CN112076073A - Automatic massage area dividing method and device, massage robot and storage medium - Google Patents
Automatic massage area dividing method and device, massage robot and storage medium Download PDFInfo
- Publication number
- CN112076073A CN112076073A CN202010734260.7A CN202010734260A CN112076073A CN 112076073 A CN112076073 A CN 112076073A CN 202010734260 A CN202010734260 A CN 202010734260A CN 112076073 A CN112076073 A CN 112076073A
- Authority
- CN
- China
- Prior art keywords
- massage
- human body
- point cloud
- area
- dimensional information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H7/00—Devices for suction-kneading massage; Devices for massaging the skin by rubbing or brushing not otherwise provided for
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/12—Driving means
- A61H2201/1207—Driving means with electric or magnetic drive
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/16—Physical interface with patient
- A61H2201/1657—Movement of interface, i.e. force application means
- A61H2201/1659—Free spatial automatic movement of interface within a working area, e.g. Robot
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/50—Control means thereof
- A61H2201/5007—Control means thereof computer controlled
Landscapes
- Health & Medical Sciences (AREA)
- Dermatology (AREA)
- Epidemiology (AREA)
- Pain & Pain Management (AREA)
- Physical Education & Sports Medicine (AREA)
- Rehabilitation Therapy (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Massaging Devices (AREA)
Abstract
The invention discloses an automatic segmentation method of a massage area, which comprises the following steps: acquiring two-dimensional information and three-dimensional information of a human body massage area, wherein the two-dimensional information is an RGB image, and the three-dimensional information is point cloud data with depth information; performing key point identification on the RGB image based on an OpenPose algorithm to generate 2D key points of each part of the human body; and automatically acquiring the region to be massaged of the human body according to the two-dimensional information and the three-dimensional information, and segmenting the region to be massaged by combining with the 2D key points of each part of the human body. The automatic segmentation method of the massage area provided by the invention can identify the 2D key points of each part of the human body, automatically segment the areas such as the shoulder, the back, the waist and the like according to the 2D key points, feed back the segmentation information of the areas to the massage robot after segmenting each part of the human body, and automatically match the corresponding massage strategies according to different massage areas by the massage robot so as to achieve better massage effect.
Description
Technical Field
The invention relates to the field of region segmentation, in particular to a massage region automatic segmentation method and device, a massage robot and a storage medium.
Background
The massage robot can automatically massage the human body, gradually replaces the traditional manual massage, can relax and eliminate fatigue of the human body, and can perform physical therapy on all parts of the human body.
After the massage robot sets the massage parameters, such as the massage force of the massage head, the massage temperature of the massage head, the massage mode of the massage head, and the like, the human body is massaged only according to the set parameters. That is, when the massage head is used for massaging different parts of the human body, such as the shoulder, the back, the waist and the like, the massage strategies adopted by the massage head are the same, namely, the massage strength, the massage temperature, the massage mode and the like of the massage head are not changed.
When the traditional manual massage mode is adopted for massage, a masseur can apply different massage force with different sizes according to different parts of a human body, and different massage modes can be adopted aiming at different parts of the human body at the same time so as to achieve better massage effect. However, the existing massage robot cannot identify different parts of the human body, and then selects a corresponding massage strategy according to the different parts of the human body, which has the defect of poor massage effect.
Disclosure of Invention
The invention mainly aims to provide an automatic segmentation method for a massage area, and aims to solve the technical problem that the existing massage robot is poor in massage effect.
In order to achieve the above object, the present invention provides an automatic segmentation method for a massage area, including: acquiring two-dimensional information and three-dimensional information of a human body massage area, wherein the two-dimensional information is an RGB image, and the three-dimensional information is point cloud data with depth information; performing key point identification on the RGB image based on an OpenPose algorithm to generate 2D key points of each part of the human body; and automatically acquiring the region to be massaged of the human body according to the two-dimensional information and the three-dimensional information, and segmenting the region to be massaged by combining with the 2D key points of each part of the human body.
