device, server and system |
| CN107928633A (en) * | 2017-12-22 | 2018-04-20 | 西安蒜泥电子科技有限责任公司 | A kind of light-type three-dimensional and body component tracker and body composition test method |
| CN108113120A (en) * | 2017-12-20 | 2018-06-05 | 中国科学院合肥物质科学研究院 | Three-dimensional foot type and plantar pressure integral measuring instrument |
| CN108143421A (en) * | 2017-12-21 | 2018-06-12 | 西安蒜泥电子科技有限责任公司 | A kind of human body three-dimensional scans human body component detection device |
| CN108451533A (en) * | 2017-02-17 | 2018-08-28 | 红崴科技股份有限公司 | Automate foot detection device |
| CN109893095A (en) * | 2019-03-11 | 2019-06-18 | 常州市贝叶斯智能科技有限公司 | A kind of intelligent robot system of body composition detection and analysis |
| CN209285514U (en) * | 2018-12-12 | 2019-08-23 | 南京林业大学 | A kind of stance stability measurement device |
| CN110211668A (en) * | 2019-05-31 | 2019-09-06 | 邵阳学院 | A kind of psychoanalysis method and device based on drawing psychology |
| CN110245575A (en) * | 2019-05-21 | 2019-09-17 | 东华大学 | A kind of human somatotype parameter catching method based on body contour line |
| CN110662484A (en) * | 2018-04-20 | 2020-01-07 | 智能身型 | System and method for whole body measurement extraction |
| CN110974235A (en) * | 2019-12-31 | 2020-04-10 | 南京莱曼信息科技有限责任公司 | Outdoor physical examination pavilion of intelligence |
| CN111079589A (en) * | 2019-12-04 | 2020-04-28 | 常州工业职业技术学院 | Automatic height detection method based on depth camera shooting and height threshold value pixel calibration |
| CN111067530A (en) * | 2019-12-04 | 2020-04-28 | 常州工业职业技术学院 | Subway passenger height automatic detection method and system based on depth camera shooting |
| CN111127446A (en) * | 2019-12-26 | 2020-05-08 | 中国科学院合肥物质科学研究院 | Gait analysis-oriented plantar pressure image partitioning method |
| CN111144327A (en) * | 2019-12-28 | 2020-05-12 | 神思电子技术股份有限公司 | Method for improving recognition efficiency of face recognition camera of self-service equipment |
-
2020
- 2020-05-13 CN CN202010399754.4A patent/CN111289076B/en active Active
Patent Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170109571A1 (en) * | 2010-06-07 | 2017-04-20 | Affectiva, Inc. | Image analysis using sub-sectional component evaluation to augment classifier usage |
| CN102175356A (en) * | 2011-02-22 | 2011-09-07 | 中国科学院合肥物质科学研究院 | Device and method for measuring gait touch information |
| CN105286871A (en) * | 2015-11-27 | 2016-02-03 | 西安交通大学 | Video processing-based body height measurement method |
| US20170319148A1 (en) * | 2016-05-04 | 2017-11-09 | Mimitec Limited | Smart mirror and platform |
| CN106108907A (en) * | 2016-06-17 | 2016-11-16 | 合肥工业大学 | A kind of sole pressure distribution detector |
| CN106236562A (en) * | 2016-09-21 | 2016-12-21 | 云南中医学院 | The biological health information management of intelligence foot and health care decision system Treatment decsion and the method for feedback improvements |
| CN108451533A (en) * | 2017-02-17 | 2018-08-28 | 红崴科技股份有限公司 | Automate foot detection device |
| CN107392681A (en) * | 2017-09-12 | 2017-11-24 | 中山大学 | A kind of height and weight measuring system and method based on Internet of Things |
| CN107704851A (en) * | 2017-10-30 | 2018-02-16 | 歌尔股份有限公司 | Character recognition method, Public Media exhibiting device, server and system |