Preferably, the performing, based on the openpos algorithm, the keypoint identification on the RGB image to generate 2D keypoints for each part of the human body includes: performing feature extraction on the RGB map through a VGG19 convolutional network to generate a group of feature maps; respectively extracting the confidence coefficient and the relevance degree of the feature map by using a CNN network; and analyzing the confidence coefficient and the relevance through a greedy algorithm to generate 2D key points of all parts of the human body.
Preferably, the automatically acquiring the region to be massaged of the human body according to the two-dimensional information and the three-dimensional information includes: identifying point clouds belonging to a plane area in the three-dimensional point cloud; judging whether the plane area is a bed plane area or not according to the point cloud area of the plane area; and if so, deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
Preferably, the automatically acquiring the region to be massaged of the human body according to the two-dimensional information and the three-dimensional information includes: identifying point clouds belonging to a plane area in the three-dimensional point cloud; judging whether the plane area is a bed plane area or not according to the position of the plane area; and if so, deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
The present invention also provides an automatic massage area dividing device, including: the information acquisition module is used for acquiring two-dimensional information and three-dimensional information of a human body massage area, wherein the two-dimensional information is an RGB image, and the three-dimensional information is point cloud data with depth information; the 2D key point generation module is used for carrying out key point identification on the RGB image based on an OpenPose algorithm so as to generate 2D key points of all parts of the human body; and the region segmentation module is used for automatically acquiring the region to be massaged of the human body according to the two-dimensional information and the three-dimensional information and segmenting the region to be massaged by combining the 2D key points of each part of the human body.
Preferably, the 2D keypoint generation module comprises: the feature map generation unit is used for performing feature extraction on the RGB map through a VGG19 convolutional network to generate a group of feature maps; the confidence degree association degree extracting unit is used for extracting the confidence degree and the association degree of the feature map through a CNN network; and the 2D key point generating unit is used for analyzing the confidence coefficient and the relevance through a greedy algorithm to generate 2D key points of all parts of the human body.
Preferably, the region segmentation module includes: the identification unit is used for identifying point clouds belonging to a plane area in the three-dimensional point cloud; the judging unit is used for judging whether the plane area is a bed plane area or not according to the point cloud area of the plane area; and the point cloud eliminating unit is used for deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
Preferably, the region segmentation module includes: the identification unit is used for identifying point clouds belonging to a plane area in the three-dimensional point cloud; the judging unit is used for judging whether the plane area is a bed plane area or not according to the position of the plane area; and the point cloud eliminating unit is used for deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
The present invention also proposes a massage robot including:
a memory for storing a computer program;
and a processor for implementing the automatic segmentation method for the massage area described in the foregoing embodiments when executing the computer program.
The present invention also provides a storage medium storing a computer program that, when executed by a processor, implements the automatic massage region segmentation method described in each of the aforementioned embodiments.
Compared with the prior art, the embodiment of the invention has the beneficial technical effects that:
the massage area automatic segmentation method provided by the embodiment of the invention can identify the 2D key points of each part of the human body, automatically segment the areas such as the shoulder, the back, the waist and the like according to the 2D key points, feed back the segmentation information of the areas to the massage robot after segmenting each part of the human body, and automatically match the corresponding massage strategies according to different massage areas by the massage robot so as to achieve better massage effect.
Drawings
FIG. 1 is a flowchart illustrating a first embodiment of a method for automatically segmenting a massage area according to the present invention;
FIG. 2 is a 2D key point generation diagram of the automatic segmentation method for massage areas according to the present invention;
FIG. 3 is a diagram of the human body region to be massaged generated by the automatic segmentation method for the massage region according to the present invention;
FIG. 4 is a diagram of the segmentation of the region to be massaged of the human body according to the automatic segmentation method for the massage region of the present invention;
FIG. 5 is a flowchart illustrating a second embodiment of the method for automatically segmenting a massage area according to the present invention;
FIG. 6 is a flowchart illustrating a method for automatically segmenting a massage area according to a third embodiment of the present invention;
FIG. 7 is a flowchart illustrating a fourth embodiment of the method for automatically segmenting a massage area according to the present invention;
FIG. 8 is a functional block diagram of an embodiment of the automatic segmentation apparatus for massage area according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be illustrative of the present invention and should not be construed as limiting the present invention, and all other embodiments that can be obtained by one skilled in the art based on the embodiments of the present invention without inventive efforts shall fall within the scope of protection of the present invention.
The invention provides an automatic segmentation method of a massage area, which comprises the following steps in one embodiment, referring to fig. 1:
step S10, acquiring two-dimensional information and three-dimensional information of the human body massage area, wherein the two-dimensional information is an RGB image, and the three-dimensional information is point cloud data with depth information;
in practical application, a user lies prone on a massage bed firstly, the position of the massage bed is adjusted according to practical conditions until the requirements of acquisition and massage of three-dimensional point cloud data are met, and then two-dimensional information and three-dimensional information of a human body massage area are acquired through the 3D structured light module. The 3D structured light module mainly comprises a camera and a projector, structured light is active structure information such as laser stripes, Gray codes and sine stripes projected to the surface of a measured object through the projector, then the measured surface is shot through a single or a plurality of cameras to obtain structured light images, and finally three-dimensional analysis calculation is carried out through the images based on the triangulation principle, so that three-dimensional reconstruction is realized.
Step S20, performing key point identification on the RGB image based on the OpenPose algorithm to generate 2D key points of each part of the human body;
in this embodiment, the openpos algorithm is used to identify key points of the human body in the RGB image, so as to generate 2D key points of each part of the human body, such as shoulder key points, back key points, and waist key points, as shown in fig. 2.
And step S30, automatically acquiring the region to be massaged of the human body according to the two-dimensional information and the three-dimensional information, and dividing the region to be massaged by combining the 2D key points of each part of the human body.
In this embodiment, the point cloud of the bed plane area needs to be deleted, and the bed plane and the human body surface have significant distinguishing characteristics, that is, the bed plane is a plane area with a large area in the point cloud acquisition space, and the human body surface is a curved area with a large area in the point cloud acquisition space. In practical application, because the area of the bed plane exposed is affected by the position covered by the human body, the area representing the bed plane in the point cloud can be changed within a certain range, but the accurate detection of the bed plane in the embodiment cannot be affected.
In this embodiment, the point cloud corresponding to the bed plane area may be deleted from the three-dimensional point cloud according to a preset condition to obtain the area to be massaged of the human body, as shown in fig. 3. Specifically, the preset condition mainly includes two points, namely, the area of the plane region, and whether the plane region is located at the lower part of the whole point cloud, and the plane region is determined by using the preset condition from the whole point-surface region. In a preferred embodiment, a correlation algorithm in PCL (point Cloud library) may be used to identify the point Cloud belonging to the plane area (for example, feature vectors of each point are used as correlation parameters), and calculate the area of the plane area.
After the 2D key points of the region to be massaged of the human body and each part of the human body are obtained, the region to be massaged is divided in proportion to obtain the massage region information of different parts such as the shoulder, the back, the waist and the like, as shown in fig. 4. After the information of the massage areas of different parts is obtained, the information is fed back to the massage robot, and the massage robot can automatically display the massage areas of all the parts on a display interface for a user to check and operate. It should be noted that, when the user lies flat on the massage couch, the region of the human body to be massaged may be divided into a chest region, an abdomen region, a waist region, and the like.
In an embodiment, referring to fig. 5, the step of performing keypoint recognition on the RGB image based on the openpos algorithm to generate 2D keypoints for each part of the human body includes:
step S21, performing feature extraction on the RGB map through a VGG19 convolutional network to generate a group of feature maps;
step S22, extracting the confidence and the relevance of the feature map by using the CNN network respectively;
and step S23, analyzing the confidence coefficient and the relevance through a greedy algorithm to generate 2D key points of each part of the human body.