| CN108113120A (en) * | 2017-12-20 | 2018-06-05 | 中国科学院合肥物质科学研究院 | Three-dimensional foot type and plantar pressure integral measuring instrument |
| CN108143421A (en) * | 2017-12-21 | 2018-06-12 | 西安蒜泥电子科技有限责任公司 | A kind of human body three-dimensional scans human body component detection device |
| CN107928633A (en) * | 2017-12-22 | 2018-04-20 | 西安蒜泥电子科技有限责任公司 | A kind of light-type three-dimensional and body component tracker and body composition test method |
| CN110662484A (en) * | 2018-04-20 | 2020-01-07 | 智能身型 | System and method for whole body measurement extraction |
| CN209285514U (en) * | 2018-12-12 | 2019-08-23 | 南京林业大学 | A kind of stance stability measurement device |
| CN109893095A (en) * | 2019-03-11 | 2019-06-18 | 常州市贝叶斯智能科技有限公司 | A kind of intelligent robot system of body composition detection and analysis |
| CN110245575A (en) * | 2019-05-21 | 2019-09-17 | 东华大学 | A kind of human somatotype parameter catching method based on body contour line |
| CN110211668A (en) * | 2019-05-31 | 2019-09-06 | 邵阳学院 | A kind of psychoanalysis method and device based on drawing psychology |
| CN111079589A (en) * | 2019-12-04 | 2020-04-28 | 常州工业职业技术学院 | Automatic height detection method based on depth camera shooting and height threshold value pixel calibration |
| CN111067530A (en) * | 2019-12-04 | 2020-04-28 | 常州工业职业技术学院 | Subway passenger height automatic detection method and system based on depth camera shooting |
| CN111127446A (en) * | 2019-12-26 | 2020-05-08 | 中国科学院合肥物质科学研究院 | Gait analysis-oriented plantar pressure image partitioning method |
| CN111144327A (en) * | 2019-12-28 | 2020-05-12 | 神思电子技术股份有限公司 | Method for improving recognition efficiency of face recognition camera of self-service equipment |
| CN110974235A (en) * | 2019-12-31 | 2020-04-10 | 南京莱曼信息科技有限责任公司 | Outdoor physical examination pavilion of intelligence |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110705421A (en) * | 2019-09-25 | 2020-01-17 | 浙江鸿泉电子科技有限公司 | Body type data processing method and device |
| WO2022042200A1 (en) * | 2020-08-27 | 2022-03-03 | 华为技术有限公司 | Human body composition detection method and system, and electronic device and storage medium |
| CN112906512A (en) * | 2021-02-03 | 2021-06-04 | 北京海迩西医疗科技有限公司 | Method, device and storage medium for determining human body joint |
| CN112906512B (en) * | 2021-02-03 | 2024-06-11 | 北京海迩西医疗科技有限公司 | Method, device and storage medium for determining joints of human body |
| CN113288120A (en) * | 2021-05-27 | 2021-08-24 | 江苏盖睿健康科技有限公司 | Height measuring device and height measuring method capable of eliminating tiptoe standing error |
| CN113288120B (en) * | 2021-05-27 | 2022-11-01 | 江苏盖睿健康科技有限公司 | Height measuring device and height measuring method capable of eliminating tiptoe standing error |
| CN113288087A (en) * | 2021-06-25 | 2021-08-24 | 成都泰盟软件有限公司 | Virtual-real linkage experimental system based on physiological signals |
| CN114360148A (en) * | 2021-12-06 | 2022-04-15 | 深圳市亚略特科技股份有限公司 | Automatic selling method and device, electronic equipment and storage medium |
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Denomination of invention: Human physiological experiment system with automatic collection of basic human data Effective date of registration: 20221208 Granted publication date: 20200821 Pledgee: Bank of Chengdu science and technology branch of Limited by Share Ltd. Pledgor: CHENGDU TAIMENG SOFTWARE Co.,Ltd. Registration number: Y2022980025270 |