In another embodiment, referring to fig. 6, the step of automatically acquiring the region of the human body to be massaged based on the two-dimensional information and the three-dimensional information includes:
step S31, identifying point clouds belonging to a plane area in the three-dimensional point clouds;
step S32, judging whether the plane area is a bed plane area or not according to the point cloud area of the plane area;
and step S33, if yes, deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
In this embodiment, a correlation algorithm in pcl (point Cloud library) may be adopted to identify a point Cloud belonging to a planar area (for example, using a feature vector of each point as a correlation parameter), and calculate an area of the planar area. In practical application, because the exposed area of the bed plane is affected by the human body covering position, the area of the bed plane in the point cloud can be changed within a certain range, and whether the identified plane area is the bed plane area can be judged according to the change.
In still another embodiment, referring to fig. 7, the step of automatically acquiring the region of the human body to be massaged based on the two-dimensional information and the three-dimensional information includes:
step S34, identifying point clouds belonging to a plane area in the three-dimensional point clouds;
step S35, judging whether the plane area is a bed plane area or not according to the position of the plane area;
and step S36, if yes, deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
In this embodiment, a correlation algorithm in pcl (point Cloud library) may be adopted to identify a point Cloud belonging to a planar area (for example, a feature vector of each point is used as an association parameter). In practical application, because the bed plane is located below the human body, whether the identified plane area is the bed plane area can be determined accordingly.
Based on the aforementioned proposed automatic segmentation method for the massage area, referring to fig. 8, the present invention further proposes an automatic segmentation apparatus for the massage area, comprising:
the information acquisition module 10 is used for acquiring two-dimensional information and three-dimensional information of a human body massage area, wherein the two-dimensional information is an RGB image, and the three-dimensional information is point cloud data with depth information;
the 2D key point generating module 20 performs key point identification on the RGB image based on the openpos algorithm to generate 2D key points of each part of the human body;
and the region segmentation module 30 is configured to automatically obtain a region to be massaged of the human body according to the two-dimensional information and the three-dimensional information, and segment the region to be massaged by combining 2D key points of each part of the human body.
In an embodiment, the 2D keypoint generation module 20 provided by the present invention includes:
the characteristic map generating unit is used for extracting the characteristics of the RGB map through a VGG19 convolutional network to generate a group of characteristic maps;
the confidence degree association degree extraction unit is used for extracting the confidence degree and the association degree of the feature map through the CNN network;
and the 2D key point generating unit is used for analyzing the confidence coefficient and the relevance through a greedy algorithm to generate the 2D key points of all parts of the human body.
In another embodiment, the region segmentation module 30 provided in the present invention includes:
the identification unit is used for identifying point clouds belonging to a plane area in the three-dimensional point cloud;
the judging unit is used for judging whether the plane area is a bed plane area or not according to the point cloud area of the plane area;
and the point cloud eliminating unit is used for deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
In another embodiment, the region segmentation module 30 provided in the present invention includes:
the identification unit is used for identifying point clouds belonging to a plane area in the three-dimensional point cloud;
the judging unit is used for judging whether the plane area is a bed plane area or not according to the position of the plane area;
and the point cloud eliminating unit is used for deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
Based on the aforementioned proposed automatic segmentation method for massage areas, the present invention further proposes a massage robot, comprising:
a memory for storing a computer program;
a processor, configured to implement the automatic segmentation method for a massage area according to the foregoing embodiments when executing a computer program, the automatic segmentation method for a massage area at least includes the following steps:
step S10, acquiring two-dimensional information and three-dimensional information of the human body massage area, wherein the two-dimensional information is an RGB image, and the three-dimensional information is point cloud data with depth information;
step S20, performing key point identification on the RGB image based on the OpenPose algorithm to generate 2D key points of each part of the human body;
and step S30, automatically acquiring the region to be massaged of the human body according to the two-dimensional information and the three-dimensional information, and dividing the region to be massaged by combining the 2D key points of each part of the human body.
Based on the aforementioned proposed automatic segmentation method for massage area, the present invention further proposes a storage medium storing a computer program, which when executed by a processor, implements the automatic segmentation method for massage area described in the aforementioned embodiments, the automatic segmentation method for massage area at least comprising the following steps:
step S10, acquiring two-dimensional information and three-dimensional information of the human body massage area, wherein the two-dimensional information is an RGB image, and the three-dimensional information is point cloud data with depth information;
step S20, performing key point identification on the RGB image based on the OpenPose algorithm to generate 2D key points of each part of the human body;
and step S30, automatically acquiring the region to be massaged of the human body according to the two-dimensional information and the three-dimensional information, and dividing the region to be massaged by combining the 2D key points of each part of the human body.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another apparatus, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a part of or preferred embodiments of the present invention, and neither the text nor the drawings should be construed as limiting the scope of the present invention, and all equivalent structural changes, which are made by using the contents of the present specification and the drawings, or any other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for automatically segmenting a massage area is characterized by comprising the following steps:
acquiring two-dimensional information and three-dimensional information of a human body massage area, wherein the two-dimensional information is an RGB image, and the three-dimensional information is point cloud data with depth information;
performing key point identification on the RGB image based on an OpenPose algorithm to generate 2D key points of each part of the human body;
and automatically acquiring the region to be massaged of the human body according to the two-dimensional information and the three-dimensional information, and segmenting the region to be massaged by combining with the 2D key points of each part of the human body.
2. The method of claim 1, wherein the performing keypoint recognition on the RGB image based on the openpos algorithm to generate 2D keypoints for each part of the human body comprises:
performing feature extraction on the RGB map through a VGG19 convolutional network to generate a group of feature maps;
respectively extracting the confidence coefficient and the relevance degree of the feature map by using a CNN network;
and analyzing the confidence coefficient and the relevance through a greedy algorithm to generate 2D key points of all parts of the human body.
3. The automatic segmentation method for massage areas according to claim 1, wherein the automatically obtaining the areas to be massaged of the human body according to the two-dimensional information and the three-dimensional information comprises:
identifying point clouds belonging to a plane area in the three-dimensional point cloud;
judging whether the plane area is a bed plane area or not according to the point cloud area of the plane area;
and if so, deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
4. The automatic segmentation method for massage areas according to claim 1, wherein the automatically obtaining the areas to be massaged of the human body according to the two-dimensional information and the three-dimensional information comprises:
identifying point clouds belonging to a plane area in the three-dimensional point cloud;
judging whether the plane area is a bed plane area or not according to the position of the plane area;
and if so, deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
5. An automatic segmentation device for a massage area, comprising:
the information acquisition module is used for acquiring two-dimensional information and three-dimensional information of a human body massage area, wherein the two-dimensional information is an RGB image, and the three-dimensional information is point cloud data with depth information;
the 2D key point generation module is used for carrying out key point identification on the RGB image based on an OpenPose algorithm so as to generate 2D key points of all parts of the human body;
and the region segmentation module is used for automatically acquiring the region to be massaged of the human body according to the two-dimensional information and the three-dimensional information and segmenting the region to be massaged by combining the 2D key points of each part of the human body.
6. The automatic segmentation device for massage areas as set forth in claim 5, wherein the 2D keypoint generation module comprises:
the feature map generation unit is used for performing feature extraction on the RGB map through a VGG19 convolutional network to generate a group of feature maps;
the confidence degree association degree extracting unit is used for extracting the confidence degree and the association degree of the feature map through a CNN network;
and the 2D key point generating unit is used for analyzing the confidence coefficient and the relevance through a greedy algorithm to generate 2D key points of all parts of the human body.
7. The automatic segmentation device for massage areas as set forth in claim 5, wherein the area segmentation module comprises:
the identification unit is used for identifying point clouds belonging to a plane area in the three-dimensional point cloud;
the judging unit is used for judging whether the plane area is a bed plane area or not according to the point cloud area of the plane area;
and the point cloud eliminating unit is used for deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
8. The automatic segmentation device for massage areas as set forth in claim 5, wherein the area segmentation module comprises:
the identification unit is used for identifying point clouds belonging to a plane area in the three-dimensional point cloud;
the judging unit is used for judging whether the plane area is a bed plane area or not according to the position of the plane area;
and the point cloud eliminating unit is used for deleting the point cloud corresponding to the bed plane area from the three-dimensional point cloud.
9. A massage robot, comprising:
a memory for storing a computer program;
a processor for implementing the method of automatic segmentation of a massage area according to any one of claims 1 to 4 when executing said computer program.
10. A storage medium storing a computer program which, when executed by a processor, implements the automatic segmentation method for a massage area according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010734260.7A CN112076073A (en) | 2020-07-27 | 2020-07-27 | Automatic massage area dividing method and device, massage robot and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010734260.7A CN112076073A (en) | 2020-07-27 | 2020-07-27 | Automatic massage area dividing method and device, massage robot and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112076073A true CN112076073A (en) | 2020-12-15 |
Family
ID=73735627
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010734260.7A Pending CN112076073A (en) | 2020-07-27 | 2020-07-27 | Automatic massage area dividing method and device, massage robot and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112076073A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112837418A (en) * | 2021-02-05 | 2021-05-25 | 深圳瀚维智能医疗科技有限公司 | Massage area dividing method, device, equipment and storage medium |
| CN113592884A (en) * | 2021-08-19 | 2021-11-02 | 遨博（北京）智能科技有限公司 | Human body mask generation method |
| CN114404256A (en) * | 2022-01-24 | 2022-04-29 | 辽宁师范大学 | Intelligent sojourn bed suitable for health maintenance of middle-aged and elderly people |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101909573A (en) * | 2007-12-28 | 2010-12-08 | 松下电工株式会社 | Massage apparatus |
| CN108830150A (en) * | 2018-05-07 | 2018-11-16 | 山东师范大学 | One kind being based on 3 D human body Attitude estimation method and device |
| CN109498384A (en) * | 2018-09-17 | 2019-03-22 | 鲁班嫡系机器人（深圳）有限公司 | A kind of identification of massage part, positioning, massage method and device, equipment |
| CN109758355A (en) * | 2019-03-07 | 2019-05-17 | 西安伊蔓蒂电子科技有限公司 | One kind is based on the processing of human acupoint two dimensional image and 3 D stereo localization method |
| CN109907950A (en) * | 2017-12-13 | 2019-06-21 | 曹可瀚 | Massager and massage method |
| CN110464633A (en) * | 2019-06-17 | 2019-11-19 | 深圳壹账通智能科技有限公司 | Acupuncture point recognition methods, device, equipment and storage medium |
| CN110766704A (en) * | 2019-10-22 | 2020-02-07 | 深圳瀚维智能医疗科技有限公司 | Breast point cloud segmentation method, device, storage medium and computer equipment |
| US20200121556A1 (en) * | 2018-10-17 | 2020-04-23 | Midea Group Co., Ltd. | System and method for generating pressure point maps based on remote-controlled haptic-interactions |
| CN111402360A (en) * | 2020-03-13 | 2020-07-10 | 北京奇艺世纪科技有限公司 | Method, apparatus, computer device and storage medium for generating a human body model |
-
2020
- 2020-07-27 CN CN202010734260.7A patent/CN112076073A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101909573A (en) * | 2007-12-28 | 2010-12-08 | 松下电工株式会社 | Massage apparatus |
| CN109907950A (en) * | 2017-12-13 | 2019-06-21 | 曹可瀚 | Massager and massage method |
| CN108830150A (en) * | 2018-05-07 | 2018-11-16 | 山东师范大学 | One kind being based on 3 D human body Attitude estimation method and device |
| CN109498384A (en) * | 2018-09-17 | 2019-03-22 | 鲁班嫡系机器人（深圳）有限公司 | A kind of identification of massage part, positioning, massage method and device, equipment |
| US20200121556A1 (en) * | 2018-10-17 | 2020-04-23 | Midea Group Co., Ltd. | System and method for generating pressure point maps based on remote-controlled haptic-interactions |
| CN109758355A (en) * | 2019-03-07 | 2019-05-17 | 西安伊蔓蒂电子科技有限公司 | One kind is based on the processing of human acupoint two dimensional image and 3 D stereo localization method |
| CN110464633A (en) * | 2019-06-17 | 2019-11-19 | 深圳壹账通智能科技有限公司 | Acupuncture point recognition methods, device, equipment and storage medium |
| CN110766704A (en) * | 2019-10-22 | 2020-02-07 | 深圳瀚维智能医疗科技有限公司 | Breast point cloud segmentation method, device, storage medium and computer equipment |
| CN111402360A (en) * | 2020-03-13 | 2020-07-10 | 北京奇艺世纪科技有限公司 | Method, apparatus, computer device and storage medium for generating a human body model |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112837418A (en) * | 2021-02-05 | 2021-05-25 | 深圳瀚维智能医疗科技有限公司 | Massage area dividing method, device, equipment and storage medium |
| CN112837418B (en) * | 2021-02-05 | 2023-09-19 | 深圳瀚维智能医疗科技有限公司 | Massage area dividing method, device, equipment and storage medium |
| CN113592884A (en) * | 2021-08-19 | 2021-11-02 | 遨博（北京）智能科技有限公司 | Human body mask generation method |
| CN113592884B (en) * | 2021-08-19 | 2022-08-09 | 遨博（北京）智能科技有限公司 | Human body mask generation method |
| CN114404256A (en) * | 2022-01-24 | 2022-04-29 | 辽宁师范大学 | Intelligent sojourn bed suitable for health maintenance of middle-aged and elderly people |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107111833B (en) | Fast 3D model adaptation and anthropometry | |
| US10607420B2 (en) | Methods of using an imaging apparatus in augmented reality, in medical imaging and nonmedical imaging | |
| CN112076073A (en) | Automatic massage area dividing method and device, massage robot and storage medium | |
| US6434278B1 (en) | Generating three-dimensional models of objects defined by two-dimensional image data | |
| CN107220933B (en) | Reference line determining method and system | |
| EP3655924B1 (en) | Method and apparatus to generate and track standardized anatomical regions automatically | |
| EP2856426B1 (en) | Body measurement | |
| CN104679831B (en) | Method and device for matching human body model | |
| WO2022095721A1 (en) | Parameter estimation model training method and apparatus, and device and storage medium | |
| KR102006019B1 (en) | Method, system and non-transitory computer-readable recording medium for providing result information about a procedure | |
| KR20210051141A (en) | Method, apparatus and computer program for providing augmented reality based medical information of patient | |
| JP2019096113A (en) | Processing device, method and program relating to keypoint data | |
| CN114155376A (en) | Target feature point extraction method and device, computer equipment and storage medium | |
| CN113570684A (en) | Image processing method, image processing device, computer equipment and storage medium | |
| KR102433473B1 (en) | Method, apparatus and computer program for providing augmented reality based medical information of patient | |
| CN113657357B (en) | Image processing method, image processing device, electronic equipment and storage medium | |
| WO2018190805A1 (en) | Depth image pose search with a bootstrapped-created database | |
| US10970655B2 (en) | Surgery planning based on predicted results | |
| KR20230074468A (en) | Methods and systems for placing transducer arrays and avoiding skin surface conditions | |
| KR20190069750A (en) | Enhancement of augmented reality using posit algorithm and 2d to 3d transform technique | |
| US11250592B2 (en) | Information processing apparatus | |
| CN113129418B (en) | Target surface reconstruction method, device, equipment and medium based on three-dimensional image | |
| KR20220052140A (en) | Method and server for generating custom 3d human body model data | |
| KR101678455B1 (en) | Device and method for providing haptic information using texture recognition space | |
| CN111222448B (en) | Image conversion method and related product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201215 |
|
| RJ01 | Rejection of invention patent application after publication |